Advanced_Risk_Management_A1_V1.ipynb

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    "# Advanced Risk Management – Assignment 1\n",
    "\n",
    "**Deadline**:  February 20, 13.00h.\n",
    "\n",
    "| |Name |Student number|Email|\n",
    "\n",
    "* |Jason Spano  |: ***** |:  *****  |\n",
    "\n",
    "\n",
    "****Hand in the following via Canvas****:\n",
    "* Your notebook.\n",
    "* A (printed) pdf version of your notebook. In Google Colab, this is most conveniently done in the Chrome browser, and then using the **`File` -> `Print`** menu option; you may have to print in landscape mode to make sure that everything appears in the pdf.\n",
    "\n",
    "****Notes****:\n",
    "* The assignment is part of the examination, so the usual rules regarding plagiarism and fraud apply.\n",
    "* Before submitting your work, click on **`Runtime`-> `Restart and run all ...`** and verify that your notebook produces the desired results and does not error.\n",
    "\n",
    "**Declaration of Originality**: We whose names are given under 1. above declare that these solutions are solely our own work, and that we have not made these solutions available to any other student."
   ]
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   "source": [
    "## Introduction\n",
    "The file `RV_data.xlsx` contains daily data (January 2000 – February 2020, or a sub-period), for a number of international stock market indices, on the open-to-close log-return R (measured as percentage) and the daily realized variance RV (obtained from 5-minute returns).\n",
    "A list of the included indices is given on the website of the data provider, see\n",
    "https://realized.oxford-man.ox.ac.uk/data/assets (in the Excel file, the leading '.' has been removed from the symbol; e.g. `FCHI` instead of `.FCHI`). In this assignment, you are asked to estimate, test and compare two GARCH models for one of the indices in terms of their in-sample fit and their out-of-sample forecast quality."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Question 1: Load and display data\n",
    "First, install and import the relevant libraries:"
   ]
  },
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   "source": [
    "#Modules\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "## pip install arch\n",
    "from arch import arch_model"
   ]
  },
  {
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   "source": [
    "import math\n",
    "from datetime import date\n",
    "from scipy.stats import shapiro, probplot\n",
    "from statsmodels.stats.diagnostic import het_arch\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from arch.univariate import ARCH, GARCH\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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   "source": [
    "#pre-define an output function to later graph the 3 plots more easily\n",
    "def ts_plot(residuals, stan_residuals, lags=50):\n",
    "    \n",
    "    stan_residuals.plot(title='Line graph of GARCH Standardized Residuals', \n",
    "                        figsize=(15, 10))\n",
    "    \n",
    "    ## may require importing matplotlib inline\n",
    "    plt.show()\n",
    "    \n",
    "    ## create subplots with 1 row and 2 columns: \n",
    "    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))\n",
    "    ax[0].set_title('GARCH Standardized Residuals KDE')\n",
    "    ax[1].set_title('GARCH Standardized Resduals Probability Plot')    \n",
    "    \n",
    "    #To show the probability density function of the standardized residuals\n",
    "    stan_residuals.plot(kind='kde', ax=ax[0]) \n",
    "    \n",
    "    #Shows a probability plot of standardized residuals against the quantiles of a normal distribution\n",
    "    probplot(stan_residuals, \n",
    "             dist='norm', \n",
    "             plot=ax[1]) \n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    ## autocorrelation plot\n",
    "    acf = plot_acf(stan_residuals, lags=lags)\n",
    "    acf.suptitle('GARCH Model Standardized Residuals Autocorrelation', \n",
    "                 fontsize=15)\n",
    "    \n",
    "    acf.set_figheight(5)\n",
    "    acf.set_figwidth(15)\n",
    "    plt.show()\n",
    "    \n",
    "    ## repeat for squared standardised residuals \n",
    "    acf2 = plot_acf(residuals, lags=lags)\n",
    "    acf2.suptitle('GARCH Model Squared Standardized Residuals Autocorrelation', \n",
    "                  fontsize=15)\n",
    "    \n",
    "    acf2.set_figheight(5)\n",
    "    acf2.set_figwidth(15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
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   "metadata": {
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    "id": "1m5W_Ks1CNUX"
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   "source": [
    "Next, import the data and obtain the returns for one chosen index. Uncomment and adapt the lines necessary to mount the drive and change the path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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   "source": [
    "## read in the excel .xlsx (if file is in the same location as workbook)\n",
    "df = pd.read_excel('RV_data.xlsx')\n",
    "\n",
    "## set date column to datetime\n",
    "df['Date'] = pd.to_datetime(df['Date'], format='%Y%m%d')\n",
    "\n",
    "## set date to index\n",
    "df = df.set_index(['Date'])\n",
    "\n",
    "## select a ticker symbol \n",
    "sel = df['Symbol']=='AEX'   \n",
    " \n",
    "## loc the Returns column based off the selected ticker    \n",
    "R = df['R'].loc[sel]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Display a line graph of the returns, and display the autocorrelation function of the returns and of the squared returns. Discuss whether you find the \"stylized facts\" mentioned in the textbook and slides of the course."
