Advanced Simulation Methods.ipynb

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    "\n",
    "<h1><center> Advanced Simulation Methods for Derivatives Pricing </center></h1>\n",
    "<h3><center> by Anthony Li, </center></h3>\n",
    "<h3><center> June 2022 </center></h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Methods Covered:\n",
    "- Introduction to Monte Carlo for estimating integrals\n",
    "- Variance Reduction Techniques\n",
    "    - Antithetic Variables\n",
    "    - Control Variables\n",
    "    - Stratified Sampling\n",
    "- Von Neumann's Acceptance-Rejection \n",
    "- Inverse Transform Method\n",
    "- Complex Derivative Pricing by Euler and Milstein Schemes"
   ],
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    "## Introduction\n",
    "\n",
    "In this project, we will be investigating the use of simulation methods for pricing derivatives under different asset price dynamics, namely Geometric Brownian Motion and Heston's Model. The purpose will be to demonstrate the use of Monte Carlo rather than fitting these models on real data. To calibrate these models to observed asset prices, we will need access to stock option data on exchanges which is costly data. It is easy to calibrate this using Maximum Likelihood Estimation (MLE) or any other preferred method, such as error minimisation.\n",
    "\n",
    "We will also investigate the use of different variance reduction techniques to improve efficiency and increase rate of convergence. This is important as Monte Carlo simulations can be computationally expensive and these variance reduction techniques can help improve accuracy with less simulations. The techniques we will investigate are antithetic variables, stratified sampling, and control variables."
   ]
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   "cell_type": "markdown",
   "source": [
    "## 1.1 What is Monte Carlo?\n",
    "Monte Carlo simulation is a computational technique used to estimate the outcomes of complex systems or processes through random sampling and statistical analysis. It is named after the famous Monte Carlo Casino in Monaco, known for its games of chance and randomness.\n",
    "\n",
    "In a Monte Carlo simulation, you break down a problem into smaller components or variables and then use random sampling to generate a large number of possible scenarios or outcomes for these variables. By repeatedly simulating these scenarios, you can analyze the distribution of possible outcomes and make probabilistic assessments of the system's behavior.\n",
    "\n",
    "Monte Carlo simulations are used in a wide range of fields, including finance, engineering, physics, economics, and more. They can be particularly useful when dealing with complex systems that are difficult to model analytically, or when you want to understand the range of possible outcomes and associated probabilities.\n",
    "\n",
    "One famous example of Monte Carlo simulation is its application in option pricing in finance, specifically the Black-Scholes model, which estimates the value of financial options based on the underlying asset's price, volatility, and time to expiration using random sampling.\n",
    "\n",
    "For Black-Scholes model, it is a very simple case for vanilla options as we have closed-form solutions to the risk-neutral expectation of the option prices. Therefore, we can simply simulate a series of Normal Distributions (i.e. the brownian motion) and calculate an average, an unbiased approximation of the expectation.\n",
    "\n",
    "In more complex cases, we will have to simulate the entire brownian motion path with many time steps rather than just the final result. This is usually the case when we do not have closed-form solutions for the risk-neutral expected asset price. For example, this could be derivatives of which value depends on the entire underlying price history rather than the most recent price. A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event."
   ],
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    "## 1.2 Demonstrating Monte Carlo on Integrals\n",
    "Before we apply it to derivative pricing, we will demonstrate its effectiveness by comparing a numerical approximation of a complex integral to a simulation based estimation of the same integral.\n",
    "\n",
    "### Numerical Approximation\n",
    "To approximate $\\bf{I_1 = \\int_{0}^{4} \\frac{1}{(2+cos(x))^2} dx}$ numerically, I have applied the trapezoidal rule **(Section 1.1.3)**. That is,\n",
    "\\begin{align}\n",
    "I_1 &= \\int_{0}^{4} \\frac{1}{(2+cos(x))^2} dx\\\\\n",
    "&= \\int_{0}^{4} g(x) dx\\\\\n",
    "&\\approx h\\left( \\frac{g(0) + g(4)}{2} + \\sum_{i=1}^{n-1} g \\left(ih \\right) \\right)\\\\\n",
    "&=T(h)\n",
    "\\end{align}\n",
    "where $\\bf{h = \\frac{4}{n}} $ and $ \\bf{g(x) =  \\frac{1}{(2+cos(x))^2}} $."
   ]
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    "However, to ensure that the approximation error $\\leq$ 0.01, I first identified the minimum number of interpolation points such that the total error is bounded by 0.01. I.e. find **n** such that...\n",
    "\n",
    "\\begin{align}\n",
    "\\textrm{Total Error} = T(h) - \\int_0^4 g(x) dx = \\frac{h^2(4-0)}{12} g''(\\xi) \\leq 0.01\n",
    "\\end{align}\n",
    "where\n",
    "\n",
    "\\begin{align}\n",
    "& g'(x) = \\frac{2sin(x)}{(2+cos(x))^3} \\\\\n",
    "& g''(x) = \\frac{2cos(x)}{(2+cos(x))^3} + \\frac{6sin^2(x)}{(2+cos(x))^4} .\n",
    "\\end{align}\n",
    "\n",
    "**Note**: we can do this because $g \\in C^2[0, 4]$, i.e. twice differentiable for the interval $[0,4]$."
   ]
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    "To find the upper bound of the Total Error, we must first maximise $g''(x)$ on $(0,4)$. Let $\\xi^* \\in (0,4)$ take the value which maximises $g''$ on $(0,4)$. This gives us\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{h^2(4-0)}{12} g''(\\xi^*) = 0.01 \\\\\n",
    "\\end{align}\n",
    "\n",
    "therefore\n",
    "\n",
    "\\begin{align}\n",
    "n = \\sqrt{\\frac{1600}{3}g''(\\xi^*)} .\n",
    "\\end{align}"
   ]
  },
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   "execution_count": 1,
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    "executionInfo": {
     "status": "ok",
     "timestamp": 1693515776611,
     "user_tz": -60,
     "elapsed": 556,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
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   "source": [
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
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   "execution_count": 2,
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    "executionInfo": {
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     "timestamp": 1693515779021,
     "user_tz": -60,
     "elapsed": 3,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  message: Optimization terminated successfully.\n",
      "  success: True\n",
      "   status: 0\n",
      "      fun: -0.5925925925925213\n",
      "        x: [ 2.094e+00]\n",
      "      nit: 4\n",
      "      jac: [-6.929e-07]\n",
      " hess_inv: [[ 2.799e-01]]\n",
      "     nfev: 12\n",
      "     njev: 6\n"
     ]
    }
   ],
   "source": [
    "def g_2(x):\n",
    "    y = 2*np.cos(x)/(2+np.cos(x))**3 + 6*np.sin(x)**2/(2+np.cos(x))**4\n",
    "    return y\n",
    "\n",
    "# Minimising -g(x) is same as maximising g(x)\n",
    "optimizer = opt.minimize(lambda x: -g_2(x),2);\n",
    "print(optimizer)"
   ]
  },
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    "executionInfo": {
     "status": "ok",
     "timestamp": 1693515787971,
     "user_tz": -60,
     "elapsed": 247,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "f16afcac-4e47-41af-f0ba-0f976013c166",
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     "start_time": "2024-07-09T09:32:44.717517Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.77777777777671\n"
     ]
    }
   ],
   "source": [
    "# Since we can see above that x falls in the bounds [0,4], we may continue using this maximum of 0.5925...\n",
    "g2_max = -optimizer['fun']\n",
    "\n",
    "# Applying the function defined above to find the number of interpolations.\n",
    "n = np.sqrt(1600/3 * g2_max)\n",
    "print(n)"
   ]
  },
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     "timestamp": 1693515790627,
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   "source": [
    "n = int(np.ceil(n)) \n",
    "h = 4/n\n",
    "\n",
    "# Defining the integrand\n",
    "def g(x):\n",
    "    y = (2+np.cos(x))**(-2)\n",
    "    return y"
   ]
  },
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   "execution_count": 5,
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    "id": "FBTGoN-URj1w",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693515795595,
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     "elapsed": 3,
     "user": {
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      "userId": "16806347570419169042"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My trapezoidal approximation of the integral is: 1.9100.\n"
     ]
    }
   ],
   "source": [
    "# Generating a sequence with increments of size h.\n",
    "seq = np.linspace(h, 4-h, n-1)\n",
    "\n",
    "# trapezoidal approximation\n",
    "I_1 = h*(0.5*(g(0)+g(4))+np.sum(g(seq)))\n",
    "\n",
    "print('My trapezoidal approximation of the integral is: {:.4f}.'.format(I_1))"
   ]
  },
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   "outputs": [
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vJdOUDh5U4Zpl2rlvvfZH+qmkbaJMf5d1wVF3bDaVt4hXSqSf9u5ep4qkjQqze/Xvq3rqhSt6KNjl0IrdObrgXwv11doUq9MCDRLDLQEAAACgEVm0I1N3f7xWGQVlcjlsemzEKbrmjJZV71yZl3forpUMrfQ5nshwZQX4q3TfdiWWFCqwwym69LQWOr1VpO7+eI1W783VXR+t1fytGXrykq4K8fc7/pMCPoIryQAAAACgEXB7vHp2zhZd9/YyZRSUqUNMsP5z+wBd27fV/wqy34ZWrl/K0EofZgb4q7BdonZ5MpW1Ybm0f79aRgZo1vh+uuucDrIZ0hdrUnTxy4sq57IDQEkGAAAAAA3egdwSXfXGUr0yb6dMU7r6jJb6z+0D1Cku5NAKXm/l0Mod+9ZpfzMnQyt9nd2uspYJ2h9maN/2VfJs2SyH16N7zu2oWeP7KT7MX8mZRRr5yiLNWrHP6rRAg8BwSwAAAABowH7Znqk7PlytnGK3Qvwd+sel3XVh99/NPfbr0MrU7L3KCXXK3aGlZON6CBxSERWpzMAAle7fosSiAvl3OkWnt47UN3cO1KRZh4Zd3v/Zei1LztZfR3ZVoJOaAL6L35wAAAAA0AB5vaZembdDN7yzTDnFbnVLCNPsOwf+ryD7dWhlxrpDQyszWkXLHR9DQYbDeAMDlN82QbvKUpW7frl08KAig5x6Z3Qf3Te8k2yG9Nnq/Rr5yiLtSC+0Oi5gGX57AgAAAEADk1fi1i0zV+rZOVvlNaWr+iTqk1v7KTEysHJoZcHqpdqxf71SohhaiWpwOFTSOkF7A9w6uHWVvNu3yeb16LYh7fX+uD8pOsSlbWmFuvjlX/TthoNWpwUsQUkGAAAAAA1I0oF8XfzyL/phc7qcDpueuayb/nFZd/n72Q8NrdywTvu2LFeyX5Hy2yfKEx5qdWQ0FoYhd1y0UuNDtHv/JpVvWCcVFalfu2aafedA9WvbTMXlHk14f7Wem7NVHq9pdWKgXlGSAQAAAEAD8Z91B3Tpa4u0J6tYCeEB+uzW/rqyT0uprEze7dsODa0s3MvQSpwUb0iwctvEa2fhXhWsXyllZCg6xKWZN52hcQPaSJJenrdDN89YqbwSt8VpgfrDb1QAAAAAsJjXa+rZOVt054drVOr2alDHaH19xwB1ax5SObRyZ8rGQ0Mr27RgaCVOmun0U3HbRCU7CpWWtELmrl1yGNIjF52iF6/sKZfDpp+2pP86T1mB1XGBekFJBgAAAAAWKiyr0Pj3VumVeTslSeMHtdW0MX0UUVHyv6GVzmKGVqL2GYbcCXE6GB2gPbvXyb1xvVRaqpG9EvTZhP5qHuav5MwijXxlsb7flGp1WqDOUZIBAAAAgEX2ZRfrslcXa25Smpx2m57/cw9NPqetjJ3bGVqJeuMJD1V261jtzNmlonUrpexsnZoQpv/cMUB920SqsKxCt8xcpdfm75RpMk8Zmi5+ywIAAACABZbszNLFL/+irWkFig5x6aObz9Bl8bb/Da2MdjG0EvXG9HepqF2idpnZyty0Qtq7V1FBTr03rq9u6NdKkvTMd1v0wGfrVV7htTgtUDccVgcAAAAAAF/zycp9mvz5BlV4TXVvEaY3LumgyKw92pezTzmhTrkTE7lyDPXPZlN5ywSlZOWoZOcaxRfkya9DJz15yalqGxWkJ79O0qyV+7Uvu0SvXXeawgOdVicGahW/dQEAAACgnpimqRe+36r7Pl2vCq+pi06N1UfnRMmevFbbixhaiYaholmEMlpGaVfGNpWsWyXl5WnMmW309ug+CnLatWRXli59dbGSM4usjgrUKn7zAgAAAEA9KKvwaNKsdfr3TzskSbedHqu/ty1USlqSUqJdKm2TyNBKNBhmgL8K2rbQLne6cjaskA4c0JBO0fpsYn8lhAdoV2aRRr26SMt2ZVkdFag1lGQAAAAAUMfyit264e3l+mJNiuyG9LfTQ3VVsxTt8S89dNfKsBCrIwKHczhU2rqF9gZ7lLJ1pbzbtqpzVKC+uK2/eiSGK7fYreveXqZPV+23OilQKyjJAAAAAKAO7csu1qWvLdKy5GwF+9n04uk29WqerYzWMXLHRTO0Eg1eRUyU0hPCtetAksrWr1GMUaGPb/mTLuweL7fH1L2frNNLP27nzpdo9PhtDAAAAAB1ZM3eHI16dZF2ZhQp1l96/owSJZ4SoNLWLWS6mPQcjYc3OEh5bRO0s2i/8tavkH92pl66qpcmDG4nSXp+7jY9+tVGebwUZWi8KMkAAAAAoA78tCVNV7+5VJmF5erkKtMz3TIU1YuhlWjE/BwqaZuoPa5SpW5ZKWPXTj1wbgc9cXFXGYb03tK9mvj+KpW6PVYnBU4IJRkAAAAA1LLPVu3XzTNWqdTtVf9o6fnYHYoM9WNoJRo/w5A7PkapMUHavW+D3BvXa3TPGL1yzWlyOmyasylN1721TLnF5VYnBWqM39AAAAAAUIte/3mn/vLJOnm8pi5IMPSXIXb5O/nTC02LJyxEOa3jtDNvtwo3rNIF8X6aeeMZCvF3aOWeHF0+dYlSckusjgnUCL+pAQAAAKAWeL2mnpq9WU9/u0WSdF0bQ7f0N6S4aIuTAXXDdDlV1LaFko08ZSSt1Bm2An16y58UF+qvHemFuuzVxdqSmm91TKDaKMkAAAAA4CS5PV7d++k6vbFglyTpri52XdFb8sTHWJwMqGM2m8pbxCsl0k/7ktepXfYefT7udHWICVZqfqn+PHWJVu3JtjolUC2UZAAAAABwEkrKPRo/c5U+X50iuyE90dNP55zikbt5rNXRgHrjiQxXZsso7czcrvA9m/TpNV11eqsIFZRW6Lq3lmvh9gyrIwLHRUkGAAAAACcor8St695epp+2pMvfYdP/9fHTaW3L5U6IszoaUO/MAH8VtkvULk+mKrav1szzWmhQhyiVuD26afpKfbfxoNURgWOiJAMAAACAE5BVWKar31iqVXtyFOqy6/W+TrVpXqpyCjL4MrtdZS0TtD/MUNaetXq9b7Au6Bqrco9XE99frU9W7rM6IXBUlGQAAAAAUEOpeaW68o2lSjqYr6hAP73Tz6lm0UUqT2wuGYbV8QDLVURFKiMhQvsztujZzhW6skecvKZ036fr9c4vyVbHA46IkgwAAAAAamBfdrH+/Ppi7UgvVHyIU9P6OxUQWqAyCjKgCm9QoPLbtdCe8jQ9mJijcb0O3cjiya+T9OIP22SapsUJgaooyQAAAACgmnakF+rPU5doX3aJWob7691+LtkD8lTakoIMOCKHQyWtE7QvsEI3xqRp0mnNJEkv/rBdf/16M0UZGhRKMgAAAACohqQD+bry9SVKzS9Vh+hAzejvUoVflkpaNZds/GkFHJVhyB0XrbTmIboo+qAe6RUiSXpnUbIe/WqjvF6KMjQM/CYHAAAAgONYszdHV72xRFlF5eoaF6x3+zpVYmSopHUCBRlQTd6QYOW2ideZzbP0ZE+XDEnvLd2rh77YQFGGBoHf5gAAAABwDCt2Z+u6t5Ypv7RCvRPDNO0Mp/K8aSpunSDZ7VbHAxoV0+mn4raJ6tmuTFO6SzZD+mjFPt336Xp5KMpgMUoyAAAAADiK5cnZGv3OchWVe9SvTYTePN1P2e6DFGTAyTAMuRPi1LNHgB4+tUJ2Q/ps9X5NmrVWFR6v1engwyjJAAAAAOAIlu7K0phpy1Vc7tGAtpGaeppTGaUpKmqdIDkcVscDGj1PRJh69YvW5J5uOQzpq7UHdNdHa+WmKINFKMkAAAAA4A8W78zU2GkrVFzu0cD2zfRab5fSivaqqFVzyY+CDKgtpr9Lvc5M0INneOQwpG82HNRt769WeQVFGeofJRkAAAAA/M6iHZm6cfoKlbg9OqtDlF7r7VJqwW4Vtm4u0+lndTyg6bHZ1Ktvgiaf5ZDTZur7pDRNmLFCZRUeq5PBx1CSAQAAAMCvftl+qCArdXs1uGOUXuntUmpusgpaxVOQAXWsZ/coPTg8WE6bqR+3ZeqWNxer1E1RhvpDSQYAAAAAkhZsy9BN765QWYVXZ3eK1iunBygtZ5fyW8XJdDmtjgf4hB4dQvTQiDD5G179vCdft05fzhVlqDeUZAAAAAB83vyt6Ro3Y6XKKrwa2jlaL58eqLTMHcprGSvT32V1PMCndGsVqL+1SZPL8Gr+zmxNnLGCOcpQLyjJAAAAAPi0BdsydMvMVSqv8OrcLjF6qU+w0jK3K69ljMwAf6vjAT6pe3CppnTKkstm6sdtWbrtvZXc9RJ1jpIMAAAAgM9avDNTN89YqfIKr4adEquXzghRWtpW5baIkjcwwOp4gE/rFuHWAxeGyWkzNXdLhu78YDVFGeoUJRkAAAAAn7Rid7Zumr6ycg6yf58RovTUrcpp0UzeoECr4wGQ1KNNoB44L0R+hqlvN6Xp7o/WqIKiDHWEkgwAAACAz1mzN0djp61QidujgR2i9Gr/CGUc3KKs5uHyBgdZHQ/A7/RsH6z7hwXLYZj6ZkOqJs1aS1GGOkFJBgAAAMCnbEzJ0w3vLFdhWYX6tW2m1wc2U+bBzcqKC5M3JNjqeACO4LROIbp/aKAchqn/rDuo+z5ZJ4/XtDoWmhhKMgAAAAA+Y/PBfF339jIVlFbo9FYRenNItHJSkpQZEyJPWIjV8QAcQ+8uYbr3LH/ZDVNfrD2g+z9dLy9FGWoRJRkAAAAAn7A9rUDXvbVMucVu9UgM17RhzVWQslkZ0UHyhIdaHQ9ANfTpHqG/DHDKbkifrd6vR7/aKNOkKEPtoCQDAAAA0OQlZxbpmreWKauoXF2bh2rG8AQV7d+ktKgAeSLDrY4HoAb69mqmu/9kkyHp/WV79dTszRRlqBWUZAAAAACatH3ZxbrmzaXKKChT57gQvXdhK5WmJCktwklBBjRS/fvE6Lbeh/77zYXJ+teP260NhCaBkgwAAABAk3Ugt0RXv7lUB/NK1S46SO9d3EYVKUlKDbOrIirS6ngATsKQM+N0c7dDd7l88YftenPBLosTobGjJAMAAADQJGUUlOm6t5Zpf06JWjcL1Aej2kspSToYLFXERFkdD0AtOG9wvG7o7JEk/X32Zr23dI/FidCYUZIBAAAAaHLySty64Z3l2pVZpITwAH1wWUf5HdisA4FeueOirY4HoLYYhi4ZGq8r2h0qyh79cqO+WLPf4lBorCjJAAAAADQpxeUVunH6Cm0+mK+oYJfeu7yjAg5s1n5XudzxMVbHA1DbbDZdMSxGF7eqkCnp3k/W67uNB61OhUaIkgwAAABAk1FW4dH4mau0ak+OQv0dmvnnzopI26r9zlK5E+Ksjgegjhh+frrhvBgNS/DI4zV1x4drNH9rutWx0MhQkgEAAABoEio8Xt314Vot3J6pQKdd0/98ihKydmifo1jlLeKtjgegjhkup24+v5kGx3nk9pgaP3OVlu7KsjoWGhFKMgAAAACNntdr6sHPN+i7Taly2m1647Iu6pC/S3tt+RRkgA+xBfpr4vkR6hflVVmFVzdNX6G1+3KtjoVGgpIMAAAAQKNmmqae/DpJn67aL7vN0EujOqtHyR7tUa7KEptLhmF1RAD1yB4SqLvPD9FpkV4VlXs0ZtpybU8rsDoWGgFKMgAAAACN2v/9sF3TF++WJD17UScN8KRonyeHggzwYY6IEN03PEinhJnKLXbr+reXa39OsdWx0MBRkgEAAABotN5auEv//nG7JOnJ8zroPL807XFnqqRVc8nGnzuAL3NGh2nyuS61CZZS80t1/dvLlVlYZnUsNGB8agAAAABolD5esVd/+2azJOm+s9vqz0FZ2lOappLWCRRkACRJgc0j9dhgm+IDpOTMIo1+Z7kKSt1Wx0IDxScHAAAAgEbnm/UHNfnzDZKk8f1bamxErnaXHFQxBRmAPwhtG6PHBkoRTmnTgXyNe3elSt0eq2OhAeLTAwAAAECjsnB7hu7+eI28pnR17wTd3bxYe4t/LcjsdqvjAWiAYjrFakp/j4Ic0rLkbN3+wRpVeLxWx0IDQ0kGAAAAoNFYvz9X42eukttj6sJTY/VY2wrtLUpRUZsEyeGwOh6AhsowlNituR7rUyGnTfphc5oe+GyDvF7T6mRoQCjJAAAAADQKuzIKNWbaChWXezSgbaSeOcWmfQV7VdiaggxANRiG2p8Wr8m93LIb0mer9+up2ZtlmhRlOISSDAAAAECDl/brnemyi8rVPSFUL/V26WD+bhW2bi75UZABqCa7Xd37xuuebocm73/rl2S9On+nxaHQUJxQSfbKK6+odevW8vf3V9++fbV8+fJjrv/iiy+qU6dOCggIUGJiou655x6VlpaeUGAAAAAAviWv2K0b3l6ulNwStWkWqDf+FKSs/N0qaBUv0+lndTwAjY3Dof7943RrpwpJ0rNztur9ZXssDoWGoMYl2ccff6xJkyZpypQpWr16tXr06KHhw4crPT39iOt/8MEHevDBBzVlyhRt3rxZb7/9tj7++GM99NBDJx0eAAAAQNNW6vZo3IwV2ppWoJgQl6YNCldR4W7lt4qT6XJaHQ9AI2U6/TRsUJSubXOoKHvky436ev0Bi1PBajUuyV544QXdfPPNGjt2rE455RRNnTpVgYGBeuedd464/uLFi3XmmWfqmmuuUevWrTVs2DBdffXVx736DAAAAIBvq/B4dfsHq7Vid45C/B2aNriZvMW7ldcyVqa/y+p4ABo5M8Bflw2J0MUtPDJN6Z6P12rRjkyrY8FCNSrJysvLtWrVKg0dOvR/T2CzaejQoVqyZMkRt+nfv79WrVpVWYrt2rVLs2fP1gUXXHDU/ZSVlSk/P7/KFwAAAADfYZqmHvpig37YnC6Xw6Y3B0crsHy3clvGyAzwtzoegCbCDA7SDWeHanCsKbfH1PiZq7QxJc/qWLBIjUqyzMxMeTwexcbGVlkeGxur1NTUI25zzTXX6Mknn9SAAQPk5+endu3aafDgwcccbvn0008rLCys8isxMbEmMQEAAAA0cv+cs1WzVu6XzZBeGhyrOO8e5SZGU5ABqHVGeIgmnu2v0yKlwrIKjZm2Qnuziq2OBQvU+d0t58+fr6eeekqvvvqqVq9erc8//1zffPON/vrXvx51m8mTJysvL6/ya9++fXUdEwAAAEAD8dbCXXrt17vNPTUgRp3t+5TTopm8gQEWJwPQVNmjI3TvEIc6hEiZhWW64Z1lyiwsszoW6lmNSrKoqCjZ7XalpaVVWZ6Wlqa4uLgjbvPoo4/q+uuv17hx49StWzeNGjVKTz31lJ5++ml5vd4jbuNyuRQaGlrlCwAAAEDT98Wa/frbN5slSff3idaZgQeU1Txc3uAgi5MBaOpcCVF6+CxD8QHS7qxi3Th9hYrKKqyOhXpUo5LM6XSqd+/e+vHHHyuXeb1e/fjjj+rXr98RtykuLpbNVnU3drtd0qF5BgAAAABAkuZtTdd9n6yXJN14aqQuijiozPhweUOCLU4GwFeEtI3VYwNMhTul9fvzNOH91SqvOPIFPmh6ajzcctKkSXrzzTf17rvvavPmzZowYYKKioo0duxYSdINN9ygyZMnV64/YsQIvfbaa/roo4+UnJysuXPn6tFHH9WIESMqyzIAAAAAvm3N3hxNfG+1KrymLukQprHNM5QdFypvKAUZgPoV0zlOj/atUIBdWrAtQw98tl5eLxf5+AJHTTe48sorlZGRoccee0ypqanq2bOnvvvuu8rJ/Pfu3VvlyrFHHnlEhmHokUceUUpKiqKjozVixAj9/e9/r72jAAAAANBo7Ugv0NjpK1Ti9mhQYrAmtclRVmywPOFMuwLAAoah1j2a68HSFD250k9frElRTIhLky/oYnUy1DHDbARjHvPz8xUWFqa8vDzmJwMAAACakLT8Ul366mKl5JaoR2ygXuherIJYf3kiw62OVqvCZs9Tcat4ubt2tjpKgxCwYYuMAwdUPPxsq6M0CLbCYoXM/VkF/U+T99cLUHxdyPwlKgtyqbzPadaF8Hi0eMEBPb/BT5L0yIVdNG5gW+vyoFJd9UR1fndLAAAAADiSglK3xkxboZTcErUNd+mfpxarMMbV5AoyAI2U3a7+A+J1U8dDk/f/7ZvN+mptisWhUJcoyQAAAADUu/IKrya8t1qbD+YrKtChF3q6VRrjp4pmEVZHA4D/8XPogsHRurTloaLs3k/WaeH2DItDoa5QkgEAAACoV6Zp6sHP1uuXHZkK9LPp/04zZcTaVBHdzOpoAHA4f5euGdZMQ+K8cntM3TpzlTbsz7M6FeoAJRkAAACAevXc91v1+ZoU2Q3pHz0NhcWbqoiNsjoWAByVERigCcND1TvSq6Jyj8ZOX649WUVWx0ItoyQDAAAAUG/eW7pHr8zbKUl6tLtDrVt65I6PsTgVAByfPSxYfxkerI6hpjILy3X928uVUVBmdSzUIkoyAAAAAPViblKaHvtqoyRpQieHTmtfIXdCnMWpAKD6XNGhmnyuv5oHSHuzizV2+nIVlVVYHQu1hJIMAAAAQJ1bvTdHd3y4Wl5TurSlXed3KacgA9AohSZE6NEhdoU7pY0p+Zr4/mq5PV6rY6EWUJIBAAAAqFPJmUUa9+5Klbq9Ghhr1+ie5XInxlsdCwBOWEz7aD18puRvk37elqGHv9gg0zStjoWTREkGAAAAoM5kFpZpzLTlyi4q1ylhNt15epkqWjWXDMPqaABwUtp2i9N9fTyySZq1cr/+9eN2qyPhJFGSAQAAAKgTxeUVumn6Cu3JKlaLQEMP9S2TrW0CBRmAJqPnGc01sZtHkvTiD9v18Yq9FifCyaAkAwAAAFDrKjxe3f7BGq3bn6dwp6HHzihXQEcKMgBNjGFoyFnxuqr9oaLsoc83at7WdItD4URRkgEAAACoVaZp6tGvNuqnLely2aRHTy9Xs1MTJBt/fgBogmw2XX5ujIYmeOQxTd32/mqt359rdSqcAD6lAAAAANSql3/aoQ+X75NN0v293GrdqzkFGYAmzfDz080XROu0Zl4Vl3t047QV2ptVbHUs1BCfVAAAAABqzScr9+n5udskSRNOcatn33jJbrc4FQDUPUeAU5NGRKp9iFeZReWVNy1B40FJBgAAAKBW/LwtQ5M/3yBJujo0Xef0jZAcDotTAUD9CQj11+SLwhTrb2pXZpHGvbtCpW6P1bFQTZRkAAAAAE7axpQ8TXxvlSq8poZGlWpMWApDLAH4pPDoID18fpCCHaZW783VnR+ukcdrWh0L1cCnFgAAAICTsi+7WGOnr1BRuUenRXo1sa9NNm5iCcCHJSSGavLZLvkZpr5PStMT/90k06Qoa+goyQAAAACcsNziQ/PuZBSUqV2wqUkXhMoe6LI6FgBYrnPnSN1zpp8MSTOW7NHrC3ZZHQnHQUkGAAAA4ISUuj0a9+5K7cwoUoy/qYfOC1BAZLDVsQCgweh7WpRu7Hno0tp/fLtFX61NsTgRjoWSDAAAAECNebym7v5orVbuyVGwQ3r0bD+FNw+3OhYANDgXDIrVJe0PDbW8d9Y6Ld6RaXEiHA0lGQAAAIAaMU1Tf/06Sd9tSpWfTZrcX2rePsrqWADQYF13XqwGNvfK7TU1fuZKbUnNtzoSjoCSDAAAAECNvLUwWdMX75Yk3X2aV517xlkbCAAaOJvNpokXx+rUSK8Kyjwa8/ZyHcgtsToW/oCSDAAAAEC1/WfdAf199mZJ0rhTPPpTv3iLEwFA4+B02nXfqGi1DDaVWlCmMe8sU16J2+pY+B1KMgAAAADVsmRnlu6dtVaSNLK1R+cNjpUMw9pQANCIBAf56aGRkYp0mdqWXqTxM1aorMJjdSz8ipIMAAAAwHFtTS3QLTNXqtxjakCsR9cMj5HhcFgdCwAanehIlx6+KEyBdlNLk3N076y18npNq2NBlGQAAAAAjiM1r1Rjpi1XQWmFTg336vaLomR3+VkdCwAardYJgbp3WLDshqn/rk/VM99utjoSREkGAAAA4BjyS90aM225DuaVqmWQqfsvCpNfkMvqWADQ6PXoEKLbBwZIkl5fmKzpi5ItTgRKMgAAAABHVF7h1a0zV2lLaoGauUw9fF6QgiKDrI4FAE3GoJ7huvY0pyTpif8m6buNBy1O5NsoyQAAAAAcxus1df+n67R4Z5YC7dLD57gUlRBqdSwAaHJGnRmh8zrYZUq668M1Wrk72+pIPouSDAAAAMBh/jlnq75ce0B2Q7p/gE2t2kdaHQkAmiTDMHTj8Cj1STBU5jE1bvpy7cwotDqWT6IkAwAAAFDFjCW7NfXnnZKk20+TuvWIsTgRADRtdpuhey6OUcdIKbfUo9FvLVV6QanVsXwOJRkAAACASnM2pWrKV5skSdd18WpQ/1iLEwGAb3D5GXrw0hjFB5nan1emG99epsKyCqtj+RRKMgAAAACSpFV7cnTnh2tkSjq/pUcjz46VDMPqWADgM8ICbXrksiiFOU1tTC3UbTNWyO3xWh3LZ1CSAQAAANCujEKNe3eFyiq86hvt0dgLY2XY7VbHAgCfExfup8mXRMplN/Xzzmw99MlamaZpdSyfQEkGAAAA+Lj0glKNfme5cord6hTm1V0XR8vu57A6FgD4rA7xLk06L0w2mfpk7UG9OGeL1ZF8AiUZAAAA4MOKyip00/QV2pdTouYBph4YES5XkNPqWADg805vF6ibzwqRJP1r/i59tHS3tYF8ACUZAAAA4KPcHq9u+2C1NqTkK8zP1CMXhSgsMtDqWACAXw3rEazLe/lLkh7+apPmbU61OFHTRkkGAAAA+CDTNPXIFxs1f2uGXDZTD58boNj4YKtjAQD+4KoBYTq7hUceU5r4/mqt35djdaQmi5IMAAAA8EH/+nG7Pl65TzZJfxnop3btw62OBAA4AsMwdEdPr05z5aukwtTYd5Zpb1aR1bGaJEoyAAAAwMfMWrFPL/6wXZI0/jRDvXtEWZwIAHAsDpv0WFSy2kQYyirxaPQbi5VdVG51rCaHkgwAAADwIfO2pGvy5+slSX/uZGrogFiLEwEAqiPQ5tXDl0QoOkhKzivXTW8uUkm5x+pYTQolGQAAAOAj1u/P1cT3V8ljSme38OrKcynIAKAxiQiy6ZFRUQp2SmtSi3Xnu0vl8ZpWx2oyKMkAAAAAH7A3q1g3TluhErdXvaK8umVErAwbfw4AQGPTItKhBy+OlJ9NmrszV49/skqmSVFWG/hUBAAAAJq4rMIyjX5nmTKLytUuxKu/jIySn5/d6lgAgBPUpblTdw0PlyFTM9ekaer3SVZHahIoyQAAAIAmrKTco5veXaHkrGLF+puafEmkAgKdVscCAJykfh38NWZgqCTpmXm79eXSXRYnavwoyQAAAIAmqsLj1R0frtbafXkK9TP18IhQRUT6Wx0LAFBLLuoVpBE9AyRJ9321WYs2pVicqHGjJAMAAACaINM09dh/NumHzely2kw9eG6gEuKDrI4FAKhlNwwMVf/2LrlNafxH67R5b5bVkRotSjIAAACgCXpl3g59sGyvDEmTBrrUqX2Y1ZEAAHXAZhi6Y1i4Tol3qNBtasy05TqQWWB1rEaJkgwAAABoYj5ZuU/Pfb9NknRzb5v69Ii0OBEAoC45HYYeGBGpxAi70kq8Gv3mEuUVllodq9GhJAMAAACakJ+3ZWjy5xskSZd1koafGWNxIgBAfQj2t+nhkZGKDDS0Pc+tW95cpLJyt9WxGhVKMgAAAKCJ2JiSpwkzV6rCa2pwoqmrhsVaHQkAUI+iQ+x6+JJIBfhJy9JK9ZfpS+T1eK2O1WhQkgEAAABNwL7sYo15Z7mK3V71jPLq1hGxshmG1bEAAPWsdbSf7rswQnZD+npXgZ6etdzqSI0GJRkAAADQyGUXlWv028uUWVSutiFe/WVUtPwcnOoDgK/q0dKliUMP3bDlzXVZmjZ7rbWBGgk+OQEAAIBGrKTco3HTV2hXVrFi/E1NHtVMgQF+VscCAFhscJcAXdsvWJL05IIUffvLFosTNXyUZAAAAEAj5fGauuujNVq9L1fBDlMPjwhTZLjL6lgAgAZi1OlBGt4tQKaku2bv1Ip1yVZHatAoyQAAAIBGyDRNPf6fjfo+KU1Om6nJ5wWrRXyg1bEAAA2IYRi66axQ9WnjUrlXuunTJO3YecDqWA0WJRkAAADQCL06f6dmLt0rQ6buHhSgzm1DrI4EAGiA7DZD95wXrg6xfsp3Sze8t1bpBzKtjtUgUZIBAAAAjcznq/fr2TlbJUk3ne6nvt3DrQ0EAGjQXH6GJo+IUFyYXQdKTI2esVKFOflWx2pwKMkAAACARmTh9gzd/+l6SdLITobO7x9lcSIAQGMQFmjTo5dEKDTA0OZcj8ZPXyp3cYnVsRoUSjIAAACgkdiYkqcJM1epwmtqUKJ07bAYqyMBABqRuHCHHr44Ui6HtCjNrftmLJZZXm51rAaDkgwAAABoBPZkFWnMO8tUWO5R9yivJo6Ikc0wrI4FAGhk2sf66S/nh8tmSF/uLtVTHy2RPB6rYzUIlGQAAABAA5dRUKYb3lqmzCK32oZ6de+oGPk5OJUHAJyY3m38NeGcUEnSm0mFevOL5ZLXa3Eq6/HJCgAAADRghWUVGjttmfbklCguwNRDo6IUFOCwOhYAoJE7+5RAXdsvWJL095XZ+uL71ZJpWpzKWpRkAAAAQANVXuHVrTNWauOBAoU7TT1ySYQiwpxWxwIANBGjTg/SBT0CJUn3/ZyqBQs3WJzIWpRkAAAAQAPk9Zr6y6y1+mVnlvztph66IETxMf5WxwIANCGGYWjsoBCd2cFfFaahW+fs1fpVW62OZRlKMgAAAKCBMU1Tf/0mSf9df1B2w9QDQwPVrmWw1bEAAE2QzTB0x7lh6pboVLHH0Oivdig5KdnqWJagJAMAAAAamKk/79K0RbslSXee6VL3TmHWBgIANGl+DkP3XxCuNtEO5ZRL132SpPTdKVbHqneUZAAAAEAD8snKfXrmuy2SpBtPs2vAaZEWJwIA+IJAl02PXBKh2FC7Ukqk6z9Yp4LUDKtj1StKMgAAAKCB+GlLmh78bL0kaVQn6cIB0RYnAgD4kvBAux4dGaGwAJu25psa994qlWXnWh2r3lCSAQAAAA3Aqj05mvjeanlM6exEU9cMi7U6EgDAB8WHO/TIJRHy9zO0LNOjO99fLk9BodWx6gUlGQAAAGCxHekFumn6cpVWeHV6jFfjR8TKZhhWxwIA+Ki2MX66/8JwOWzSnBS3HvtomcySEqtj1TlKMgAAAMBCB/NKdMPby5RbUqFOYV7dMypWDgen6QAAa/Vo6dIdww7dOOb9naX69+fLpfJyi1PVLT59AQAAAIvkFbs1+u3lOpBXphZBXj14aZT8XXarYwEAIEka0DFAYweFSJL+b0OhPvhmhVRRYXGqukNJBgAAAFig1O3RuHdXaFt6oZq5TD1ySYRCQ5xWxwIAoIqLegZpVO8gSdIjy3I054dVksdjcaq6QUkGAAAA1LMKj1d3fLhGK/bkKMhh6pGLQhUdFWB1LAAAjuja/sEa0iVAXhm6Y0GGli9aL5mm1bFqHSUZAAAAUI9M09TkzzdoblKanDZTk88NVMuEIKtjAQBwVIZh6NazQ9W7tUvlXkM3fp+ipOWbmlxRRkkGAAAA1BPTNPX0t1v0yar9skmaNNClLh3CrI4FAMBxOeyG/nJ+uDrH+6mwwtB13+zWng3brY5VqyjJAAAAgHry2s879caCXZKk2/ra1adHpMWJAACoPpefoYcujlCrKIeyyw1d/cV2pW3dbXWsWkNJBgAAANSDD5bt1T+/2ypJGttNGtw32uJEAADUXJDLpkcviVBcmF0HSqRrPtmk3D0pVseqFZRkAAAAQB2bveGgHv5igyTpzx29umhInMWJAAA4cRFBdj02MkIRQTbtLJRu+Gidig+mWx3rpFGSAQAAAHVo4fYM3fXhGpmSzm/p0ZXDKMgAAI1fbJhDj42MULDL0PocUzd9sErl2TlWxzoplGQAAABAHVmzN0fjZ66S22tqUJxHYy+MlWHjFBwA0DS0bOanhy6OkMshLcnw6s73l8uTX2B1rBPGJzQAAABQB7alFWjMtBUqLveodzOvJl4cI7ufw+pYAADUqk7xTt13YYQcNum7lAo99OEymcXFVsc6IZRkAAAAQC3bl12s699eprwSt04J82rSxZHy8/ezOhYAAHWiVyuX7hwWJkPSx8lleuaTZVJZmdWxauyESrJXXnlFrVu3lr+/v/r27avly5cfc/3c3Fzddtttio+Pl8vlUseOHTV79uwTCgwAAAA0ZBkFZbr+7WVKyy9Tm2BTD44Ik3+Iv9WxAACoU2d2DNAtQ0IlSVM3F+uNL5dLbrfFqWqmxiXZxx9/rEmTJmnKlClavXq1evTooeHDhys9/ch3MSgvL9e5556r3bt369NPP9XWrVv15ptvKiEh4aTDAwAAAA1Jfqlbo99Zrt1ZxYoPMPXo+YEKigyyOhYAAPViWLdAXdMvWJL01Jp8zZq9UvJ4LE5VfTWeFOGFF17QzTffrLFjx0qSpk6dqm+++UbvvPOOHnzwwcPWf+edd5Sdna3FixfLz+/QJeatW7c+udQAAABAA1NS7tG46SuVdDBfkU7psWEuhcWHWR0LAIB6denpQSoo9eq/a4r14NIshfqv1nnn9pYawY1rapSwvLxcq1at0tChQ//3BDabhg4dqiVLlhxxm//85z/q16+fbrvtNsXGxurUU0/VU089Jc8xmsSysjLl5+dX+QIAAAAaKrfHq9s+WK3lu7MV7JAeG2xXTKtIq2MBAFDvDMPQ6AEhGtIlQF7T0J0/p2nxL+sl07Q62nHVqCTLzMyUx+NRbGxsleWxsbFKTU094ja7du3Sp59+Ko/Ho9mzZ+vRRx/V888/r7/97W9H3c/TTz+tsLCwyq/ExMSaxAQAAADqjcdr6p6P1+qnLely2aRH+ptK7BxtdSwAACxjGIYmnBOqM9q6VO41NG7Ofq1dvtnqWMdV59e6eb1excTE6I033lDv3r115ZVX6uGHH9bUqVOPus3kyZOVl5dX+bVv3766jgkAAADUmNdravLn6/X1+oNyGNJDZ3jUoUec1bEAALCc3WbonvPCdWoLp4o9hm74epe2rttudaxjqlFJFhUVJbvdrrS0tCrL09LSFBd35JOB+Ph4dezYUXa7vXJZly5dlJqaqvLy8iNu43K5FBoaWuULAAAAaEhM09Rfv0nSrJX7ZZOpB1qm6tTT4yXDsDoaAAANgtNh6MGLwtUh1k/5bkNXf7ZNyZt3Wx3rqGpUkjmdTvXu3Vs//vhj5TKv16sff/xR/fr1O+I2Z555pnbs2CGv11u5bNu2bYqPj5fT6TzB2AAAAIC1Xpi7TdMW7ZYk3Refqn7NShrFpMQAANSnAKdNj1wSodZRDmWXS1d9vEn7d6ZYHeuIavwpPmnSJL355pt69913tXnzZk2YMEFFRUWVd7u84YYbNHny5Mr1J0yYoOzsbN11113atm2bvvnmGz311FO67bbbau8oAAAAgHo09eedeumnHZKkiad6NCTBe5wtAADwXcH+Nj02MkLNI+xKK5Wu/mCd0velHX/DelbjkuzKK6/Uc889p8cee0w9e/bU2rVr9d1331VO5r93714dPHiwcv3ExETNmTNHK1asUPfu3XXnnXfqrrvu0oMPPlh7RwEAAADUk5lLdusf326RJI3t4tE5g+MlG0MsAQA4lrBAux4fGamYULv2FZm6ZsZq5aRlWx2rCseJbHT77bfr9ttvP+K/zZ8//7Bl/fr109KlS09kVwAAAECD8dmq/Xr0q02SpCs7enXR0ObMQQYAQDU1C7FryqgIPfpptnYUeHXdtOX68Jb+Co1sGHPRM2kCAAAAUA3fbTyo+z5dJ0ka0darPw+LoyADAKCG4sIcmjIqUqH+hjblejT27SUqzi+yOpYkSjIAAADguOZvTdcdH6yR15TObWXqhgviZDBJPwAAJ6RFpEOPjoxUoNPQqqwK3fz2YpUWlVgdi5IMAAAAOJZlu7I0fuZKub2mBrYwdfOIONkoyAAAOCltY/z0yCUR8ndIi9LKdfu0xXKXlVuaiU93AAAA4CjW7cvVTdOXq6zCVJ846fZL4mRnkn4AAGpFp3inHhwRIT+79MP+Uv1l+mJ5KjyW5aEkAwAAAI5gS2q+Rr+9VIXlXnWLlu65NFYOOwUZAAC1qVuiS/ddEC67If0nuUgPzVgs0+u1JAslGQAAAPAHO9ILdO3rS5Rb6lGnZtIDl8XI5aAgAwCgLvRu46+7hofJZkgfb8vXXz9YaklRRkkGAAAA/M6ujEJdPXWxskoq1C5CeuiyGAU4OW0GAKAundkxQBPOCZUkvbMxR898slKmadZrBj7tAQAAgF/tySrSNVMXK6O4Qq3DpUcvj1GwP6fMAADUh7NPCdTNgw8VZVPXZOj/vlhdr/vnEx8AAACQtD+nWNe8tkipRW4lhhl67PIYhQRwugwAQH06r3ugxg4KkST9e3mqXvrP2nrbN5/6AAAA8HkHc4t19au/KKXQreYhhqZcHqWwQE6VAQCwwkU9g3TDgENF2fOLUzT12w31sl8++QEAAODT0vJKdPVri7SvwK24EEOP/zlKEUF2q2MBAODTLjktSNf0C5Yk/ePnvXp7blKd75OSDAAAAD4rI79U17z6i3bnlSsm2NDjl0epWTAFGQAADcFlfYJ1xRlBkqS//pisGfO31un+HHX67AAAAEADlV1QqmtfW6ideeWKCjL0+GXNFB1CQQYAQENyRd9gVXhMfb6qWI99t0N+Drsu7B5TJ/uiJAMAAIDPyS0s1XWvLdS2nHJFBh4qyGLDODUGAKChMQxD1/QPkbvC1H/Xleihr7fKXV5cJ/tiuCUAAAB8St6vBVlSdrnCAwxNubSZ4sMpyAAAaKgMw9DoQaE6/1R/mZIe/W/dDLukJAMAAIDPyC8q1Zipv2hjVrlC/Q1NuTRSLSIpyAAAaOgMw9BNQ8I0rItLZh3tg5IMAAAAPiG/sETXv7ZQazLLFOwyNGVUpFo287M6FgAAqCbDMHTz0HANal83n9+UZAAAAGjy8gpKdP1rv2hdZrmCXYYevzRSraMpyAAAaGxshqEbzw6vk+fm2nIAAAA0aXn5xbp+6i9an+2uLMjaUJABANBo2W1GnTwvJRkAAACarLy8Il3/+iKtz3YrhCvIAADAMVCSAQAAoEnKyy3Uta8v0sacispJ+ltHUZABAIAjoyQDAABAk5OXU6Br3lisTb8WZI9fGqlWFGQAAOAYKMkAAADQpORm5+uaN5YoKZeCDAAAVB8lGQAAAJqM3Kw8Xf3mEm3O9Sg0wNATl0aqZTMKMgAAcHyUZAAAAGgScjNzdfVbS7U516OwgENXkFGQAQCA6qIkAwAAQKOXk56jq99epi15hwqyJy5tpsRmnOoCAIDq48wBAAAAjVrGwSxdO225tuV7FR5g6HEKMgAAcAI4ewAAAECjlZqSoaunrVRyoVcRgTY9fmmkWkRyigsAAGqOMwgAAAA0Svv3pOqqd1drf7GpqBCbHh8VqfhwTm8BAMCJ4SwCAAAAjc7uXQd01cy1Si0xFRtq1+OXRiom1G51LAAA0IhRkgEAAKBR2b5tv655f50yyqSECLumjIpUs2AKMgAAcHIoyQAAANBobErao2s/2qjccqllM4emjIpQeCAFGQAAOHmUZAAAAGgU1m1I1nWzklTgltrFOPToJZEKCbBZHQsAADQRlGQAAABo8Fas3qExn29VUYXUKc5PD18SoSAXBRkAAKg9lGQAAABo0BYv36Ybv9quUo/UNcGpySPCFeCkIAMAALWLkgwAAAAN1rzFmzX+610q90o9Wzp1/4URcvkZVscCAABNECUZAAAAGqSv523U3d/vUYUp9Wnr0l/OC5efg4IMAADUDUoyAAAANDgffrdWD81PkSnpzA7+unNYmBx2CjIAAFB3KMkAAADQcJimpn61Sv9YmiZJGnZqgMYNDpXdRkEGAADqFiUZAAAAGgTT69U/Zy3Xa2uzJEmjegfp2v7BMgwKMgAAUPcoyQAAAGA5j8erR2cu0QdbciVJ158ZrJG9g60NBQAAfAolGQAAACxV7vZo0rRF+npXgQxJ488O1bmnBlodCwAA+BhKMgAAAFimpNStW9/6RT/vL5bDJt05LExndgywOhYAAPBBlGQAAACwRF5RmW564xetTCuV0y7df2GEerV2WR0LAAD4KEoyAAAA1LuMvGKNfn2RkrLLFeiUHr44Up2bO62OBQAAfBglGQAAAOrV7vR83fDGEu0trFCYv6HHRkWqdbSf1bEAAICPoyQDAABAvdmwJ0uj31mu7DKvYkNsemRUpJqHc0oKAACsxxkJAAAA6sXCzam69f3VKqow1aaZXQ+PjFREkN3qWAAAAJIoyQAAAFAPvlq5R/d+vlFur9Q9waH7LopUoMtmdSwAAIBKlGQAAACoU2/N266/zdkmSTqznVN3DI+Qn8OwOBUAAEBVlGQAAACoE16vqX98vUlvLN4jSbqwm7/GDA6TzaAgAwAADQ8lGQAAAGqd2+PV/R+v0RfrUyVJ1/0pUCP7hMigIAMAAA0UJRkAAABqVVFZhSbOXKGfd2TLZpiaODhEQ7oFWx0LAADgmCjJAAAAUGvS80t147Rl2niwUC67qXvPC9Np7QKtjgUAAHBclGQAAACoFdvTCjTmnWVKyStTqNPU5BER6pjgb3UsAACAaqEkAwAAwElbvDNT42esVEGZR82DTD00spnimzmtjgUAAFBtlGQAAAA4KV+s2a/7P1kvt9fUKRFe3XdpjEKDOM0EAACNC2cvAAAAOCGmaeqln3bohbnbJEkDmnt128WxcjrtFicDAACoOUoyAAAA1Jjb49XDX2zQrJX7JUmXtvfq6vPiZLPZLE4GAABwYijJAAAAUCMFpW5NfH+1Fm7PlE3SrT28OmdQvGQYVkcDAAA4YZRkAAAAqLaDeSUaO22FtqQWKMAu3XeGVz1OpyADAACNHyUZAAAAqmXD/jyNm7FCafllirJX6OHTy9SyTzurYwEAANQKSjIAAAAc17cbDuqeWWtV6vaqTZBXfwvdoqD4TvJYHQwAAKCWMLMqAAAAjso0Tb3803ZNeH+1St1e9Yny6G8jQxTrcFsdDQAAoFZxJRkAAACOqNTt0eTP1uuLtQckSRe39eq64bGyM/0YAABogijJAAAAcJjMwjKNf3eFVu3Lk90wdcvpDg3tF3foHysqrA0HAABQByjJAAAAUMXW1ALd+M4ypeSXKdhh6i/DgtW9fYjVsQAAAOoUJRkAAAAqzducpjs+WK1Ct1fxQaYmXxKphCiX1bEAAADqHCUZAAAAZJqm3vl5h/4+Z5u8pnRqrKF7L45RSAD3eQIAAL6BkgwAAMDHlbo9euTjVfp0Y4YkaWgnp8YNjZAfM/QDAAAfQkkGAADgw9JyizV+2lKtTSuRTaauPzNYI04LlmFQkAEAAN9CSQYAAOCjVu9I163vr1Z6iUfBTmnSBZHq0ZL5xwAAgG+iJAMAAPBBs+Zv0cPf75TbKyVG2PTgiEjFhXNqCAAAfBdnQgAAAD7E7a7Q3z5eoXc3ZkuS+rZ16o5h4QpwMkE/AADwbZRkAAAAPiI7K18TZizXsrQySdKVfYN1+RlBsjH/GAAAACUZAACAL9i0eZ9u/mSDDhSb8veT7hoWrjPa+VsdCwAAoMGgJAMAAGjKvF7956cNun/efpV6pLgwux64KFwtm/lZnQwAAKBBoSQDAABoosoLi/W3T1ZoxtZCSVKPlk5NOi9cwf7MPwYAAPBHlGQAAABNUNqegxr/8TqtzfZIki49PUhX/SlYdhvzjwEAABwJJRkAAEBT4vVqyZIk3TZnj7LLpUCnoTuHhalPW+YfAwAAOBZKMgAAgCbCLCnR6/9ZqWfX5sljGmrZzKH7LwxXfDinfAAAAMfDGRMAAEATUHgwXZM+WaPvD1RIMjSok79uPTtMLj+GVwIAAFQHJRkAAEBj5vVq+9ptuvmbndpdJDls0thBoRreLUCGQUEGAABQXSd0a6NXXnlFrVu3lr+/v/r27avly5dXa7uPPvpIhmFo5MiRJ7JbAAAA/F5Jib78Zqku/myHdhdJkUE2/fWySJ3XPZCCDAAAoIZqXJJ9/PHHmjRpkqZMmaLVq1erR48eGj58uNLT04+53e7du3Xvvfdq4MCBJxwWAAAAh5SmpuuBGQt196IclXgMdWvh1HNXN1PHeKfV0QAAABqlGpdkL7zwgm6++WaNHTtWp5xyiqZOnarAwEC98847R93G4/Ho2muv1RNPPKG2bdueVGAAAACf5vVq15otuvjtFfo42S1D0qWnB+nRkREKC7RbnQ4AAKDRqlFJVl5erlWrVmno0KH/ewKbTUOHDtWSJUuOut2TTz6pmJgY3XTTTSeeFAAAwNeVlOiLr5fqwk93aFuBFBpg0yOXROja/iGy2xheCQAAcDJqNHF/ZmamPB6PYmNjqyyPjY3Vli1bjrjNL7/8orfffltr166t9n7KyspUVlZW+Tg/P78mMQEAAJqc0oPpeuSrtfp0t1uSoa4Jfrp7eLgig7l6DAAAoDbU6d0tCwoKdP311+vNN99UVFRUtbd7+umn9cQTT9RhMgAAgEbC49GOddt067e7tKNAMiRd1idIV/QN5uoxAACAWlSjkiwqKkp2u11paWlVlqelpSkuLu6w9Xfu3Kndu3drxIgRlcu8Xu+hHTsc2rp1q9q1a3fYdpMnT9akSZMqH+fn5ysxMbEmUQEAABq/4mJ9NneNHll+aHL+0ACb7h4eph4tXVYnAwAAaHJqVJI5nU717t1bP/74o0aOHCnpUOn1448/6vbbbz9s/c6dO2vDhg1Vlj3yyCMqKCjQv/71r6MWXy6XSy4XJ38AAMB3lRxI1SNfrdNneyp0aHilU3cPD2N4JQAAQB2p8XDLSZMmafTo0Tr99NN1xhln6MUXX1RRUZHGjh0rSbrhhhuUkJCgp59+Wv7+/jr11FOrbB8eHi5Jhy0HAACAJI9Hm1Zt0W1zdmt30aHhlZefEaQ/n8HwSgAAgLpU45LsyiuvVEZGhh577DGlpqaqZ8+e+u677yon89+7d69sthrdNBMAAACSvEVFeuvr1Xp2bZ7cpqHIIJvuGBam7olcYQ8AAFDXTmji/ttvv/2Iwyslaf78+cfcdvr06SeySwAAgCYtPTlF93yxQYvSPZIM9Wnr0sRzwhQawP/5CAAAUB/q9O6WAAAAOA6PRz8u2Kh75+1XTrnkdEhjBoZq2KkBMgyGVwIAANQXSjIAAACLlOYX6O+frNTM7cWSpNZRDt09PFyJzThFAwAAqG+cgQEAAFhga9Ju3f7lZm3P90qSLuoZqGv7h8jp4OoxAAAAK1CSAQAA1COvu0LTv1mjZ5anq8wrhQXYdMe5YerVmsn5AQAArERJBgAAUE9SDmTpLx+t1tL0cklSr1ZO3X5umMID7RYnAwAAACUZAABAHTNNU1/M36zHfkxWYYXkckg3DAjV8G5Mzg8AANBQUJIBAADUoay8Yj304QrN2V0oSeoY56c7hoWpeTinYQAAAA0JZ2cAAAB15Ic1e/TAl0nKKvPKbkhX/ilYI3sHyW7j6jEAAICGhpIMAACglhWUuvW3T1fr442ZkqTECLvuHB6utjF+FicDAADA0VCSAQAA1KKl29N178drtL+wQoakEb0CdXW/EDkdXD0GAADQkFGSAQAA1IKisgr94z8bNHPVAUlSTLCh24dHqGuC0+JkAAAAqA5KMgAAgJP0y/ZMPTBrjVIKyiVJ53Zx6YZBYQp02SxOBgAAgOqiJAMAADhB+aVuPf11kj5cuV+SFBMo3XpuuHq08rc4GQAAAGqKkgwAAOAEzN+arsmfrtfBgjJJ0vmd/XTt4AgFOLl6DAAAoDGiJAMAAKiBvGK3/vpNkj5ddejqsfhAUxPODVfXVgEWJwMAAMDJoCQDAACoph+S0vTQFxuUXlAmQ6ZGdLDpynNi5M/VYwAAAI0eJRkAAMBxpOeX6on/JumbDQclSS0CvZo4OEid2odZnAwAAAC1hZIMAADgKLxeUx+u2Kt/fLtFBaUVshvSqLYeXXp2jFwBflbHAwAAQC2iJAMAADiC7WkFmvz5Bq3ckyNJ6uIq1fhBgUrsHCsZhsXpAAAAUNsoyQAAAH6n1O3Rq/N26LX5O+X2mgqwS2Ob5+kiW7JKupxndTwAAADUEUoyAACAXy3dlaWHPluvXVnFkqS+0R6NGxSs+OIS2TdbHA4AAAB1ipIMAAD4vJyicv1jdpI+XpUiSYp0mhrX16kzesTIsNmk7dkWJwQAAEBdoyQDAAA+y+s1NWvFXj3z7WbllHokScPbGbr27CgFBXCaBAAA4Es4+wMAAD5p4/5cPfLZWq09WCRJahUq3XxOmLokBlicDAAAAFagJAMAAD4lr8St5/+7Qe+tOSivKQU4TF3VJ0jnnRYih527VgIAAPgqSjIAAOATTNPU50t26anvtyvr16GVA9v56YazwhUZbLc4HQAAAKxGSQYAAJq8LXuz9Mhn67QyrUSSlBBm6Oazw9Ut0WVxMgAAADQUlGQAAKDJyiso0Yv/Xa8ZGzLlMSWXXfpz32Bd1CtIfgytBAAAwO9QkgEAgCbHU+HRhz9u0nOL9iu33JQk/amdS2MHhSoqhKGVAAAAOBwlGQAAaDpMU4vX7NITc3Zoa16FJKlFhF03nhWqHi0ZWgkAAICjoyQDAABNwr7dqfrrN5v0/b5SSVKwy9CVfwrW8G6BstsYWgkAAIBjoyQDAACNWlFOnl6evUFvb8pTuVeyGdLwboG6sm+wQgJsVscDAABAI0FJBgAAGiVvcYk+/zlJ/1iaqsyyQ8u6JTp146AQtWzmZ204AAAANDqUZAAAoHFxu7VoxXb99efd2pJ3aFL+2FC7xgwMUZ+2LhkGQysBAABQc5RkAACgcfB4tDVpt/72ww4tTDs0KX+g09Clpwfpop5B8nNQjgEAAODEUZIBAICGzTSVvvuAnv1+sz5LLpVXhuy2Q/OOXd4nWGGBzDsGAACAk0dJBgAAGqyitExNnbtJb24uUKnHkGSobzuXrusfouYRnMYAAACg9nB2CQAAGpyK/AJ9/NMmvbAmS1llkmSoQ6yfxgwMUefmTqvjAQAAoAmiJAMAAA2Gt7hEs39J0rPLUrWn6NCy2FC7rjszRP3aMyk/AAAA6g4lGQAAsJxZXq4Fy7bpH7/s1eZf71gZ4m/osj7BOq9bIJPyAwAAoM5RkgEAAOt4PFq1Zqeenr9LKzM9kiR/P0MX9wrUiF5BCnQxKT8AAADqByUZAACof6apLUl79MwP2zTvoFuS5Gc/dMfKS0/njpUAAACof5RkAACgXu3ZeUAvzN2i/+wulilDNkMa0iVAV/QNVlSI3ep4AAAA8FGUZAAAoF7s2Zuuf89J0pe7iuQxJclQv/b+urpfsBIiOCUBAACAtTgjBQAAdWrvgWy99N0mfb49/9dyTOrVyqmr+4WoXYyfteEAAACAX1GSAQCAOrEvLU8vfbtRn2/NVcXvyrEr+garY5zT2nAAAADAH1CSAQCAWrUvs0CvzN6oTzdn/68ca/lrORZPOQYAAICGiZIMAADUiv1ZRXrl2436ZFNmZTnWs6Wfrugbok6UYwAAAGjgKMkAAMBJ2ZFeqKnfJ+nLTRmV5ViPFn66ol+IOlOOAQAAoJGgJAMAACdkY0qeXp27Rd9uydSv3Zi6Jzh0Zb9QdW5OOQYAAIDGhZIMAADUyLJdWXrlx21asDO7clnfVg6N6huqDkzIDwAAgEaKkgwAAByXaZqavzVDr/y0XSv35kqSbIapgW0cGtkvXC2b+VkbEAAAADhJlGQAAOCoPF5T3248qFd+2qHNqQWSJD/D1Dnt7RrRL0Jx4ZRjAAAAaBooyQAAwGGKyyv0ycr9evuXXdqbXSJJCrCbGt7epov6N1NECOUYAAAAmhZKMgAAUOn/27v34Cjv+97jn+fZ++p+AV24CDB3czMQCLgNqU1MWpoTZk6nrqcTM26athl7xh7muMWdjqknJ8VuU8eZhImdeByfJPbgOD6QntgxxbKBxmBsS1AD5n7H6I5uu6u9/84fEjISEmhB0kra92tmZ7W/5/usfqv58dOjD8/ze+rbwvo/+87plx9cUGtHTJKU6zL60xmW7vtikXKyWXMMAAAAYxMhGQAA0Im6dv10zxn95uBniiY671U50ZvQ/5hl6Q+XFMmd7UlzDwEAAIChRUgGAECGMsZo7+km/WTPGe0+0dDdPi/f6H96L+quOzxKLFmYxh4CAAAAw4eQDACADBOJJ/TmJzV6cc9pfVobkCRZku4uTeprCzyaPiNfue+dUoftVSK9XQUAAACGDSEZAAAZor4trF/uO6dX959XYyguSfI6jO6psPSnX8hRSUlWmnsIAAAApA8hGQAAY5gxRgcutujl3af01tF6xZOd7UVeo6/OdusrS/OV43ekt5MAAADACEBIBgDAGBSJJ/TmgUt6+fdn9EldqLt9TrH0x4uytXxWlpwOK409BAAAAEYWQjIAAMaQutYOvbL7hF6prlFTuHNFMadt9KU7XPrjJXmaNt6V5h4CAAAAIxMhGQAAo5wxRvuO1uiVvWe143SL4qazvcgn3bfAr6/Mz1Iel1QCAAAAN0RIBgDAKNUcCOvX/3VSrx6o0dm2WHf7nBKH/viubC2/w8sllQAAAMAAEZIBADCKGGNUdeyyXtl3Vm+ealW0ayF+r1NaNdun++b7NWUcl1QCAAAAqSIkAwBgFGhrD2n73tP6ZfVlnWiNd7dPLXbovgVZ+sOZXvncdhp7CAAAAIxuhGQAAIxQJplU9aeX9NqHF/Qfp1vVtQ6/3A7pD2b5dN88n6aXuGRZXFIJAAAA3C5CMgAARpj6hlZt++CMXvukTmfaE93tEwscWrPAr1WzfcrycNYYAAAAMJgIyQAAGAFisbh2VZ/Vax9f0nuXQkp03aHS45RWzvDpnrk+zSnnrDEAAABgqBCSAQCQRqfO1ev1D87qjaNNaoyY7vZZpU7dM9evlTO88nPWGAAAADDkCMkAABhmrW0hvbn/jN74pEZVDdHu9jyfpVVzfLpnjl+TivgVDQAAAAwnjsABABgG0VhCuw6e1/+tuqjKCwHFkp3ttiUtnuLRPXN9WjLFI6eDyykBAACAdCAkAwBgiBhjVH2qXts+PKv/d/yKWqOfX045udChVXP8+tIsrwqzHWnsJQAAAACJkAwAgEF3vqFd2/ad0bZDtTrfHu9uL/Bb+sNZPq2a7dOUca409hAAAABAb4RkAAAMgsZARL+rOq/tBz5TVW2ou93rlJbf4dWqOT7Nm+iWw+ZySgAAAGAkIiQDAOAWtYSi2vHJZ/pt9SXtvdimRNfVlLYlLZjg0pfm+rVsmkc+N3enBAAAAEY6QjIAAFLQHo5p55E6/bb6gv7rbHP3AvySNL3Y1t2zs/QHM1lnDAAAABhtCMkAALiJUDSuyqP1+u2BS3rvZKOiic8X4J+Sb2nlLJ9WzvKrLJ9fqwAAAMBoxdE8AAB9CEXj2n28QW9+UqPKY3XquOaUsYk50soZHt09N0cTC/lVCgAAAIwFHNkDANCltSOmd4/V6e3Dtdp9vEHh+OfBWKnf6O5pLq2cl6OKcW5ZFgvwAwAAAGMJIRkAIKM1tEe089M6vX2kVntPNSqe/PxSylKf0YrJtr54Z67umOAlGAMAAADGMEIyAEDG+aylQzsO1+rtI7X66NwVmc9zMU3NNlpWbrR8TpYmT8qRZXNnSgAAACATEJIBAMY8Y4w+rWlT5dF6vfNprT75rK3H9tk5Cf2BXafFi4tVMqdccvLrEQAAAMg0/BUAABiTIvGE9p1uUuXROlV+WqfLbZHubZakeQVJLZ9ka/kMn8Z5Esr+r3q1l09RkoAMAAAAyEi39JfAli1b9G//9m+qra3VwoUL9cMf/lDLli3rs/anP/2pfv7zn+vw4cOSpCVLluhf/uVf+q0HAOBWNQUievdYvSqP1GjPqSaFrrkjpcc2WjTeaGmFW0tmZiuvwNu9zTS3pqO7AAAAAEaQlEOy1157TRs2bNDzzz+v5cuX67nnntOaNWt0/PhxjR8//rr6Xbt26YEHHtDKlSvl9Xr1zDPP6L777tORI0c0YcKEQfkQAIDMZIzRibqAKo/UqPJIjaovB3TN8mIq8hgtnWhryXS/5k3LksfF+mIAAAAA+pZySPbss8/qW9/6lh566CFJ0vPPP68333xTL730kjZu3Hhd/SuvvNLj9Ysvvqg33nhDlZWVevDBB2+x2wCATNUWjmnviQbtOnJZu09fUU0g1mP7tHzpC1NcWjIzW9NK3NyREgAAAMCApBSSRaNRVVVV6Yknnuhus21bq1ev1r59+wb0HqFQSLFYTIWFhan1FACQkYwxOnK5VbsPX9bu4w2qqgkocc3pYm7baF6pQ0un+7T0Dr+Kchzp6ywAAACAUSulkKyxsVGJREIlJSU92ktKSnTs2LEBvcc//MM/qLy8XKtXr+63JhKJKBL5fIHltra2fmsBAGNPSyiqPZ/WaPfRWu0506yGjkSP7RNypUWTPbrrDr/mTnDL4+RsMQAAAAC3Z1hv4fX0009r69at2rVrl7xeb791mzdv1lNPPTWMPQMApFM0ntTBc016/+hl7TnVpP+u71DymrPFvE5pfrlTi6b5dFeFRyV53IESAAAAwOBK6a+M4uJiORwO1dXV9Wivq6tTaWnpDff93ve+p6efflrvvPOOFixYcMPaJ554Qhs2bOh+3dbWpkmTJqXSVQDACJZMGh2vadX7R2v0/slG7f+sXaG46VEzOd/WoikeLZ7q1ewyt1ycLQYAAABgCKUUkrndbi1ZskSVlZVat26dJCmZTKqyslKPPPJIv/v967/+q7773e9qx44dWrp06U2/j8fjkcfjSaVrAIAR7lJzSO9/elm/P9Ggvedb1RTueQllnkeaP8mt+ZO9uqvCw9piAAAAAIZVyterbNiwQevXr9fSpUu1bNkyPffccwoGg913u3zwwQc1YcIEbd68WZL0zDPP6Mknn9Srr76qKVOmqLa2VpKUnZ2t7OzsQfwoAICRpCkQ0f6T9fr9sVq9f7ZZ59t63oXS45DuLHdqfoVPCya7NbnIKZs7UQIAAABIk5RDsvvvv18NDQ168sknVVtbq0WLFuntt9/uXsz/woULsm27u/7HP/6xotGo/uzP/qzH+2zatEn//M//fHu9BwCMGPXtYe0/1aj9J+q0/1yzTjZHemy3LWnmOLsrFPNoRqlLLgehGAAAAICR4ZZWPn7kkUf6vbxy165dPV6fO3fuVr4FAGCEq20Na/+ZRn1wol77z13RmV6hmCRNzre0YLJH8yu8mlvult9j9/FOAAAAAJB+3B4MADAgl5pD2n/mivafqtf+M1d0vrVnKGbJaEqBrbkT3Lqzwqc55W7l+gjFAAAAAIwOhGQAgOvEE0kdrWnXx+evqOrsFVWdv6Ka9miPGltGUwsszZ3o0Z2TvZozwaNsL6EYAAAAgNGJkAwAoLZwTNXnm1V1vllV55p18GKzQrFkjxrbMpqeb2nuBJfmTvFr9gSPsrh8EgAAAMAYQUgGABnGGKOLVzpUdeGKPj7XGYwdr22X6VWX5TSaVSTNLnVpZoVfMyZ45XURigEAAAAYmwjJAGCMawvHdOhSqw5ebOl+NLRfv8h+uc9odpHRrDKXZlZkaWKZT7bF3ScBAAAAZAZCMgAYQ2KJpI7VtOvgpRYdvNCi/77YrNMNwevOEnNaRjO9Uc0uc2pm15liecVZEqEYAAAAgAxFSAYAo9TVyyYPXGzWf19s1cELV3Skpl2RePK62lKv0ayCpKYX25pe4tLc1otyh9oVuvdLaeg5AAAAAIw8hGQAMApcDcQOfdaqw5+16PDFZh2uaVdzR/y62hyn0YwCoxnFtqaXunXHBK/yCnw9zhJzV9dIoeH8BAAAAAAwshGSAcAIk0wanW0K6vBnrZ1h2KUWHa4JqD2auK7WZRlNzVdnIFbm1vQJfpUVu2Vx2SQAAAAApISQDADSKJ5I6kxjUIcutejwhSs68lmrjtQFFYxdf8mk0zaakmfpjmKHppa4NbXMq4pxbrmcBGIAAAAAcLsIyQBgmLSEojpa065jl1t07FKLjtW263hTSOF472X1JbfDaFq+pWnFTk0t9WhauVcTC51yOgjEAAAAAGAoEJIBwCCLJ5I62xjUpzVtnWHY5RYdqw+qJhDrs97nNJpaYGvaeJemlnk0rcStCQVOOWwCMQAAAAAYLoRkAHCLjDFqCER0si6go5dbdeyzFh2tadPJpg5FE9efHSZJ4/3SlEJbFePdqijxqKLYpdJ8h2zWEAMAAACAtCIkA4CbMMaooT2ik/UBnahr18naNp2sadPJxqBawtcvpi91nh02Oc9SRbFTFeM9mlLi0aQip7I89jD3HgAAAAAwEIRkANDlahh2oi6gk/XtOlHXrlO1bTpRH1BrP2GYJaPSLEsVhbYqxnWdHTbOpfF5nB0GAAAAAKMJIRmAjBNLJHW+KaSzjUGdaQjoTENQpxvadbIuoNZwvM99bBmVZkmT8m1NKnRqwjiPJpV4VF7glIe7SwIAAADAqEdIBmBMMsaoMRDtDMGuCcPONAZ14UpIiWTfa4bZMirLkibnSRMLHJo0zqMJJV6VF7nlJgwDAAAAgDGLkAzAqBaMxHueFdYY7A7F2vs5K0ySfA6jiVlSeY5ReZ5DZYVOTSzxqmy8T24X64YBAAAAQKYhJAMwohlj1ByK6VxTUBeaQjrfFNL5pmD3ozEY63dfW1Kpz2hiltGkyBWVjHOprKJQZeM8yi/yyXK5hu+DAAAAAABGNEIyAGmXTBrVtYd1rjGkC1eCOtcU0oWGQGcQdqVD7dG+F82/Ks9lVJ5lVJYjTcizNaHAqfICl8YXueXyuyWHQ/n/cUSBmVMVnzlumD4VAAAAAGA0ISQDMOSSSaPGQEQXmzt0qTmkS80dutQU1KUrQX3W3KFLbRFFE32vEXZVsdeoLMuoNMdWaZ5DpflOjS90qaTIoyw/Z4QBAAAAAG4PIRmA2zYYIZjDMirxS6XZlkpzbZXmO1VS4FJJkVvjC9zcQRIAAAAAMKQIyQDcVFs4ppqWsC63dqi2Nayalg5dbg6ppiWkmpbwgEIwW0bFPml8lqXxObbG5To0Lt+pcQVujctzqTjHIYdNEAYAAAAASA9CMiDDBSNx1bR26HJLWLWtnUFYTUtYl1tCqm3pUE1bRIGbrAkmdYZgRV0hWEmvEGx8vkuFWQ45HYRgAAAAAICRiZAMGKMi8YQa2iOqa4uooT2suraI6trCqm/vem4Nq6YtrLZwfEDvl+3qPBOs2G+pKNtWcY5DhbkuFeW7ND7fpaJsQjAAAAAAwOhFSAaMMtF4Ug2BrqDrmtCrri2i+vaI6tvCqmsLqzkUG/B7ZjmNir1Skc+oONtWUbZDxTkOFeS5VFTgVlGuSz63PYSfCgAAAACA9CIkA0aAUDSupkBUDYGIGtsjagpGu5+72wIRNQYiaukY2JlfkuS0pGKPUZHXKN8rFfikgixbBdkOFWQ7VZDnVlGhW34fd4cEAAAAAGQ2QjJgCMQSSbWEYmoORXUlGFVjIKKmQOdzY3uk6znc2RaMKhRLpvT+TsuoyCMVeo0KvVK+16jQb6nQbys/y9b4SEClF0/J+soXZbKyJIdjiD4pAAAAAABjAyEZcBOxRFLNoahaQjFdCUbVHIyq+ZoArDkYVXMgrCvBrpqOmNojN1/ovje3bVTgkfI8RnkeKd9nK99nKc9nK89vKzfLobxsl/JyXMrOdsq2+7/80X0+Ju/lhNp8XgIyAAAAAAAGgJAMGcEYo2A0odaOmFpDsc7njpjaOmJqC3/+unNbZ/DVEorqSujWAi9JsmSU45JyPVKeR8rzWsr3W8rzOZTfHXo5lZvtUn6OU16XJcti4XsAAAAAANKBkAyjQjJpFIzGFYjEFQjH1X71ORzvGXJ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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a sequence of 1000 values of x between 0 and 4 with equal increments.\n",
    "x =np.linspace(0,4,1000)\n",
    "y = g(x)\n",
    "\n",
    "fix, ax = plt.subplots(figsize = (15,8))\n",
    "ax.plot(x,y)   # Plotting the graph of g\n",
    "ax.set_xlim([0, 4]); ax.set_ylim([0, 1.3]);   # Setting axis limits\n",
    "\n",
    "x_trapez = np.linspace(0,4,n)\n",
    "y_trapez = g(x_trapez)\n",
    "\n",
    "for i in range(n - 1):\n",
    "    xs = [x_trapez[i], x_trapez[i+1]]\n",
    "    ys = [g(x_trapez[i]), g(x_trapez[i+1])]\n",
    "    ax.fill_between(xs, ys, edgecolor='red', facecolor='g', alpha=0.2)\n",
    "\n",
    "plt.title('Trapezoid rule with n = {} interpolation points'.format(n));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nvx-x9l2Rj1w"
   },
   "source": [
    "### Monte Carlo Estimation\n",
    "To check the accuracy of my numerical approximation, I decided to use a Monte Carlo estimator **(Section 3.2)** to estimate the integral since I cannot find a analytical solution.\n",
    "\n",
    "\\begin{align}\n",
    "I_1 &= \\int_0^4 \\frac{1}{(2+cos(x))^2} dx \\\\\n",
    "&= 4 \\int_0^4 \\frac{1}{(2+cos(x))^2} \\frac{1}{4} dx \\\\\n",
    "&= 4\\mathbb{E} \\left(\\frac{1}{(2+cos(X))^2} \\right)\n",
    "\\end{align}\n",
    "\n",
    "where $X \\sim \\mathcal{U} (0,4)$.\n",
    "\n",
    "Therefore, the MC estimator is\n",
    "\n",
    "\\begin{align}\n",
    "I_1^{MC} &= 4 \\frac{1}{n} \\sum_{i=1}^n \\left(\\frac{1}{(2+cos(U_i))^2} \\right)\n",
    "\\end{align}\n",
    "\n",
    "where $U_i \\sim \\mathcal{U}[0, 4]$ for $i = 1, 2, 3 \\ldots$ I also applied the antithetic variable $Y_i = 4 - U_i$ to reduce the variance of my estimation of $I_1$.\n",