   ]
  },
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5YuDjBNalKED5yVCTC4OHiWInsAjJ7JWbfStTUZKBXyGUbwMTgQNEZLmIXAYMA44TkYVA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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(y='R', label='Returns')\n",
    "plt.title('Line graph of Daily Log-Returns', fontsize=12)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "tIfVWsWK9-jl",
    "outputId": "6d8c7998-dfcd-46a7-e986-b06dcd840f3a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5nGhq4ZYHOjhtRv+v7Odt2bWXHz27vt8tTn/95DZOn3lUye2MS66S55c51Pcf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\nzCwjnNDNzDLCCd3MLCOc0M3MMuL/A0V7+mL6RCgdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(R,lags=50)\n",
    "plt.title('Autocorrelation function of Log-Returns', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "nup4x3Sw9-jq",
    "outputId": "6a81a8ff-3b3b-4252-8805-0e2e77c92651"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XB0plOQ+1GBwrNm6vW75yU/1AWf10dUDs2ZuVr+stJiBqg6C/v7Kjqv6wVz60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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rsquared = R**2\n",
    "plot_acf(Rsquared, lags=50)\n",
    "plt.title('Autocorrelation function of Squared Log-Returns', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "60C90oALLThc"
   },
   "source": [
    "### Discussion of results:\n",
    "* There are a number of stylized facts for asset returns. One of them can be derived from the first graph, which is volatility clustering. The daily returns do not seem to be random but seem to have clustered values over time. We see that large absolute returns tend to be followed by large absolute returns and vice versa. This is also shown in the third graph where squared daily returns have relatively high positive autocorrelation, espcecially during the first lags.\n",
    "\n",
    "* A second fact is that daily returns have relatively low autocorrelation and no predictability in mean. This is shown in the second graph where autocorrelation values are very low.\n",
    "\n",
    "* Another important stylized fact is that (daily) returns have higher kurtosis than the normal distribution and are negatively skewed. This means they are more peaked and have longer tails and thus are non-normal. This fact however cannot be derived from our graphs.\n",
    "\n",
    "* The mean blur, leverage effect and time-varying correlations are the other three stylized facts but these cannot be derived from our graphs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "63Thc8rvtK0y"