    "\n",
    "**Note:** I will explain in the next part how antithetic variables work in more detail.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B_H41yYHRj1w",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693515804937,
     "user_tz": -60,
     "elapsed": 341,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "b658b31b-02d5-40f5-a6b0-57bb650b5dc8",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:45.100958Z",
     "start_time": "2024-07-09T09:32:44.876867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My trapezoidal approximation of the integral is: 1.9100.\n",
      "My Monte-Carlo estimation of the integral is   : 1.9113.\n"
     ]
    }
   ],
   "source": [
    "rng = np.random.default_rng(seed=56473)\n",
    "U = rng.random(10**5)*4\n",
    "\n",
    "Y = (g(U)+g(4-U))/2\n",
    "\n",
    "I_1_MC = 4*np.mean(Y)\n",
    "\n",
    "print('My trapezoidal approximation of the integral is: {:.4f}.'.format(I_1))\n",
    "print('My Monte-Carlo estimation of the integral is   : {:.4f}.'.format(I_1_MC))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BsENNuatRj1w"
   },
   "source": [
    "We can see that the Monte-Carlo estimation of the integral is very close to the trapezoidal approximation of the integral. We will now move onto solving integrals with infinite bounds using Monte Carlo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UlEiEbVWRj1x"
   },
   "source": [
    "### Infinite Bounds: Use Substitution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cKZcucNfRj1x"
   },
   "source": [
    "We will try to estimate the value of $I_2$ given by\n",
    "\n",
    "\\begin{align}\n",
    "I_2 &= \\int_{0}^{\\infty} x^{\\frac{3}{2}}e^{-\\frac{x^2}{2}} dx\n",
    "\\end{align}\n",
    "\n",
    "using the same Monte-Carlo method as above. One important difference is that the integral has infinite bounds therefore we must use a substitution to transform this into an integral with finite bounds. Although we can still approximate an integral with infinite bounds using a distribution with similar bounds, it is much easier and less computationally expensive to simulate uniform distributions.\n",
    "\n",
    "The first step to estimating $I_2$ using a MC estimator is to transform the integral so that the bounds are finite in order to use the uniform for estimating the integral. The transformation I will be using is $\\bf{y=e^{\\frac{-x^2}{2}}}$ which gives $\\bf{dx = -\\frac{1}{x}e^{\\frac{x^2}{2}}dy}$, and $\\bf{x = \\sqrt{-2\\ln{y}}}$. Applying this to $I_2$ gives us\n",
    "\n",
    "\\begin{align}\n",
    "I_2 &= \\int_{0}^{\\infty} x^{\\frac{3}{2}}e^{-\\frac{x^2}{2}} dx \\\\\n",
    "&= \\int_{1}^{0} x^{\\frac{3}{2}}e^{-\\frac{x^2}{2}} \\cdot \\left( -\\frac{1}{x} e^{\\frac{x^2}{2}} \\right) dy \\\\\n",
    "&= \\int_{0}^{1} \\sqrt{x} dy \\\\\n",
    "&= \\int_{0}^{1} (-2 \\ln{y})^{\\frac{1}{4}} dy \\\\\n",
    "&= \\mathbb{E} (-2 \\ln{Y})^{\\frac{1}{4}})\n",
    "\\end{align}\n",
    "\n",
    "where $Y \\sim \\mathcal{U}[0,1]$. Therefore my MC estimator of $I_2$ is\n",
    "\n",
    "\\begin{align}\n",
    "I_2^{MC} = \\frac{1}{n} \\sum_{i=1}^{n} (-2\\ln U_i)^\\frac{1}{4}\n",
    "\\end{align}\n",
    "\n",
    "where $U_i \\sim \\mathcal{U}[0,1]$ for $i = 1,2,3 \\ldots$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dt-B07IVRj1x"
   },
   "source": [
    "**Note**: I could apply other transformations such as $y= \\frac{1}{1+x}$ and $y= e^{-x}$.\n",
    "\n",
    "**Extra**: Although transforming the integral such that the bounds are finite make variance reduction techniques much simpler later on, an alternative could be to apply the transformation $y = x^2$ then sampling from an Exponential distribution to compute the Monte-Carlo estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SSxoENmRRj1x"
   },
   "source": [
    "### Variance Reduction Techniques\n",
    "The variance reduction methods I will be using are antithetic variables and control variables.\n",
    "\n",
    "#### Antithetic Variable\n",
    "Recall that if $U \\sim \\mathcal{U} (0,1)$ then $1-U \\sim \\mathcal{U} (0,1)$ also. Therefore it is very computationally cheap to obtain a larger sample of the uniform by taking $1-U$ compared to sampling a further $n$ random variables. Applying this gives us the antithetic pair $(U, 1-U)$ which yields\n",
    "\\begin{align}\n",
    "\\overline{X}_n &=\\frac{1}{n} \\sum_{i=1}^{n} (-2\\ln U_i)^\\frac{1}{4} \\\\\n",
    "\\overline{Y}_n &=\\frac{1}{n} \\sum_{i=1}^{n} (-2\\ln (1-U_i))^\\frac{1}{4} \\\\\n",
    "\\end{align}\n",
    "\n",
    "giving us the antithetic MC estimator\n",
    "\n",
    "\\begin{align}\n",
    "I_2^{Antithetic} &=  \\frac{\\overline{X}_n+ \\overline{Y}_n}{2}\n",
    "\\end{align}\n",
    "\n",
    "Not only does this reduce the variance of the MC estimator because we have a larger sample size, but the covariance of the antithetic pair is also negative which reduces it even further. We can see this by computing the variance of the antithetic MC estimator as follows\n",
    "\n",
    "\\begin{align*}\n",
    "{\\rm Var} (I_2^{Antithetic})\n",
    "& = {\\rm Var} \\left( \\frac{\\overline{X}_n + \\overline{Y}_n}{2} \\right) \\\\\n",
    "& = {\\rm Var} \\left(\\frac1{2n}\\sum_{i=1}^n (X_i+Y_i)\\right) \\\\\n",
    "& = \\frac{1}{4n^2} {\\rm Var} \\left(\\sum_{i=1}^n (X_i+Y_i)\\right) \\\\\n",
    "&= \\frac{1}{4n} {\\rm Var} \\left( X_i+Y_i \\right) \\\\\n",
    "& = \\frac{1}{4n} \\left({\\rm Var} (X_i) + 2{\\rm Cov} (X_i,Y_i) + {\\rm Var} (Y_i)\n",
    "\\right) \\\\\n",
    "& = \\frac{1}{2n} \\left({\\rm Var} (X) + {\\rm Cov} (X,Y) \\right).\n",
    "\\end{align*}\n",
    "\n",
    "Therefore even with half of the original sample size, antithetic variables will have a smaller variance than a standard MC with  the original sample size.\n",
    "\n",
    "#### Control Variable\n",
    "There are many different control variates to choose from however the ones I will be using are\n",
    "\n",
    "\\begin{align}\n",
    "Y_i = U_i-\\mathbb{E}(U_i) = U_i - 0.5\n",
    "\\end{align}\n",
    "\n",
    "and\n",
    "\n",
    "\\begin{align}\n",
    "Y_i = U_i^2 - \\mathbb{E}(U_i^2) = U_i^2 - \\frac{1}{3}\n",
    "\\end{align}\n",
    "\n",
    "where $U_i \\sim \\mathcal{U} (0,1)$ for $i = 1, 2, 3 \\ldots$ The following explanation of how Control Variables work apply to both.\n",
    "\n",
    "My MC estimator with a control is given by\n",
    "\n",
    "\\begin{align}\n",
    "I_2^{Control 1} = \\overline{X}_n(b) &= \\frac{1}{n} \\sum_{i=1}^{n} \\left( X_i - bY_i  \\right)\n",
    "\\end{align}\n",
    "\n",
    "where $X_i = g(U_i) = (-2 \\ln{U_i})^\\frac{1}{4} $.\n",
    "\n",
    "**Simple proof of how control variables work**\n",
    "\n",
    "Firstly, we have\n",
    "\\begin{align*}\n",
    "X_i(b) &= g(U_i) - b Y_i \\\\\n",
    "\\end{align*}\n",
    "hence\n",
    "\\begin{align*}\n",
    "\\mathbb{E}(X_i(b)) &= \\mathbb{E}(g(U_i) - b Y_i) \\\\\n",
    "&= \\mathbb{E}(g(U_i)) - b  \\mathbb{E}(Y_i) \\\\\n",
    "&= \\mathbb{E}(g(U_i)).\n",
    "\\end{align*}\n",
    "\n",
    "The variance of the MC estimator after applying a control variable is minimised when we take the value of $b$ as\n",
    "\n",
    "\\begin{align*}\n",
    "b^* =  \\frac{\\rm{Cov} (g(U), Y)}{\\rm{Var} (Y)}\n",
    "\\end{align*}\n",
    "\n",
    "which gives us\n",
    "\n",
    "\\begin{align}\n",
    "{\\rm Var} \\left( I_2^{control} \\right) = \\frac{1}{n} \\left( {\\rm Var}  (g(U)) - \\frac{{\\rm Cov}\n",
    "(g(U), Y)^2} {{\\rm Var} (Y)} \\right).\n",
    "\\end{align}\n",
    "\n",
    "As you can see above, the control variate reduces the variance most when the covariance between the control variate and the estimator is largest in magnitude.\n",
    "\n",
    "**Note:** the value of $b^*$ is obtained by minimising the variance with respect to $b$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5-LHAuN2Rj1x"
   },
   "source": [
    "## Application and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VT7Jpgz4Rj1x"
   },
   "source": [
    "#### Standard MC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hTututrrRj1y",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516230985,
     "user_tz": -60,
     "elapsed": 277,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "d2ec50c3-e1df-4d0b-a5d7-61d733b2e997",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:45.131340Z",
     "start_time": "2024-07-09T09:32:44.886062Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My standard Monte-Carlo estimation is         : 1.07802\n",
      "My standard Monte-Carlo estimation variance is: 9.15284e-08\n"
     ]
    }
   ],
   "source": [
    "nSim = 10**6\n",
    "# Defining a random number generator with a fixed seed\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "U = rng.random(nSim)  # Generating sample of size nSim from standard uniform\n",
    "Y1 = (-2*np.log(U))**0.25\n",
    "I_2_MC = np.mean(Y1)\n",
    "MC_var = np.var(Y1)/nSim\n",
    "print('My standard Monte-Carlo estimation is         : {:.6g}'.format(I_2_MC))\n",
    "print('My standard Monte-Carlo estimation variance is: {:.6g}'.format(MC_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u3SzI5TgRj1y"
   },
   "source": [
    "#### Antithetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YUbAyzMwRj1y",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516233316,
     "user_tz": -60,
     "elapsed": 283,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "60ca0036-b96e-470c-e952-8315a5e6fb01",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:45.144308Z",
     "start_time": "2024-07-09T09:32:44.910427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Antithetic Estimation is         : 1.0779\n",
      "My Antithetic Variance is           : 5.51603e-11\n",
      "Variance reduction compared to MC is: 0.999397\n"
     ]
    }
   ],
   "source": [
    "Y2 = (-2*np.log(1-U))**0.25\n",
    "I_2_Antithetic = np.mean((Y1+Y2)/2)\n",
    "Antithetic_var = np.var((Y1+Y2)/2)/nSim\n",
    "print('My Antithetic Estimation is         : {:.6g}'.format(I_2_Antithetic))\n",
    "print('My Antithetic Variance is           : {:.6g}'.format(Antithetic_var))\n",
    "print('Variance reduction compared to MC is: {:.6g}'.format( (MC_var-Antithetic_var)/MC_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aNu88OJ6Rj1y"
   },
   "source": [
    "#### Control 1: $Y_i = U_i-\\mathbb{E}(U_i) = U_i - 0.5 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yYCdtG5VRj1y",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516236832,
     "user_tz": -60,
     "elapsed": 293,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "925f9dba-4d6b-4ee7-cf9c-e86ea6f5ffdf",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:45.145732Z",
     "start_time": "2024-07-09T09:32:44.935232Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Control Variable estimation is   : 1.0778\n",
      "My Control Variable variance   is   : 3.21894e-09\n",
      "Variance reduction compared to MC is: 0.964831\n"
     ]
    }
   ],
   "source": [
    "control = U-0.5\n",
    "b = np.cov(Y1,control)[0,1]/(1/12)   # The first part gives the covariance matrix hence we must take the value of the off-diagonal\n",
    "I_2_Control = np.mean(Y1-b*control)\n",
    "Control_var = np.var(Y1-b*control)/nSim\n",
    "print('My Control Variable estimation is   : {:.6g}'.format(I_2_Control))\n",
    "print('My Control Variable variance   is   : {:.6g}'.format(Control_var))\n",
    "print('Variance reduction compared to MC is: {:.6g}'.format( (MC_var-Control_var)/MC_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2qKpJp6hRj1y"
   },
   "source": [
    "#### Control 2: $Y_i = U_i^2 - \\mathbb{E}(U_i^2) = U_i^2 - \\frac{1}{3}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9edzLugVRj1z",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516249425,
     "user_tz": -60,
     "elapsed": 333,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "ee0d1b2f-2709-4627-9df9-18d349a48750",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:45.145941Z",
     "start_time": "2024-07-09T09:32:44.957301Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Control Variable estimation is   : 1.07785\n",
      "My Control Variable variance   is   : 7.66845e-09\n",
      "Variance reduction compared to MC is: 0.916218\n"
     ]
    }
   ],
   "source": [
    "control2 = U**2 - 1/3\n",
    "b2 = np.cov(Y1,control2)[0,1]/np.var(control2)\n",
    "I_2_Control2 = np.mean(Y1-b2*control2)\n",
    "Control_var2 = np.var(Y1-b2*control2)/nSim\n",
    "print('My Control Variable estimation is   : {:.6g}'.format(I_2_Control2))\n",
    "print('My Control Variable variance   is   : {:.6g}'.format(Control_var2))\n",
    "print('Variance reduction compared to MC is: {:.6g}'.format( (MC_var-Control_var2)/MC_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qTkhCgNERj1z"
   },
   "source": [
    "#### Stratified Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GKh7RctuRj1z"
   },
   "source": [
    "Stratified sampling is a sampling technique used in statistics and research to improve the representativeness of a sample by dividing the population into subgroups or strata and then selecting samples from each subgroup. This method aims to ensure that each subgroup's characteristics are adequately represented in the final sample, which can lead to more accurate and reliable conclusions.\n",
    "\n",
    "Stratified sampling offers several benefits over simple random sampling, particularly when dealing with populations that have diverse characteristics. It helps ensure that important subgroups are not underrepresented in the sample, which can lead to more accurate and reliable statistical inferences. However, it can be more complex to implement and requires prior knowledge of the population's characteristics to create meaningful strata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SR8i1vWeRj1z",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516254084,