   },
   "source": [
    "## Question 2: Fitting a symmetric GARCH model\n",
    "Estimate and test a GARCH model for the returns, using only data over the sub-period January 2000 – December 2012. For this question, do **not** consider a GJR-GARCH model or any model with an asymmetric NIC (see next question), but focus on standard GARCH($p,q$) models. Display and discuss the estimation output, and test the model for the absence of volatility clustering in the standardized residuals (shocks) $\\hat{z}_t$. If you try out various GARCH models, only report discuss the results on the final model.\n",
    "\n",
    "Note: estimation over a sub-sample using the ARCH package can be done by specifying `last_obs =` in the `.fit` function; see https://arch.readthedocs.io."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 728
    },
    "colab_type": "code",
    "id": "S17lJ0cD9-jy",
    "outputId": "1dfa9569-cb00-4e08-ebe3-ab16b6d9d12c",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      1,   Func. Count:      6,   Neg. LLF: 4725.046087455656\n",
      "Iteration:      2,   Func. Count:     16,   Neg. LLF: 4717.62473123438\n",
      "Iteration:      3,   Func. Count:     24,   Neg. LLF: 4713.185680755121\n",
      "Iteration:      4,   Func. Count:     32,   Neg. LLF: 4712.248333761487\n",
      "Iteration:      5,   Func. Count:     39,   Neg. LLF: 4703.584407995852\n",
      "Iteration:      6,   Func. Count:     46,   Neg. LLF: 4702.092536668209\n",
      "Iteration:      7,   Func. Count:     53,   Neg. LLF: 4698.829004081826\n",
      "Iteration:      8,   Func. Count:     60,   Neg. LLF: 4698.520617138741\n",
      "Iteration:      9,   Func. Count:     67,   Neg. LLF: 4698.389445651215\n",
      "Iteration:     10,   Func. Count:     73,   Neg. LLF: 4698.385062719118\n",
      "Iteration:     11,   Func. Count:     79,   Neg. LLF: 4698.385055875802\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 4698.385055876145\n",
      "            Iterations: 11\n",
      "            Function evaluations: 79\n",
      "            Gradient evaluations: 11\n",
      "                     Constant Mean - GARCH Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:                      R   R-squared:                      -0.002\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                 -0.002\n",
      "Vol Model:                      GARCH   Log-Likelihood:               -4698.39\n",
      "Distribution:                  Normal   AIC:                           9404.77\n",
      "Method:            Maximum Likelihood   BIC:                           9429.19\n",
      "                                        No. Observations:                 3312\n",
      "Date:                Wed, Feb 19 2020   Df Residuals:                     3308\n",
      "Time:                        21:43:34   Df Model:                            4\n",
      "                                  Mean Model                                 \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "mu         4.4148e-03  1.425e-02      0.310      0.757 [-2.352e-02,3.235e-02]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega          0.0136  4.366e-03      3.106  1.894e-03 [5.006e-03,2.212e-02]\n",
      "alpha[1]       0.1055  1.888e-02      5.589  2.289e-08   [6.851e-02,  0.143]\n",
      "beta[1]        0.8878  1.818e-02     48.844      0.000     [  0.852,  0.923]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "### Estimating of the standard symmetric GARCH model\n",
    "\n",
    "## set index to the same index used by the returns series\n",
    "index = R.index\n",
    "\n",
    "## drop any missing values\n",
    "returns = R.dropna()\n",
    "\n",
    "## initialise thE GARCH model instance on the NaN Dropped Returns series, distribution == normal \n",
    "am_2 = arch_model(returns, vol='Garch',p=1,o=0,q=1, dist='Normal')\n",
    "\n",
    "## the last observation date\n",
    "res1 = am_2.fit(last_obs='2012-12-31')\n",
    "\n",
    "## print the summary which includes steps to optimisation value / iterations \n",
    "print(res1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5HSpxwip9-j3"
   },
   "outputs": [],
   "source": [
    "#define parameters\n",
    "resi = res1.resid                  #model residuals\n",
    "hsq = res1.conditional_volatility  #square root of conditional variance\n",
    "sres = resi/hsq                    #standardized residuals\n",
    "Zt = R / hsq                       #standardised returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6f-XiC2-9-j_"
   },
   "outputs": [],
   "source": [
    "model_1_LLF = res1.loglikelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rhTfEHa29-kD"
   },
   "outputs": [],
   "source": [
    "#drops any data out of the chosen sample period\n",
    "model1_zt = Zt.dropna()\n",
    "model1_resi = resi.dropna()\n",
    "model1_hsq = hsq.dropna()\n",
    "model1_sres = sres.dropna()\n",
    "model1_sressq = model1_sres**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### call the graphing function defined at the top of this workbook\n",