     "user_tz": -60,
     "elapsed": 521,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "daf2a587-e754-4520-fdd9-a190c0f576f3",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:45.309322Z",
     "start_time": "2024-07-09T09:32:45.011048Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Stratified Sampling estimation is: 1.07790026\n"
     ]
    }
   ],
   "source": [
    "j = range(1,nSim+1) # Creating a sequence of integers from 1 to nSim+1\n",
    "Strat_1 = (-2*np.log((U+j-1)/nSim))**0.25\n",
    "Strat_2 = (-2*np.log((j-U)/nSim))**0.25\n",
    "\n",
    "sol_Strat = np.mean((Strat_1+Strat_2)/2)\n",
    "print('My Stratified Sampling estimation is: {:.9g}'.format(sol_Strat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HnmwnDMmRj1z"
   },
   "source": [
    "#### Graph of Estimations using different sample sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 872
    },
    "id": "AAriweUVRj1z",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516307621,
     "user_tz": -60,
     "elapsed": 4009,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "44c530f3-f45e-47e6-c6fc-8253da4ab36b",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:46.034169Z",
     "start_time": "2024-07-09T09:32:45.082785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x1000 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instead of starting from a sample size of 1, I am going to start from half of the original sample size\n",
    "start_plot = nSim // 2\n",
    "\n",
    "_, ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "xx = np.arange(start_plot + 1, nSim + 1)\n",
    "ax.plot(xx, np.cumsum(Y1)[start_plot:] / xx, label='Standard MC')\n",
    "ax.plot(xx, np.cumsum(Y1-b*control)[start_plot:] / xx, label='Control variate')\n",
    "ax.plot(xx, np.cumsum(Y1-b2*control2)[start_plot:] / xx, label='Control variate 2')\n",
    "ax.plot(xx, np.cumsum((Y1+Y2)/2)[start_plot:] / xx, label='Antithetic')\n",
    "ax.hlines(sol_Strat, xmin=start_plot, xmax=nSim, color='purple', label='Stratified Sampling')\n",
    "ax.set_xlabel('Number of samples');\n",
    "ax.set_title('Performance of control variates');\n",
    "ax.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fJBVQ60LRj1z"
   },
   "source": [
    "|      | Estimation of $I_2$ | Variance of Estimation\n",
    "| :--- | :---: | :---: |\n",
    "| **Stratified Sampling**| $1.07790$ | N/A |\n",
    "| **Monte Carlo**| $1.07802$ | $9.15284e-08$ |\n",
    "| **Antithetic**| $1.07790$ | $5.51603e-11$|\n",
    "| **Control 1**| $1.07780$ | $3.21894e-09$ |\n",
    "| **Control 2**| $1.07785$ | $7.66845e-09$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CMYWDGyXRj10"
   },
   "source": [
    "Looking at the graph, you can clearly see that the antithetic estimation provides the best estimation of $I_2$ even for smaller sample sizes. If we take the Stratified Sampling estimation to be the true value, we can see that the antithetic estimation is almost perfect. Compared to the MC, both control variates perform better however control variate 2 seems to give a better estimation based on the graph although its variance is greater than that of control variate 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sgupVYfxRj11"
   },
   "source": [
    "## 1.3 Simulating Sample of an Unknown Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jn3BP9vmRj10"
   },
   "source": [
    "We have been given the function $f:\\mathbb{R}\\rightarrow\\mathbb{R}$,\n",
    "\n",
    "$$\n",
    "f(x) = \\left \\{ \\begin{array}{cc}\n",
    "\\frac{\\alpha}{\\sqrt{1+x}}, & \\textrm{ if } x \\in (0, 3), \\\\\n",
    "0, & \\textrm{ if } x \\notin (0,3),\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "\n",
    "where $\\alpha$ is a constant. Our goal is to simulate a sample from this non-standard distribution. This is important in computational mathematics as not all distributions can be easily sampled therefore we must be able to use Inverse Transform Method or Von Neumann's Acceptance-Rejection to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WKZMZZuhRj11"
   },
   "source": [
    "### Von Neumann's Acceptance-Rejection\n",
    "Von Neumann's acceptance-rejection method, also known as the Von Neumann algorithm, is a technique used to generate random numbers from a probability distribution that might be difficult to sample directly. This method is particularly useful when dealing with distributions that are complex or not easily invertible.\n",
    "\n",
    "For Von Neumann's Acceptance-Rejection, we aim to find an envelope $g(x)$ that gives us the smallest possible value of $c$ that guarantees the inequality $f(x) \\leq cg(x) \\quad \\forall x \\in  (0,3)$. This is because we want to maximise the acceptance rate which is given by $\\frac{1}{c}$. However, we must also consider the convenience of generating a sample from $g(x)$. We preferably want $g(x)$ to be a distribution such that there are already built in functions to generate samples from $g$ or a distribution that is easy to apply the inverse transform method on.\n",
    "\n",
    "I have chosen to use the $\\mathcal{U}[0, 3]$ distribution as an envelope to generate a sample from $f$. I.e. take\n",
    "\n",
    "$$\n",
    "g(x) = \\left \\{ \\begin{array}{cc}\n",
    "\\frac{1}{3}, & \\textrm{ if } x \\in (0, 3), \\\\\n",
    "0, & \\textrm{ if } x \\notin (0,3).\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "\n",
    "Next, I must minimise the constant $c$ such that it satisfies\n",
    "\\begin{align}\n",
    "\\frac{1}{2\\sqrt{1+x}} &\\leq c \\frac{1}{3-0} \\\\\n",
    "\\frac{3}{2\\sqrt{1+x}} &\\leq c.\n",
    "\\end{align}\n",
    "\n",
    "The LHS is maximised when $x=0$ therefore I take $c^*=\\frac{3}{2}$ to be my minimum value. As a result, the acceptance rate is given by $\\frac{1}{c^*} = \\frac{2}{3}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S6UnYhXhRj11"
   },
   "source": [
    "**Von Neumann's acceptance-rejection algorithm**\n",
    "1. Generate $U \\sim \\mathcal{U}[0, 1]$\n",
    "2. Generate $X \\sim \\mathcal{U}[0, 3]$\n",
    "3. Accept X if...\n",
    "\\begin{align}\n",
    "U \\leq \\frac{f(X)}{cg(X)} = \\frac{1}{\\sqrt{1+X}}\n",
    "\\end{align}\n",
    "otherwise reject X."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "saeBWeGmRj11"
   },
   "source": [
    "### Application and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d8TDV595Rj11"
   },
   "source": [
    "The following chunk of code is simple and generates a sample from our desired distribution without using loops and only using vectorised methods making it computationally efficient. The only problem is that the size of the sample is random whereas our goal is to generate 10,000 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HDqs2rCPRj11",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516665279,
     "user_tz": -60,
     "elapsed": 424,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "e3c8640e-d094-4bf5-bf6e-0350962f16c4",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:46.038973Z",
     "start_time": "2024-07-09T09:32:46.034710Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The acceptance rate is: 0.66742\n",
      "The sample mean is    : 1.331331427950982\n",
      "The sample size is    : 66742\n",
      "Duration              : 0:00:00.002473\n"
     ]
    }
   ],
   "source": [
    "# Timing how long it takes to run this algorithm to compare with other methods\n",
    "from datetime import datetime\n",
    "start_time = datetime.now()\n",
    "nSim = 10**5\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "U = rng.random(nSim)\n",
    "X = rng.random(nSim)*3\n",
    "Y = np.where(U<=1/np.sqrt(1+X), X, 0)   # Sets Y = X if a certain condition is satisfied, otherwise sets Y = 0\n",
    "Y = Y[Y!=0]   # Removes all zero values\n",
    "np.mean(Y)\n",
    "end_time = datetime.now()\n",
    "\n",
    "print(\"The acceptance rate is:\", len(Y)/10**5)\n",
    "print(\"The sample mean is    :\", np.mean(Y))\n",
    "print(\"The sample size is    :\", len(Y))\n",
    "print('Duration              : {}'.format(end_time-start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IgSrJ10_Rj12"
   },
   "source": [
    "We can use a WHILE loop instead such that the loop is terminated once we reach 10000 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sd7kYDrIRj12",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516672642,
     "user_tz": -60,
     "elapsed": 253,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "cb8f220a-2077-41f8-f97b-f45538ad061b",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:46.055995Z",
     "start_time": "2024-07-09T09:32:46.054204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We needed to generate 14855 candidates in order to obtain 10000 samples.\n",
      "We accepted 67.32% of our samples.\n",
      "The mean is: 1.33154\n",
      "Duration: 0:00:00.013613\n"
     ]
    }
   ],
   "source": [
    "start_time = datetime.now()\n",
    "nSim = 10**4\n",
    "sample_AR = []\n",
    "counter = 0\n",
    "\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "\n",
    "for i in range(nSim):\n",
    "    U = rng.random()\n",
    "    X = rng.random()*3\n",
    "    counter += 1\n",
    "    while (U > 1/np.sqrt(1+X)):\n",
    "        U = rng.random()\n",
    "        X = rng.random()*3\n",
    "        counter += 1\n",
    "    sample_AR.append(X)\n",
    "\n",
    "end_time = datetime.now()\n",
    "\n",
    "acceptance_rate = len(sample_AR)/counter\n",
    "print('We needed to generate {} candidates in order to obtain {} samples.'.format(counter, nSim))\n",
    "print('We accepted {:.4g}% of our samples.'.format((nSim/ counter)*100))\n",
    "print('The mean is: {:.6g}'.format(np.mean(sample_AR)))\n",
    "print('Duration: {}'.format(end_time-start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nuODBjGJRj12"
   },
   "source": [
    "As expected, the WHILE loop takes significantly more time to produce 10000 samples than it takes the first method to produce 66742 samples. This highlights the cost of using loops and hence we should aim to use vectorised methods in computational maths to improve efficiency and speed.\n",
    "\n",
    "### Plotting Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "4kxwilU_Rj12",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693516897585,
     "user_tz": -60,
     "elapsed": 1302,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "2b156428-9537-4596-91f0-b33100bad1f1",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:46.164906Z",
     "start_time": "2024-07-09T09:32:46.070722Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x118f90d10>]"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = lambda x: 1 / (2 * np.sqrt( 1 + x ))\n",
    "x = np.linspace(0,3, 1000)\n",
    "\n",
    "\n",
    "_, axs = plt.subplots(figsize=(8, 5))\n",
    "axs.hist(sample_AR, bins=100, density=True);\n",
    "axs.set_title('Histogram of random numbers generated by acceptance-rejection algorithm')\n",
    "axs.set_ylim([0, 0.8]);\n",
    "axs.set_xlim([-0.2, 3.2]);\n",
    "axs.plot(x,f(x),color='Red',label='f(x) in (0,3)');\n",
    "axs.plot([0, 0], [0, f(0)], color = 'red');\n",
    "axs.plot([3, 3], [0, f(3)], color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rFiGyqwURj13"
   },
   "source": [
    "## Inverse Transform Method\n",
    "The Inverse Transform Method is a widely used technique for generating random numbers from a given probability distribution. This method is particularly effective when dealing with probability distributions that have known cumulative distribution functions (CDFs) and can be inverted analytically. It's a fundamental approach in random number generation and is often used to transform uniform random numbers into random numbers following a desired distribution.\n",
    "\n",
    "The Inverse Transform Method ensures that the generated random numbers follow the desired probability distribution. It's a powerful technique because it leverages the properties of CDFs and their inverses to achieve this transformation. The generated random numbers will have the same distribution as the one for which the CDF is known.\n",
    "\n",
    "This method is especially useful when working with common probability distributions like the exponential, normal (Gaussian), and uniform distributions. However, it's important to note that not all distributions have easily invertible CDFs, and in those cases, alternative methods like the acceptance-rejection method or specialized algorithms might be needed.\n",
    "\n",
    "Overall, the Inverse Transform Method is a foundational concept in statistical simulations, Monte Carlo methods, and other applications where random numbers from specific distributions are required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LDP0eDECRj13"
   },
   "source": [
    "Before applying the inverse transform method, we must first find the CDF of the distribution. For $x \\in (0,3)$, we have\n",
    "\\begin{align}\n",
    "F(x) &= \\int_{0}^{x} \\frac{1}{2\\sqrt{1+v}} dv \\\\\n",
    "&= (1+x)^{\\frac{1}{2}} -1\n",
    "\\end{align}\n",
    "hence\n",
    "$$\n",
    "F(x) = \\left \\{ \\begin{array}{cc}\n",
    "0, &\\textrm{ if } x \\leq 0 \\\\\n",
    "(1+x)^{\\frac{1}{2}} -1, &\\textrm{ if }  0 < x < 3, \\\\\n",
    "1, &\\textrm{ if } x \\geq 3.\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "\n",
    "For $x \\in (0,3)$, we have $F^{-1}(X) = (X+1)^2 - 1$. Therefore, if $U \\sim \\mathcal{U}(0, 1)$, then $X = F^{-1}(U) = (U+1)^2 - 1$ is a sample from $f$.\n",