    "* called \"ts_plot\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1881
    },
    "colab_type": "code",
    "id": "_xtM-oMQ9-kH",
    "outputId": "20c01bfc-fded-4d20-96c3-573f39d9aceb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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taWy8gGmrDifyZMjfjDMeVpQ0xthZcBS0GZzz2aJrOOfTOOdv55y//ZxzzhGm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PiYQECfaJ40xp4uYOlWeo4N94S08r/yjLtkRawD2OLkaG2IatL2raE77OPdgKqVhya1YM8SmD9J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\nV3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MnXhcs4pmTE7CoybvuCZJkiTlRGMj3H336LY9i2fZwnwu4EnqJ9/LyQ138EZT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\nJEmSJEmSijJ5JEmSJEmSpKL+C5IvKQRGCiX2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K8DPU75qN5tyyDNh7hHwPVf4vBf4yNu5V8+mU8tj4ge0Lge1i6J8GGfG8MMRyI7mQUqP2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\n6nOiQoPb6psAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PJSJi38HCafq+uup2FuUitdlAgnxnc2zV07gZW4wxjMy8PyI+TamStT+bb7vG6W9H+XmD\n0zPzsw3dR3Nzb6Axk+HKQSfrchXtlbUtRMQ0ypOAVcBfNfUe2O+OofUF842UKqWjmneDJQPHg+q4\ncxPlfZ+jRxAHWd6FHUhWnkFp1Oi7ETE7W7+7tUXZGiTuDQy/L7ycUjXunQMdIuLgFtPaTGZ+t4rx\nEZRE9eOUd1YHa3L/uZRE5SU8/F5Zo4FWaweeTg0XdyurKIlP87poPL5B2X+GupF0R4tpDEyn+Zg+\nWO2JVt3vptzUeG+Lflu8l1s5nHKj5nnZ8M50td5HY4tlq55E7krrc6CkUfCJmjS4jwOHRcSr2hj2\neEqSdjLlrmbj5/vAKyKGbBL9XygXB//Q0O0LlIuFlg2aRMQL24hrxKoLuvdTngYO9gOn11JeYH95\nU/cTgN9l5sqqGuWvKHfMGx3f9P1yyhOJR7R6IphtNkUd5ceuZ7bodUD1d+AC92rgSRHx0NPSqlGP\n5guqZWyZ0D2/6ft04IGmqmOvpA1VFaifA48eZLkHErWrgedVydmAVheom4mIXQapNtq8Plrd3Z5G\nOT9saJjejpR3qUZqKSUhGK4cdLIu2y1rrXyasp2Pz8zmp5Q/oyRiew2yje7rcN5byMx7KAnWUVX1\n47biaJrGA1X1zY9SEolW+wWUsnVs07GpVdzLgIMGvlQJ+5FNw0ynobxU2tp+Vcx/zMyvUZ7IDZXg\nnURJBJqPs8+m3NQZqP64gnL8bIx7BqUWQ6Mtyn/1ZOgaWh/fNlG2B5Tq2X8WEXvQ2lXA7hHxzIYY\ntqckpD8ZYhmHcxnwWGBxi7Jw4yDjTK/+Nu7TT6W8y9ao3addVwEvaTrGHE95ANDJsklq4BM1aRCZ\n+d8R8XHgvIh4NiVxWUW5YzhwwT5wYXcScENmfrl5OlULXd+k3OH+UXP/al5ro/y49nsj4oDMvCkz\nb4+IUyi/mTabUp3pNkoDCycCz2SY1tFGK0srcp8cov/d1bp5V0Q8SGm043hKlcnG90TeT/kds89Q\nLsCeRVOLYJl5Y0R8ltJS4IeqaW1HuRA5MDNf02bYjwB+FxFfolQJ+yPl/Yu3U9bbwHtdXwTeRbmL\nfxblAua9lG3b6CLg1Gq7fJdyIdjcmtmlwJuqdfFtykXgSH6o+G3AZRGxiZIU30ep7vpCyu/B/Y5y\nw+B04DsR8VFKq49vp+k3/Fp4DvDPEfFFygX5piq+M4DvZOYt1XA3AHtUZe1aSgMRt0TE1cCZEXFv\nNe4ZlHU6aCuCrWRmf7VdPxKlOfcfU1qJO6hp0E7X5bBlrVmUZtT/ivKD4LtExFMaev8qM1dXZeRf\nq6d7P6IksAdSGq4YSJhHPO9hfIayvt8KvKqdOKqk7iOU96hupjw5+gfg10M8kf8g5YL7woj4AuXd\n01NbDHcRcHpE/Kqa9mvYshxcCvy/iLiK0qrgKylPbQcVEa+jPOn5HuUJ8gGU5GiL42g1/HbAi4Gv\nZeaVLfp/HfiriNi+Oqb+N/B3Ud6zXE15x7h5v2lZ/oF3A5dU+88FwJ9SjhOfb3i/9WOU37v7cUS8\nj3JT4iBgh8z8UGZeEhE/Bb4eEWdQnsD9PeWY01jFdqQ+Stk3Lo+If6Mc3/aglLuf5JatskK5KbSG\n8mPqH6I8XTurGrd5fRwXES+mJOi3D1L9/Z8oNyi+VZX72ZTydElm/qzF8JJGI2vQookfP3X+UJ5e\nXEq5i/sA5YLim8DRVf+B1h3fMcj40yjVAD9TfT+PhlbHGobbqRruc03dn0hp0GN5w/z/AzikYZgr\nGaRFREqLYm21+jhE/81afay69VGa5l9KuQt7HfDKFuO+kXLCX0up4vf8FtMKyjsuiyl3fFdSqmC+\negQxTqVc3P6oWlfrgCWUJtNnNw37eMq7QBso1cZeTEkQz2sa7u3V8t1XrfNjaWr1kZJsLaU0bvAD\nysXmZq3kDbUNKA18fI/y20n3V+vxo5QnjAPDHAH8pop3EaWa6CqGaPWR8i7KR6rhV1fL8NtqmRpb\nkNyOkrwOtJZ5XtV9f8qd+/uBP1TLeRYNLf3xcKuPM4Yqc9X2fW+1Xe+jNDLx5zS1+jgG67KdsvbQ\n9KrlyUE+jXH9BeUJyzrKPnoV8OaRzrtFvHMZpGU+SpXrByjvnw0bB+WJ8FcoidR6ylPM84F9htqH\nKInRkmqcn1DeW2pu9XEGpXn1u6vpvouy769qGuaL1TB3U5q+P4bNWzDdbHkpSdp3Kce09ZSfJPgg\nMG2Q9fXSavynDtL/0Kr/K6rve1CqO98L3Ep57/Y8Nm/1sWX5r/q9grLPbKy27fuAyU3z3JeSHN9T\nbftfAyc29J9FSTzvqbbbD4Ent3OMbo61qd9eVdzLKceFWyjHqMcOVrYoNw+ureL4DeXG2mZlglKt\n9KJqGybVMWaQsnMkpQyur9bfp2k4FjBIK7mtpuXHj5/Wn8gcSaNykjQ+RfkR3Gsz85RuxyJJkuQ7\napIkSZJUMyZqkiRJklQzVn2UJEmSpJrxiZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJ\nmiRJbYqIWyLiuaMc9xkRceNYxyRJGp9M1CRJYyYiroyIeyJi2gjGyYjYf2vG1Q3Ny5WZP87MR3cz\nJklS7zBRkySNiYiYCzwDSODYrgYzjIiY3E43SZK6xURNkjRWXg38HDgPOHmgY/WU7TUN30+JiJ9U\n//+o6vzriFgTEa+our82IpZExN0RsSAi9moY/7ERcWnVb3lEvKPqPi0iPh4Rt1efjw882YuIIyJi\nWUT8Q0TcCXyxVbdq2GMiYlFErI6In0bE41stbEQcGhE/q4a7IyI+GRFTB1uugfk1jH9QtW5WR8Ti\niDi2od95EfGpiPhuRNwXEVdFxH6j2yySpF5koiZJGiuvBr5aff4sIvYYboTMfGb17xMyc0Zmfj0i\nngP8M3ACsCdwK3ABQETsCPwA+B6wF7A/cFk1jXcCTwHmAU8ADgXe1TC7PwF2AfYFTmvVLSKeCJwL\nvA7YFfgcsGCQqpz9wN8BuwGHA0cCbxhsuRpHjIgpwLeB7wO7A38DfDUiGqtGngi8B9gZWAK8r+VK\nlCSNSyZqkqSORcTTKcnOhZl5DfB74M9HOblXAudm5i8zcwPwduDwqmrlMcCdmfkvmbk+M+/LzKsa\nxjs7M1dk5kpKkvOqhuluAt6dmRsyc90g3U4DPpeZV2Vmf2Z+CdhASQA3k5nXZObPM/PBzLyFktQ9\nq81lfAowA/hAZm7MzMuB7wAnNQxzUWb+IjMfpCS/89qctiRpHDBRkySNhZOB72fmqur712io/jhC\ne1GeogGQmWuAu4C9gTmUJHDY8ar/92r4vjIz1zeN09xtX+AtVXXE1RGxuprnXk3jEREHRsR3IuLO\niLgXeD/l6Vo79gKWZuampnj3bvh+Z8P/aymJnSRpgvDFaUlSRyJiOqWaYl/1rhfANGBmRDwBuB/Y\nvmGUPxlmkrdTEqaB6e9AqYZ4G7CUUiVwqPEWV9/3qboNyBbjNHdbCrwvM9upZvgZ4FfASZl5X0S8\nCXhZG+MNxDonIiY1JGv7AL9rc3xJ0jjnEzVJUqdeTHlf62BK9bx5wEHAjynvrS0Cjo+I7avm6k9t\nGn858KiG7+cDfxkR86p3w94PXFVVL/wOsGdEvKlqPGTHiDisYbx3RcSsiNgNOBP4jxEuy+eB10fE\nYVHsEBEvrN6Na7YjcC+wJiIeA/z1MMvV6CrKU7K3RcSUiDgCeBHVu3iSJJmoSZI6dTLwxcz8Q2be\nOfABPkl5b+xjwEZK4vIlyvtWjc4CvlRVNTwhM38A/CPwTeAOYD+qp2iZeR/wPEpScydwE/Dsajr/\nBCwEfgP8Fvhl1a1tmbkQeG0V+z2URjxOGWTwv6e8h3cfJcH7elP/zZaraT4bq2U4GlgFfBp4dWbe\nMJJ4JUnjV2S2qgkiSZIkSeoWn6hJkiRJUs2YqEmSJElSzZioSZIkSVLNmKhJkiRJUs2YqEmSJElS\nzXTtB6932223nDt3brdmL0mSJElddc0