    "\n",
    "**Algorithm**\n",
    "1. Generate $U \\sim \\mathcal{U}(0, 1)$.\n",
    "2. Set $X = F^{-1}(U) = (U+1)^2 - 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mSyOFxLyRj13"
   },
   "source": [
    "### Application and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h6AUMo5wRj13",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693517409778,
     "user_tz": -60,
     "elapsed": 285,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "0a32cd19-a8d8-461a-87e4-1e888a7becce",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:46.165363Z",
     "start_time": "2024-07-09T09:32:46.157694Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration: 0:00:00.000291\n"
     ]
    }
   ],
   "source": [
    "start_time = datetime.now()\n",
    "nSim = 10**4\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "U = rng.random(nSim)\n",
    "X = (U+1)**2-1\n",
    "end_time = datetime.now()\n",
    "print('Duration: {}'.format(end_time-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "T_82FLWxRj13",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693517460631,
     "user_tz": -60,
     "elapsed": 692,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "b4e1b1e0-6aac-447b-82c9-18f3259bcdd1",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:46.254746Z",
     "start_time": "2024-07-09T09:32:46.165307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x10f686e90>]"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axs = plt.subplots(figsize=(8, 5))\n",
    "axs.hist(X, bins=100, density=True);\n",
    "axs.set_title('Histogram of random numbers generated by acceptance-rejection algorithm')\n",
    "axs.set_ylim([0, 0.8]);\n",
    "axs.set_xlim([-0.2, 3.2]);\n",
    "axs.plot(x,f(x),color='Red',label='f(x) in (0,3)');\n",
    "axs.plot([0, 0], [0, f(0)], color = 'red');\n",
    "axs.plot([3, 3], [0, f(3)], color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rwzp1unzRj14"
   },
   "source": [
    "### Comparison: Von Neumann's A-R VS Inverse Transform Method\n",
    "The choice between the Inverse Transform Method and the Von Neumann Acceptance-Rejection Method depends on the characteristics of the probability distribution you're working with and the efficiency of each method for generating random numbers from that distribution.\n",
    "\n",
    "**Inverse Transform Method:**\n",
    "\n",
    "- **Advantages:** The Inverse Transform Method is highly efficient when the cumulative distribution function (CDF) of the desired distribution is easily invertible analytically. It's straightforward to implement and can generate random numbers that exactly follow the desired distribution.\n",
    "\n",
    "- **Limitations:** This method might not be applicable when the CDF of the distribution is not easily invertible. Additionally, if the CDF has complex mathematical properties, the calculations involved in the inverse transformation might be computationally expensive.//\n",
    "\n",
    "**Von Neumann Acceptance-Rejection Method:**\n",
    "\n",
    "- **Advantages:** The Von Neumann Acceptance-Rejection Method is more versatile and can be used for distributions that might not have easily invertible CDFs. It's applicable to a wider range of scenarios and doesn't require direct knowledge of the target distribution's CDF.\n",
    "\n",
    "- **Limitations:** This method can be less efficient than the Inverse Transform Method, especially when the enveloping distribution is significantly different from the target distribution. It involves generating additional random numbers (rejections) and might require careful selection of the enveloping distribution for optimal efficiency.\n",
    "\n",
    "In summary, if the distribution you're working with has a simple and easily invertible CDF, the Inverse Transform Method is often the preferred choice due to its efficiency and directness. However, if the distribution is complex, not analytically invertible, or if you're dealing with a more general scenario where you don't have explicit knowledge of the CDF, the Von Neumann Acceptance-Rejection Method becomes a valuable option."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-r-3CC9FRj14"
   },
   "source": [
    "## 2.1 Derivative Pricing: Straddle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2om3iuZvRj14"
   },
   "source": [
    "### Closed-Form Solution\n",
    "First thing to note is that we are working in the Black-Scholes world where the stock price follows a geometric brownian motion. Therefore, we have closed-form solutions for the prices of call and put options.\n",
    "\n",
    "For a payoff $ H = |S_T - K|$, $H$ a is combination of the payoff of a put and a call with a common strike price, K (this is commonly known as a **straddle** position). I.e.\n",
    "\n",
    "\\begin{align}\n",
    " H = |S_T - K| = (S_T-K)^+ + (K - S_T)^+\n",
    "\\end{align}\n",
    "\n",
    "Therefore the time 0 price, $V_0$, of the european option must equal the total of the price of the put and the call option by no arbitrage. That is,\n",
    "\n",
    "\\begin{align}\n",
    "V_0 = C_0 + P_0\n",
    "\\end{align}\n",
    "\n",
    "where $C_0$ and $P_0$ are the prices of the call and put respectively at time 0. Since we are working in the Black-Scholes world, for a risk-neutral measure $\\mathbb{Q}$ we have\n",
    "\\begin{align}\n",
    "V_0 &= \\mathbb{E}^\\mathbb{Q}(e^{-rT}H) \\\\\n",
    "&= \\mathbb{E}^\\mathbb{Q}(e^{-rT}|S_T-K|) \\\\\n",
    "&= \\mathbb{E}^\\mathbb{Q}(e^{-rT}((S_T-K)^+ + (K - S_T)^+)) \\\\\n",
    "&= \\mathbb{E}^\\mathbb{Q}(e^{-rT}(S_T-K)^+) + \\mathbb{E}^\\mathbb{Q}(e^{-rT}(K - S_T)^+)) \\\\\n",
    "&= C_0 + P_0\n",
    "\\end{align}\n",
    "\n",
    "which has the general closed-form solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5LrpcDhGRj14"
   },
   "source": [
    "\\begin{align}\n",
    "C_0 &= S_0\\Phi(d_1) - e^{-rT}K\\Phi(d_2)\n",
    "\\end{align}\n",
    "\n",
    "and by applying the put-call parity we also have\n",
    "\n",
    "\\begin{align}\n",
    "P_0 &= -S_0\\Phi(-d_1) + e^{-rT}K\\Phi(-d_2)\n",
    "\\end{align}\n",
    "\n",
    "where\n",
    "\n",
    "\\begin{align}\n",
    "d_{1} &= \\frac{\\log(\\frac{S_0}{K}) + (r \\pm \\frac{\\sigma^2}{2})(T)}{\\sigma \\sqrt{T}} \\\\\n",
    "d_2 &= d_1 - \\sigma \\sqrt{T}\n",
    "\\end{align}\n",
    "\n",
    "and $\\Phi(\\cdot)$ is the standard normal CDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "9TTNindeRj16",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519112142,
     "user_tz": -60,
     "elapsed": 1440,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:48.153304Z",
     "start_time": "2024-07-09T09:32:46.255562Z"
    }
   },
   "outputs": [],
   "source": [
    "# Closed-form solution to the price of the Straddle\n",
    "import scipy.stats as stats\n",
    "def option_Analytic(S0,K,r,σ,T):\n",
    "    d1 = (np.log(S0/K)+(r+σ**2/2)*T)/(σ*np.sqrt(T))\n",
    "    C_0 = S0*stats.norm.cdf(d1) - np.exp(-r*T)*K*stats.norm.cdf(d1-σ*np.sqrt(T))\n",
    "    P_0 = -S0*stats.norm.cdf(-d1) + np.exp(-r*T)*K*stats.norm.cdf(-d1+σ*np.sqrt(T))\n",
    "    return C_0+P_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RG39o0ZlRj14"
   },
   "source": [
    "### Monte-Carlo Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EB_l344_Rj14"
   },
   "source": [
    "Since the price of the option is given by\n",
    "\\begin{align}\n",
    "V_0 =  \\mathbb{E}^\\mathbb{Q}[e^{-rT}|S_T-K|],\n",
    "\\end{align}\n",
    "\n",
    "the Monte-Carlo estimator for the option price is\n",
    "\\begin{align}\n",
    "V_0^{MC} =   \\frac{1}{n} \\sum_{i=1}^{n} e^{-rT}|S_{T,i} - K| \\\\\n",
    "\\end{align}\n",
    "where the stock price, at time of maturity T, is given by\n",
    "\\begin{align}\n",
    "S_{T,i} = S_0 exp\\left((r-\\frac{\\sigma^2}{2})T + \\sigma W_{T,i}\\right)\n",
    "\\end{align}\n",
    "where $ W_{T,i} \\sim  \\mathcal{N} (0,T) $. $W_{T,i}$ are independent realisations of the Wiener process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uKLrMFxhRj14"
   },
   "source": [
    "The Monte-Carlo estimator relies on the **Strong Law of Large Numbers** which implies that $V_0^{MC}$ is strongly consistent. I.e.\n",
    "\n",
    "\\begin{align*}\n",
    "\\lim _{n \\rightarrow \\infty} V_0^{MC} = V_0.\n",
    "\\end{align*}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C1Y6-XC5Rj15"
   },
   "source": [
    "### Mathematical Proof of the Variance of the Monte-Carlo Estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fUumqFqwRj15"
   },
   "source": [
    "\\begin{align}\n",
    "\\rm{Var}^\\mathbb{Q}(V_0^{MC}) &= \\rm{Var}^\\mathbb{Q}\\left(\\frac{1}{n}\\sum_{i=1}^{n}e^{-rT}|S_{T,i}-K|\\right)\\\\\n",
    "&=\\left(\\frac{1}{n}e^{-rT}\\right)^2\\rm{var}^\\mathbb{Q}\\left(\\sum_{i=1}^{n}|S_{T,i}-K|\\right)\\\\\n",
    "&=\\frac{1}{n^2}e^{-2rT}\\sum_{i=1}^{n}\\rm{var}^\\mathbb{Q}\\left(|S_{T,i}-K|\\right) \\\\\n",
    "&=\\frac{1}{n}e^{-2rT}\\rm{var}^\\mathbb{Q}\\left(|S_{T,1}-K|\\right) \\\\\n",
    "\\end{align}\n",
    "\n",
    "since\n",
    "\n",
    "\\begin{align}\n",
    "\\rm{Cov}^\\mathbb{Q} \\left( |S_{T,i}-K|, |S_{T,j}-K| \\right) = 0\n",
    "\\end{align}\n",
    "\n",
    "for $i \\neq j$. This is because each realization of the stock price, $S_{T,i}$, are IID."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zWoqRraFRj15"
   },
   "source": [
    "\\begin{align}\n",
    "\\rm{var}^\\mathbb{Q}\\left(|S_{T,1}-K|\\right) &= \\mathbb{E}^\\mathbb{Q}\\left(|S_{T,1}-K|^2\\right) - \\mathbb{E}^\\mathbb{Q}\\left(|S_{T,1} - K|\\right)^2 \\\\\n",
    "    &= \\mathbb{E}^\\mathbb{Q} \\left( S_{T,1}^2 - 2KS_{T,1} + K^2 \\right) - \\left(e^{rT} \\mathbb{E}^\\mathbb{Q}\\left(e^{-rT}|S_{T,1}-K| \\right)\\right)^2 \\\\\n",
    "    &= \\mathbb{E}^\\mathbb{Q} \\left( S_{T,1}^2 \\right) -2Ke^{rT}S_0 + K^2 - \\left( e^{rT}V_0 \\right)^2\n",
    "\\end{align}\n",
    "\n",
    "since $\\mathbb{E}^\\mathbb{Q} (e^{-rT}S_{T,1}) = S_0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZNKnlbu1Rj15"
   },
   "source": [
    "To find $\\mathbb{E}^Q \\left(S_{T,1}^2\\right)$, we must first establish a few properties of the distribution of $S_{T,1}$. Since  \\begin{align}\n",
    "\\log S_{T,1} \\sim \\mathcal{N} \\left(\\log S_0 + (r - \\frac{1}{2} \\sigma^2)T, \\sigma^2T\\right),\n",
    "\\end{align}\n",
    "we have\n",
    "\\begin{align}\n",
    "\\log S_{T,1}^2 = 2\\log S_{T,1} \\sim \\mathcal{N} \\left(2\\log S_0 + 2(r - \\frac{1}{2} \\sigma^2)T, 4\\sigma^2T\\right).\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wozn47ibRj15"
   },
   "source": [
    "**Recall** that the MGF of a normal distribution is $\\mathbb{E}({e^{tX}}) = e^{\\mu t + \\frac{1}{2} \\sigma^2 t^2} $ where $X \\sim \\mathcal{N}(\\mu,\\sigma^2)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n7PGiwhoRj15"
   },
   "source": [
    "Using the MGF of a normal gives us\n",
    "\\begin{align}\n",
    "\\mathbb{E}^\\mathbb{Q} \\left (S_{T,1}^2 \\right) &= \\mathbb{E}^\\mathbb{Q} \\left( e^{\\log S_{T,1}^2} \\right) \\\\\n",
    "&= \\mathbb{E}^\\mathbb{Q} \\left( e^{2\\log S_{T,1}} \\right) \\\\\n",
    "&= exp \\left( 2logS_0 + 2(r - \\frac{1}{2} \\sigma^2)T + \\frac{1}{2} 4 \\sigma^2T \\right) \\\\\n",
    "&= S_0^2exp \\left( 2rT + \\sigma^2T \\right).\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oI2UD4_LRj16"
   },
   "source": [
    "Collecting all the terms together, we get\n",
    "\\begin{align}\n",
    "\\rm{var}^\\mathbb{Q}(V_0^{MC}) &=   \\rm{var}^\\mathbb{Q}\\left( \\frac{1}{n}\\sum_{i=1}^{n}e^{-rT}|S_{T,i}-K|\\right) \\\\\n",
    "&= \\frac{1}{n}e^{-2rT} \\left( S_0^2exp \\left( 2rT + \\sigma^2T \\right)-2Ke^{rT}S_0 + K^2 - \\left( e^{rT}V_0 \\right)^2 \\right)\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1WMI9rDLRj16"
   },
   "source": [
    "### Confidence Interval of the Monte-Carlo Estimator\n",
    "For $\\alpha \\in (0,1)$ consider the unique number $q_\\alpha$ such that\n",
    "\n",
    "\\begin{align}\n",
    "\\Phi (q_\\alpha) = 1 - \\alpha.\n",
    "\\end{align}\n",
    "\n",
    "The CLT then yields that $\\mu = \\mathbb{E}[X]$ belongs to the $1 - 2\\alpha$ confidence interval **(Section 3.4)**\n",
    "\n",
    "\\begin{align*}\n",
    "\\left( \\overline{X}_n - q_\\alpha \\frac{\\sigma}{\\sqrt{n}};\\,\n",
    "\\overline{X}_n + q_\\alpha \\frac{\\sigma}{\\sqrt{n}} \\right)\n",
    "\\end{align*}\n",
    "as $n \\rightarrow \\infty$.\n",
    "\n",
    "Let $\\sigma_{MC}^2 = \\rm{var}^\\mathbb{Q}(V_0^{MC}) $.  Then the $1-2\\alpha$ confidence interval for this case is\n",
    "\n",
    "\\begin{align*}\n",
    "\\left( V_0^{MC} - q_\\alpha \\sigma_{MC};\\,\n",
    "V_0^{MC} + q_\\alpha \\sigma_{MC} \\right).\n",
    "\\end{align*}\n",
    "\n",
    "For a 95% confidence interval, we have\n",
    "\\begin{align*}\n",
    "\\left( V_0^{MC} - 1.96 \\sigma_{MC};\\,\n",
    "V_0^{MC} + 1.96 \\sigma_{MC} \\right).\n",
    "\\end{align*}\n",
    "\n",
    "For a 99% confidence interval, we have\n",
    "\\begin{align*}\n",
    "\\left( V_0^{MC} - 2.58 \\sigma_{MC};\\,\n",
    "V_0^{MC} + 2.58 \\sigma_{MC} \\right).\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "eKWWxWS4Rj16",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519239270,
     "user_tz": -60,
     "elapsed": 371,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:48.153638Z",
     "start_time": "2024-07-09T09:32:48.030402Z"
    }
   },
   "outputs": [],
   "source": [
    "# Monte Carlo: Estimation, Analytical Variance, Sample Variance, and Confidence Interval\n",
    "def option_MC(S0, r, σ, T, K, α):\n",
    "    W = rng.standard_normal(size=nSim)   # Generating sample from standard normal\n",
    "    S_T = S0 * np.exp((r - σ**2/2) * T + σ * np.sqrt(T) * W)   # Calculating stock price from GBM\n",
    "    MC_samples = np.exp(-r * T) * abs(S_T - K)\n",
    "\n",
    "    V_0 = np.mean(MC_samples)\n",
    "\n",
    "    # Calculating the analytical variance of the estimator\n",
    "    variance_MC = 1/nSim *np.exp(-2*r*T) * (S0**2*np.exp(2*r*T+σ**2*T) - 2*K*np.exp(r*T)*S0 + K**2 -(np.exp(r*T)*option_Analytic(S0,K,r,σ,T))**2)\n",
    "\n",
    "    # Calculating confidence interval\n",
    "    lower = V_0 - stats.norm.ppf(1-α) * np.sqrt(variance_MC)\n",
    "    upper = V_0 + stats.norm.ppf(1-α) * np.sqrt(variance_MC)\n",
    "\n",
    "    # Calculating the sample variance of the estimator\n",
    "    sample_variance_MC = np.var(MC_samples)/nSim\n",
    "\n",
    "    return V_0, variance_MC, sample_variance_MC, lower, upper"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Application and Results"
   ],
   "metadata": {
    "id": "zI3rYPH4nE9a"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zquqohb1Rj17",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519216490,
     "user_tz": -60,
     "elapsed": 254,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "8740dd89-7ae1-435c-c356-c73035219ff6",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:48.153899Z",
     "start_time": "2024-07-09T09:32:48.033011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The analytical price of the european option is: 1.080474.\n"
     ]
    }
   ],
   "source": [
    "S0 =4; K=5; r=0.01; σ=0.2; T=1\n",
    "\n",
    "V_0 = option_Analytic(S0,K,r,σ,T)\n",
    "\n",
    "print('The analytical price of the european option is: {:.7g}.'.format(V_0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hr14yzcIRj17",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519242039,
     "user_tz": -60,
     "elapsed": 270,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "28a26490-63bb-41d1-c779-d31101f6145a",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:48.154089Z",
     "start_time": "2024-07-09T09:32:48.035633Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MC price of the european option is                                  : 1.080483.\n",
      "The MC variance of the european option is                               : 3.885224e-07.\n",
      "The MC sample variance of the european option is                        : 3.883354e-07.\n",
      "The lower bound of the 95% confidence interval of the european option is: 1.079262.\n",
      "The upper bound of the 95% confidence interval of the european option is: 1.081705.\n"
     ]
    }
   ],
   "source": [
    "nSim = 10**6\n",
    "\n",
    "confidence = 0.95\n",
    "alpha = (1-confidence)/2\n",
    "\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "result = option_MC(S0, r, σ, T, K, alpha)\n",
    "\n",
    "print('The MC price of the european option is                                  : {:.7g}.'.format(result[0]))\n",
    "print('The MC variance of the european option is                               : {:.7g}.'.format(result[1]))\n",
    "print('The MC sample variance of the european option is                        : {:.7g}.'.format(result[2]))\n",
    "print('The lower bound of the 95% confidence interval of the european option is: {:.7g}.'.format(result[3]))\n",
    "print('The upper bound of the 95% confidence interval of the european option is: {:.7g}.'.format(result[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "t5Eo2geXRj17",
    "outputId": "55fef6bb-40ed-480e-fd75-14eb48b50109",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:32:48.154563Z",
     "start_time": "2024-07-09T09:32:48.055904Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage difference in the analytical and MC price is: -0.0008833.\n"
     ]
    }
   ],
   "source": [
    "print('The percentage difference in the analytical and MC price is: {:.4g}.'.format((V_0 - result[0])/V_0 *100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bPIkQxmIRj15"
   },
   "source": [
    "## 2.2 Generating Normal Distribution using Box-Muller\n",
    "For my Monte-Carlo estimator in part i, I need to generate a sample from the normal distribution, $\\mathcal{N} (0,T)$. There are multiple methods to doing this such as applying acceptance-rejection using the doubly exponential distribution. I will be using the **Box-Muller** method in my work. The Box-Muller method transforms two independent standard uniformly random variables into two independent standard normally distributed random variables.\n",
    "\n",
    "**Algorithm:**\n",
    "1. Generate independent random variables $U_1, U_2 \\sim\\mathcal{U}(0,1)$;\n",
    "2. Set $R = -2\\log(U_1)$;\n",
    "3. Set $\\theta = 2 \\pi U_2$;\n",
    "4. Set $Z_1 = \\sqrt{R} \\cos (\\theta)$;\n",
    "5. Set $Z_2 = \\sqrt{R} \\sin (\\theta)$;\n",
    "6. Set $W_1 = \\sqrt{T}Z_1$ and $W_2 = \\sqrt{T}Z_2$\n",
    "7. Return $W_1$ and $W_2$.\n",
    "\n",
    "We have $W_1, W_2 \\sim \\mathcal{N} (0,T)$.\n",
    "\n",
    "The proof of this algorithm involves transforming a double integral using $v = x^2 + y^2$ and $\\theta = arctan(\\frac{y}{x})$ then setting the integral equal to $1$. I will not go into further detail as the purpose of this project is to implement the relevant advanced simulation techniques. Something I think is worth mentioning is the refinement of the **Box-Muller** using the **Polar Form**. This method avoids evaluating two trigonometric functions, which slows down the algorithm, by generating $\\cos(\\theta)$ and $\\sin (\\theta)$ using Cartesian coordinates. Again, I will not go into more detail about how to do this as I just wanted to mention alternative methods.\n",
    "\n",
    "**Criticisms of the Polar method as an improvement to Box-Muller**:\n",
    "1. It avoids calculating trigonometric functions at the expense of generating 27% more random variables.\n",
    "2. The polar method involves a loop. The computational cost of this depends on the programming language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "amJDdq-URj18",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519330639,
     "user_tz": -60,
     "elapsed": 262,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "81590b56-ce0e-4c9c-c78f-faf1da3168c2",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:51:59.601139Z",
     "start_time": "2024-07-09T09:51:59.548841Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MC price of the european option is                                  : 1.080088.\n",
      "The MC variance of the european option is                               : 3.885224e-07.\n",
      "The MC sample variance of the european option is                        : 3.889866e-07.\n",
      "The lower bound of the 95% confidence interval of the european option is: 1.078866.\n",
      "The upper bound of the 95% confidence interval of the european option is: 1.08131.\n"
     ]
    }
   ],
   "source": [
    "def option_MC_Box_Muller(S0, r, σ, T, K, α, W):\n",
    "    S_T = S0 * np.exp((r - σ**2/2) * T + σ * np.sqrt(T) * W)\n",
    "    MC_samples = np.exp(-r * T) * abs(S_T - K)\n",
    "\n",
    "    V_0 = np.mean(MC_samples)\n",
    "    variance_MC = 1/nSim *np.exp(-2*r*T) * (S0**2*np.exp(2*r*T+σ**2*T) - 2*K*np.exp(r*T)*S0 + K**2 -(np.exp(r*T)*option_Analytic(S0,K,r,σ,T))**2)\n",
    "\n",
    "    lower = V_0 - stats.norm.ppf(1-α) * np.sqrt(variance_MC)\n",
    "    upper = V_0 + stats.norm.ppf(1-α) * np.sqrt(variance_MC)\n",
    "\n",
    "    sample_variance_MC = np.var(MC_samples)/nSim\n",
    "\n",
    "    return V_0, variance_MC, sample_variance_MC, lower, upper\n",
    "\n",
    "# Generating standard normal using Box-Muller\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "U1 = rng.random(500_000)\n",
    "U2 = rng.random(500_000)\n",
    "R = -2*np.log(U1)\n",
    "theta = 2*np.pi*U2\n",
    "Z1 = np.sqrt(R)*np.cos(theta)\n",
    "Z2 = np.sqrt(R)*np.sin(theta)\n",
    "Z = np.concatenate((Z1,Z2))\n",
    "\n",
    "result_Box_Muller = option_MC_Box_Muller(S0, r, σ, T, K, alpha, Z)\n",
    "\n",
    "print('The MC price of the european option is                                  : {:.7g}.'.format(result_Box_Muller[0]))\n",
    "print('The MC variance of the european option is                               : {:.7g}.'.format(result_Box_Muller[1]))\n",
    "print('The MC sample variance of the european option is                        : {:.7g}.'.format(result_Box_Muller[2]))\n",
    "print('The lower bound of the 95% confidence interval of the european option is: {:.7g}.'.format(result_Box_Muller[3]))\n",
    "print('The upper bound of the 95% confidence interval of the european option is: {:.7g}.'.format(result_Box_Muller[4]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yL1YlO1ARj18"
   },
   "source": [
    "## 2.3 Derivatives Pricing: Variance Reduction Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RnnzEp7xRj18"
   },
   "source": [
    "### Antithetic\n",
    "Since the standard normal distribution is symmetric around 0, if $Z \\sim \\mathcal{N} (0,1)$ then $-Z \\sim \\mathcal{N} (0,1)$ also. This gives us the antithetic pair $(Z,-Z)$.\n",
    "\n",
    "**Note:** I have explained in **1b)** how antithetic variables reduce the variance. The same concept applies here where\n",
    "\n",
    "\\begin{align}\n",
    "S_{T,i} &= S_0 exp\\left((r-\\frac{\\sigma^2}{2})T + \\sigma W_{T,i}\\right) \\\\\n",
    "S_{T,i}' &= S_0 exp\\left((r-\\frac{\\sigma^2}{2})T - \\sigma W_{T,i}\\right)\n",
    "\\end{align}\n",
    "\n",
    "therefore our MC estimator with antithetic variate is given by\n",
    "\n",
    "\\begin{align}\n",
    "&\\overline{X}_n = \\frac{1}{n} \\sum_{i=1}^{n} e^{-rT}|S_{T,i} - K| \\\\\n",
    "&\\overline{Y}_n = \\frac{1}{n} \\sum_{i=1}^{n} e^{-rT}|S_{T,i}' - K| \\\\\n",
    "&V_0^{Antithetic} =  \\frac{\\overline{X}_n+ \\overline{Y}_n}{2}.\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "Wptf4CQiRj18",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519389035,
     "user_tz": -60,
     "elapsed": 368,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:09.466511Z",
     "start_time": "2024-07-09T09:52:09.439846Z"
    }
   },
   "outputs": [],
   "source": [
    "def option_MC_antithetic(S0, r, σ, T, K):\n",
    "    rng = np.random.default_rng(seed=56473)\n",
    "    W = rng.standard_normal(nSim)\n",
    "\n",
    "    S_T1 = S0 * np.exp((r - σ**2/2) * T + σ * np.sqrt(T) * W)\n",
    "    S_T2 = S0 * np.exp((r - σ**2/2) * T + σ * np.sqrt(T) * -W)\n",
    "    MC_samples1 = np.exp(-r * T) * abs(S_T1 - K)\n",
    "    MC_samples2 = np.exp(-r * T) * abs(S_T2 - K)\n",
    "    MC_samples = (MC_samples1 + MC_samples2)/2\n",
    "\n",
    "    return MC_samples.mean(), MC_samples.std() / np.sqrt(nSim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j6QeRnNIRj18",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519391379,
     "user_tz": -60,
     "elapsed": 763,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "7ddebb38-85f0-4320-9203-5835ac1f57bf",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:10.365339Z",
     "start_time": "2024-07-09T09:52:10.324726Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The antithetic estimation of the price of the option is: 1.080671.\n",
      "The standard deviation of the antithetic estimation is : 0.0002294192.\n"
     ]
    }
   ],
   "source": [
    "antithetic = option_MC_antithetic(S0,r,σ,T,K)\n",
    "\n",
    "print('The antithetic estimation of the price of the option is: {:.7g}.'.format(antithetic[0]))\n",
    "print('The standard deviation of the antithetic estimation is : {:.7g}.'.format(antithetic[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cf_rNPagRj19"
   },
   "source": [
    "### Control Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "irFZnp1vRj19"
   },
   "source": [
    "Again, the concept of control variable and how it works is explained in **1b)**. Since we are working in the Black-Scholes world with risk-neutrality, we know that by FTAP we have $\\mathbb{E} (e^{-rT}S_T) = S_0$. Therefore a suitable control variable to apply would be\n",
    "\n",
    "\\begin{align}\n",
    "Y_i = e^{-rT}S_{T,i} - \\mathbb{E}^\\mathbb{Q} (e^{-rT}S_{T,i}) =  e^{-rT}S_{T,i} - S_0.\n",
    "\\end{align}\n",
    "\n",
    "This is a valid control since\n",
    "\n",
    "\\begin{align}\n",
    "\\mathbb{E}^\\mathbb{Q} (Y_i) &= \\mathbb{E}^\\mathbb{Q} (e^{-rT}S_{T,i} - \\mathbb{E}^\\mathbb{Q} (e^{-rT}S_{T,i}) ) \\\\\n",
    "&=  S_0 - S_0 \\\\\n",
    "&=  0.\n",
    "\\end{align}\n",
    "\n",
    "Our control estimator of the price of the european option would therefore be\n",
    "\n",
    "\\begin{align}\n",
    "V_0^{Control 1} = \\frac{1}{n} \\sum_{i=1}^{n} \\left( e^{-rT}|S_{T,i} - K| - b(e^{-rT}S_{T,i} - S_0) \\right).\n",
    "\\end{align}\n",
    "\n",
    "For the optimal value of b, we take\n",
    "\\begin{align}\n",
    "b^* =\\frac{ \\rm{Cov}(e^{-rT}|S_{T,i} - K|,e^{-rT}S_{T,i}) }{\\rm{Var} (e^{-rT}S_{T,i}) }.\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "aPCW61LnRj19",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519399606,
     "user_tz": -60,
     "elapsed": 728,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:15.690711Z",
     "start_time": "2024-07-09T09:52:15.669344Z"
    }
   },
   "outputs": [],
   "source": [
    "def option_MC_control1(S0, r, σ, T, K):\n",
    "    rng = np.random.default_rng(seed=56473)\n",
    "    W = rng.standard_normal(nSim)\n",
    "\n",
    "    S_T = S0 * np.exp((r - σ**2/2) * T + σ * np.sqrt(T) * W)\n",
    "    MC_samples = np.exp(-r * T) * abs(S_T - K)\n",
    "\n",
    "    Y = np.exp(-r*T)*S_T - S0\n",
    "    b = np.cov(MC_samples, Y)[0,1]/np.var(Y)\n",
    "\n",
    "    estimator = MC_samples - b*Y\n",
    "\n",
    "    return estimator.mean(), estimator.std() / np.sqrt(len(estimator))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eklbgL5iRj19",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693519400290,
     "user_tz": -60,
     "elapsed": 317,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "29350cdf-d847-4c84-a1e4-0853b7181e62",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:16.380686Z",
     "start_time": "2024-07-09T09:52:16.340069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The control 1 estimation of the price of the option is: 1.0807.\n",
      "The standard deviation of the control 1 estimation is : 0.0003793022.\n"
     ]
    }
   ],
   "source": [
    "control1 = option_MC_control1(S0,r,σ,T,K)\n",
    "\n",
    "print('The control 1 estimation of the price of the option is: {:.7g}.'.format(control1[0]))\n",
    "print('The standard deviation of the control 1 estimation is : {:.7g}.'.format(control1[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oqG4dfZ-Rj1-"
   },
   "source": [
    "|      | European option price | Standard Deviation\n",
    "| :--- | :---: | :---: |\n",
    "| **Analytical**| $1.080474$ | N/A |\n",
    "| **Monte Carlo**| $1.080483$ | $0.000623$ |\n",
    "| **Antithetic**| $1.080671$ | $0.000229$|\n",
    "| **Control**| $1.080700$ | $0.000379$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ij8gfgFaRj1-"
   },
   "source": [
    "## 3.1 Euler and Milstein Schemes\n",
    "\n",
    "Euler and Milstein schemes are numerical methods used for approximating solutions to stochastic differential equations (SDEs), which are equations that involve both deterministic differential terms and stochastic (random) terms. These schemes are particularly important in the field of computational finance, physics, and other areas where random processes are involved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OZnffpQXRj1-"
   },
   "source": [
    "To demonstrate the use of Euler and Milstein Schemes, we will assume the price of an asset follows the dynamic given by\n",
    "\n",
    "\\begin{align}\n",
    "dS_t = rS_t dt + \\sigma (1+S_t) dW_t\n",
    "\\end{align}\n",
    "\n",
    "I will be applying 2 different schemes to simulate sample paths for the stock prices that follows the dynamics under the **risk-neutral measure** given by $ dS_t = rS_t dt + \\sigma (1+S_t) dW_t^Q $ where $r$ is the risk-free rate and $\\sigma >0$. First note that this dynamic can be written as\n",
    "\n",
    "\\begin{align}\n",
    "S_t = S_0 + \\int_0^t r S_t du +  \\int_0^t \\sigma (1+S_t) dW_u^Q.\n",
    "\\end{align}\n",
    "\n",
    "### Euler Scheme\n",
    "The first-order Euler scheme is given by $\\hat{S}_0 = S_0$ and\n",
    "\n",
    "\\begin{align}\n",
    "\\hat{S}_{(i+1)h} &= \\hat{S}_{ih} + r \\hat{S}_{ih} h + \\sigma (1+\\hat{S}_{ih}) (W_{(i+1)h} - W_{ih}) \\\\\n",
    "& =  \\hat{S}_{ih} + r \\hat{S}_{ih} h + \\sigma (1+\\hat{S}_{ih}) \\sqrt{h} Z_i ,    \\qquad i = 0, 1, 2, \\ldots.\n",
    "\\end{align}\n",
    "\n",
    "where $Z_i \\sim \\mathcal{N}(0,1)$ and $h$ is the step size, that is $ h = \\frac{T}{n}$.\n",
    "\n",
    "However, since $S_t \\geq 0$ we need to make a slight adjustment. That is,\n",
    "\\begin{align}\n",
    "\\hat{S}_{(i+1)h} = \\max \\left( 0, \\hat{S}_{ih} + r \\hat{S}_{ih} h + \\sigma (1+\\hat{S}_{ih}) \\sqrt{h} Z_i \\right).\n",
    "\\end{align}\n",
    "\n",
    "### Milstein Scheme\n",
    "An improved version of the Euler Scheme is the Milstein scheme which focuses on improving the diffusion term.\n",
    "\n",
    "The Milstein scheme  is given by\n",
    "\n",
    "\\begin{align}\n",
    "\\hat{S}_{(i+1)h} &= \\max \\left(0, \\hat{S}_{ih} + r \\hat{S}_{ih} h + \\sigma (1+\\hat{S}_{ih}) (W_{(i+1)h} - W_{ih})  +  \n",
    "\\frac{1}{2} \\sigma (1+\\hat{S}_{ih}) \\sigma \\left((W_{(i+1)h} - W_{ih})^2 -h\\right) \\right) \\\\\n",
    "&= \\max \\left(0, \\hat{S}_{ih} + r \\hat{S}_{ih} h + \\sigma (1+\\hat{S}_{ih}) \\sqrt{h} Z_i + \\frac{1}{2} \\sigma^2 (1+\\hat{S}_{ih}) h (Z_i^2 - 1) \\right)\n",
    "\\qquad  i = 0, 1, 2, \\ldots\n",
    "\\end{align}\n",
    "where $Z_i \\sim \\mathcal{N}(0,1)$ and $h$ is the step size, that is $ h = \\frac{T}{n}$.\n",
    "\n",
    "\n",
    "Note: we have set a minimum of 0 since the stock price cannot be negative in the real-world."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mlx82FRxRj1-"
   },
   "source": [
    "### Applications and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "mmqkc5z4Rj1_",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693520660519,
     "user_tz": -60,
     "elapsed": 263,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:29.984033Z",
     "start_time": "2024-07-09T09:52:29.931224Z"
    }
   },
   "outputs": [],
   "source": [
    "def Euler(z, T, nSteps, r, σ, S0):\n",
    "    sample = np.zeros(nSteps + 1)  # Pre defining vector of zeros to store values\n",
    "    sample[0] = S0\n",
    "    h = T / nSteps\n",
    "\n",
    "    for i in range(nSteps):\n",
    "        sample[i + 1] = max(0, sample[i] * (1 + (h * r) + (σ * np.sqrt(h) * z[i])) + (σ * np.sqrt(h) * z[i] ))\n",
    "\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "hdcjP47WRj1_",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693520661843,
     "user_tz": -60,
     "elapsed": 2,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:29.984253Z",
     "start_time": "2024-07-09T09:52:29.935234Z"
    }
   },
   "outputs": [],
   "source": [
    "def Milstein(z, T, nSteps, r, σ, S0):\n",
    "    sample = np.zeros(nSteps + 1)\n",
    "    sample[0] = S0\n",
    "    h = T / nSteps\n",
    "\n",
    "    for i in range(nSteps):\n",
    "        sample[i + 1] = max(0, sample[i] * (1 + (h * r) + (σ * np.sqrt(h) * z[i])) + (σ * np.sqrt(h) * z[i] ) + 0.5*σ**2*(sample[i]+1) *h*(z[i]**2-1))\n",
    "\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "id": "8i17XhtiRj1_",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693520664170,
     "user_tz": -60,
     "elapsed": 1312,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "ddb9807a-d17c-4da1-a896-47d840c1c8b6",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:30.115694Z",
     "start_time": "2024-07-09T09:52:29.968270Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x600 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nSims = 10\n",
    "\n",
    "S0 = 4; r = 0.01; σ = 0.2; T = 1; nSteps = 250\n",
    "\n",
    "path = np.zeros(n+1)\n",
    "\n",
    "_, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 6))\n",
    "\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "\n",
    "# Plotting each realisation of the stock price paths\n",
    "for i in range(nSims):\n",
    "    z = rng.standard_normal(size=nSteps)\n",
    "    path = Euler(z, T, nSteps, r, σ, S0)\n",
    "    ax1.plot(path)\n",
    "ax1.set_title(f'Realizations of {nSims} Stock price paths using Euler scheme');\n",
    "\n",
    "rng = np.random.default_rng(seed=56473)\n",
    "for i in range(nSims):\n",
    "    z = rng.standard_normal(size=nSteps)\n",
    "    path = Milstein(z, T, nSteps, r, σ, S0)\n",
    "    ax2.plot(path)\n",
    "ax2.set_title(f'Realizations of {nSims} Stock price paths using Milstein Scheme');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "whvm7nKrRj1_"
   },
   "source": [
    "## 3.2 Pricing the Derivative under the new Dynamic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "czjv17tiRj1_"
   },
   "source": [
    "Since the new dynamic is still under the **risk-neutral measure**, we still have $V_0 = \\mathbb{E}^\\mathbb{Q} \\left( e^{-rT} |S_T - K| \\right)$. Therefore the Monte-Carlo estimator remains the same as the one in section 2.2.\n",
    "\n",
    "\\begin{align*}\n",
    "    V^{\\textrm{MC}}_0(n) = \\frac{1}{n} \\sum_{i=1}^n e^{-rT} |S_{T,i} - K|,\n",
    "\\end{align*}\n",
    "\n",
    "where $S_{T,i}$ are independent realizations of the stock price at time of maturity, $T$.\n",
    "\n",
    "The only thing that is different in this question is the dynamics of the stock price. The price of the stock is now given by\n",
    "\n",
    "\\begin{align}\n",
    "S_t = S_0 + \\int_0^t r S_t du +  \\int_0^t \\sigma (1+S_t) dW_u.\n",
    "\\end{align}\n",
    "\n",
    "which has no closed-form solution for the price of call and put options. Therefore we can no longer compute the european option price analytically like we did in 3a). To obtain the stock price at maturity, $T$, we must now apply Euler-type Schemes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-VmCb7c_Rj1_"
   },
   "source": [
    "### Euler Scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "PQB10QLJRj2A",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693520776668,
     "user_tz": -60,
     "elapsed": 257,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:30.118045Z",
     "start_time": "2024-07-09T09:52:30.115426Z"
    }
   },
   "outputs": [],
   "source": [
    "def option_price_Euler(K,α):\n",
    "    option_payoff = np.zeros(nSim)\n",
    "\n",
    "    rng = np.random.default_rng(seed=56473)\n",
    "\n",
    "    for i in range(nSim):\n",
    "        Z = rng.standard_normal(size=nsteps)\n",
    "        S = Euler(Z, T, nsteps, r, σ, S0)\n",
    "        option_payoff[i] = np.exp(-r * T) * abs(S[-1]-K)\n",
    "\n",
    "    option_price_MC = np.mean(option_payoff)\n",
    "    MC_std = option_payoff.std() / np.sqrt(nSim)\n",
    "\n",
    "    lower = option_price_MC - stats.norm.ppf(1-𝛼)*MC_std\n",
    "    upper = option_price_MC + stats.norm.ppf(1-𝛼)*MC_std\n",
    "\n",
    "    return option_price_MC, MC_std, lower, upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SSeWF7lvRj2A",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693520791686,
     "user_tz": -60,
     "elapsed": 11126,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "b87509ca-4692-460b-ffe5-96376d2c9023",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:32.819522Z",
     "start_time": "2024-07-09T09:52:30.118765Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MC estimation of the price of the european option is                : 1.175995.\n",
      "The MC standard deviation of the estimation is                          : 0.007334558.\n",
      "The lower bound of the 95% confidence interval of the european option is: 1.161619.\n",
      "The upper bound of the 95% confidence interval of the european option is: 1.19037.\n"
     ]
    }
   ],
   "source": [
    "nSim = 10**4\n",
    "nsteps = 250\n",
    "\n",
    "confidence = 0.95\n",
    "𝛼 = (1-confidence)/2\n",
    "\n",
    "S0 = 4; K=5; r=0.01; σ = 0.2; T=1\n",
    "\n",
    "Euler_Result = option_price_Euler(K,α)\n",
    "\n",
    "print('The MC estimation of the price of the european option is                : {:.7g}.'.format(Euler_Result[0]))\n",
    "print('The MC standard deviation of the estimation is                          : {:.7g}.'.format(Euler_Result[1]))\n",
    "print('The lower bound of the 95% confidence interval of the european option is: {:.7g}.'.format(Euler_Result[2]))\n",
    "print('The upper bound of the 95% confidence interval of the european option is: {:.7g}.'.format(Euler_Result[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kzLedVRtRj2A"
   },
   "source": [
    "### Milstein Scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "KTdYDqLRRj2A",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693520792410,
     "user_tz": -60,
     "elapsed": 6,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:32.823011Z",
     "start_time": "2024-07-09T09:52:32.820497Z"
    }
   },
   "outputs": [],
   "source": [
    "def option_price_Milstein(K,α):\n",
    "    option_payoff = np.zeros(nSim)\n",
    "\n",
    "    rng = np.random.default_rng(seed=56473)\n",
    "\n",
    "    for i in range(nSim):\n",
    "        Z = rng.standard_normal(size=nsteps)\n",
    "        S = Milstein(Z, T, nsteps, r, σ, S0)   # we use the exact scheme from above\n",
    "        option_payoff[i] = np.exp(-r * T) * abs(S[-1]-K)\n",
    "\n",
    "    option_price_MC = option_payoff.mean()\n",
    "    MC_std = option_payoff.std() / np.sqrt(nSim)\n",
    "\n",
    "    lower = option_price_MC - stats.norm.ppf(1-𝛼)*MC_std\n",
    "    upper = option_price_MC + stats.norm.ppf(1-𝛼)*MC_std\n",
    "\n",
    "    return option_price_MC, MC_std, lower, upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GG1eQOD5Rj2A",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1693520804322,
     "user_tz": -60,
     "elapsed": 11915,
     "user": {
      "displayName": "Anthony Li",
      "userId": "16806347570419169042"
     }
    },
    "outputId": "23a43c39-1819-405f-9cd7-d84ae48c61d8",
    "ExecuteTime": {
     "end_time": "2024-07-09T09:52:36.387392Z",
     "start_time": "2024-07-09T09:52:32.822600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MC estimation of the price of the european option is                : 1.176125.\n",
      "The MC standard deviation of the estimation is                          : 0.007334625.\n",
      "The lower bound of the 95% confidence interval of the european option is: 1.16175.\n",
      "The upper bound of the 95% confidence interval of the european option is: 1.190501.\n"
     ]
    }
   ],
   "source": [
    "Milstein_Result = option_price_Milstein(K,α)\n",
    "\n",
    "print('The MC estimation of the price of the european option is                : {:.7g}.'.format(Milstein_Result[0]))\n",
    "print('The MC standard deviation of the estimation is                          : {:.7g}.'.format(Milstein_Result[1]))\n",
    "print('The lower bound of the 95% confidence interval of the european option is: {:.7g}.'.format(Milstein_Result[2]))\n",
    "print('The upper bound of the 95% confidence interval of the european option is: {:.7g}.'.format(Milstein_Result[3]))"
   ]
  }
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