116zKzFmt+nUtUZs7dy4LFy7s1uwlSZIkqasi4tbB+ln1\nUZIkSZJqxkRNkiRJkmrGRE2SJEmSasZETZIkSZJqxkRNkiRJkmrGRE2SJEmSasZETZIkSZJqZthE\nLSLOjYgVEXHtIP0jIj4REUsi4jcRccjYh7n19W9KLrt+OZ+47CYuu345/Zuy2yFJkiRJmqDa+cHr\n84BPAl8epP/RwAHV5zDgM9XfntG/KXnVF65i0dLVrNvYz/SpfcybM5OvnHoYfZOi2+FJkiRJmmCG\nfaKWmT8C7h5ikOOAL2fxc2BmROw5VgFuC1feuIJFS1ezdmM/Cazd2M+ipau58sYV3Q5NkiRJ0gQ0\nFu+o7Q0sbfi+rOq2hYg4LSIWRsTClStXjsGsx8bi2+9l3cb+zbqt29jPdbff26WIJEmSJE1k27Qx\nkcw8JzPnZ+b8WbNmbctZD+mxe+3E9Kl9m3WbPrWPg/faqUsRSZIkSZrIxiJRuw2Y0/B9dtWtZxzx\n6N2ZN2cm0b8RchPbV++oHfHo3bsdmiRJkqQJaCwStQXAq6vWH58C/DEz7xiD6W4zfZOCr5x6GLNu\n+jYzl/0v/3bSE21IRJIkSVLXDNvqY0ScDxwB7BYRy4B3A1MAMvOzwMXAC4AlwFrgL7dWsFtT36Rg\n+9U3s/3qmznyoD26HY4kSZKkCWzYRC0zTxqmfwKnj1lEkiRJkjTBbdPGRCRJkiRJwzNRkyRJkqSa\nMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJox\nUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFR\nkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGT\nJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMk\nSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJ\nkqSaaStRi4ijIuLGiFgSEWe06L9PRFwREb+KiN9ExAvGPlRJkiRJmhiGTdQiog/4FHA0cDBwUkQc\n3DTYu4ALM/OJwInAp8c6UEmSJEmaKNp5onYosCQzb87MjcAFwHFNwySwU/X/I4Dbxy5ESZIkSZpY\nJrcxzN7A0obvy4DDmoY5C/h+RPwNsAPw3DGJTpIkSZImoLFqTOQk4LzMnA28APhKRGwx7Yg4LSIW\nRsTClStXjtGsJUmSJGl8aSdRuw2Y0/B9dtWt0anAhQCZ+TNgO2C35gll5jmZOT8z58+aNWt0EUuS\nJEnSONdOonY1cEBEPDIiplIaC1nQNMwfgCMBIuIgSqLmIzNJkiRJGoVhE7XMfBB4I3AJcD2ldcfF\nEXF2RBxbDfYW4LUR8WvgfOCUzMytFbQkSZIkjWftNCZCZl4MXNzU7cyG/68Dnja2oUmSJEnSxDRW\njYlIkiRJksaIiZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJkiTVjImaJEmSJNWM\niZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJ\nmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJkiTVjIma\nJEmSJNWMiZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZok\nSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJ\nkiTVjImaJEmSJNWMiZokSZIk1YyJmiRJkiTVTFuJWkQcFRE3RsSSiDhjkGFOiIjrImJxRHxtbMOU\nJEmSpIlj8nADREQf8CngecAy4OqIWJCZ1zUMcwDwduBpmXlPROy+tQKWJEmSpPGunSdqhwJLMvPm\nzNwIXAAc1zTMa4FPZeY9AJm5YmzDlCRJkqSJo51EbW9gacP3ZVW3RgcCB0bE/0bEzyPiqLEKUJIk\nSZImmmGrPo5gOgcARwCzgWTsmeUAABVxSURBVB9FxJ9m5urGgSLiNOA0gH322WeMZi1JkiRJ40s7\nT9RuA+Y0fJ9ddWu0DFiQmQ9k5v8Bv6MkbpvJzHMyc35mzp81a9ZoY5YkSZKkca2dRO1q4ICIeGRE\nTAVOBBY0DfMtytM0ImI3SlXIm8cwTkmSJEmaMIZN1DLzQeCNwCXA9cCFmbk4Is6OiGOrwS4B7oqI\n64ArgLdm5l1bK2hJkiRJGs/aekctMy8GLm7qdmbD/wm8ufpIkiRJkjrQ1g9eS5IkSZK2HRM1SZIk\nSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJ\nqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmq\nGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZ\nEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkT\nNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1\nSZIkSaoZEzVJkiRJqpm2ErWIOCoiboyIJRFxxhDDvTQiMiLmj12IkiRJkjSxDJuoRUQf8CngaOBg\n4KSIOLjFcDsCfwtcNdZBSpIkSdJE0s4TtUOBJZl5c2ZuBC4Ajmsx3HuBDwLrxzA+SZIkSZpw2knU\n9gaWNnxfVnV7SEQcAszJzO+OYWySJEmSNCF13JhIREwCPgq8pY1hT4uIhRGxcOXKlZ3OWpIkSZLG\npXYStduAOQ3fZ1fdBuwIPA64MiJuAZ4CLGjVoEhmnpOZ8zNz/qxZs0YftSRJkiSNY+0kalcDB0TE\nIyNiKnAisGCgZ2b+MTN3y8y5mTkX+DlwbGYu3CoRS5IkSdI4N2yilpkPAm8ELgGuBy7MzMURcXZE\nHLu1A5QkSZKkiWZyOwNl5sXAxU3dzhxk2CM6D0uSJEmSJq6OGxORJEmSJI0tEzVJkiRJqhkTNUmS\nJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIk\nSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJ\nqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmq\nGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZ\nEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkTNUmSJEmqGRM1SZIkSaoZEzVJkiRJqhkT\nNUmSJEmqmbYStYg4KiJujIglEXFGi/5vjojrIuI3EXFZROw79qFKkiRJ0sQwbKIWEX3Ap4CjgYOB\nkyLi4KbBfgXMz8zHA98APjTWgUqSJEnSRNHOE7VDgSWZeXNmbgQuAI5rHCAzr8jMtdXXnwOzxzZM\nSZIkSZo42knU9gaWNnxfVnUbzKnA/7TqERGnRcTCiFi4cuXK9qOUJEmSpAlkTBsTiYi/AOYDH27V\nPzPPycz5mTl/1qxZYzlrSZIkSRo3JrcxzG3AnIbvs6tum4mI5wLvBJ6VmRvGJjxJkiRJmnjaeaJ2\nNXBARDwyIqYCJwILGgeIiCcCnwOOzcwVYx+mJEmSJE0cwyZqmfkg8EbgEuB64MLMXBwRZ0fEsdVg\nHwZmAP8ZEYsiYsEgk5MkSZIkDaOdqo9k5sXAxU3dzmz4/7ljHJckSZIkTVhj2piIJEmSJKlzJmqS\nJEmSVDMmapIkSZJUMyZqkiRJklQzJmqSJEmSVDMmapIkSZJUMyZqkiRJklQzJmqSJEmSVDMmapIk\nSZJUMyZqkiRJklQzJmqSJEmSVDMmapIkSZJUMyZqkiRJklQzJmqSJEmSVDMmapIkSZJUMyZqkiRJ\nklQzJmqSJEmSVDMmapIkSZJUMyZqkiRJklQzJmqSJEmSVDMmapIkSZJUMyZqkiRJklQzJmqSJEmS\nVDMmapIkSZJUMyZqkiRJklQzJmqSJEmSVDMmapIkSZJUMyZqkiRJklQzk7sdgCRJkqTe1r8pufLG\nFSy+/V4eu9dOHPHo3embFN0Oq6eZqEmSJEkatf5Nyau+cBWLlq5m3cZ+pk/tY96cmXzl1MNM1jpg\n1UdJkiRJo3bljStYtHQ1azf2k8Dajf0sWrqaK29c0e3QepqJWg/r35Rcdv1yPnHZTVx2/XL6N2W3\nQ5IkSVKHeu0ab/Ht97JuY/9m3dZt7Oe62+/tUkTjg1Ufe5SPmEfOutOSJKnuevEa77F77cT0qX2s\nbUjWpk/t4+C9dupiVL3PRK1HNT5ihs0fMR950B5djq5+evGgJ0ndllnvu/jSeHTFDctbXuNdfsNy\njnxMPa/xnnnALObNmcnPfncHOWky20+bwrw5M3nmAbPYtCmpw5GkF6/3TNRG4K41G7h51f3dDgOA\n71+3vOUj5kuvW85O06cA4Pn1Ydfceg+/vPUe1j+4CSgHvWtuvYdzfnQzT9p357an40WLxhtLdPvc\n/SXYtClZtHQ1t9x1P3N33YF5c2YyqQcvgOvsksWtr/G+v3g5M6ZN6VJUwzv9iP35zUWfoX/GHpz+\n+tOYN2cmC2+9p9thPeSwR+7Sc2XVRG0EEniwvx5n6n123p6pkyexoUo8AKZOnsScnbevTYx1cvPK\nNZutK4CND27i5pVrmDdn5rDje2KSJE10mzYl7/+f61myYg0bH9zE1MmT2H/3Gbzj6IM8J46hubvu\n0PIab+6uO3QxquFNmhRMvWsJ3LWEQ/b9h26HMy6YqHXZaBOAeXNmsv/uM1j8h1XQN5lpUyaz/+4z\n2ko6JqJODnqemCRNJL14Y6oXY+5Fi5auZsmKh298bnhwE0tWrGHR0tUc0kbtFLdTe7zG0wATtS7q\nJAGYNCl4x9EH8bq/fQv9M/bgjdUj5nYPeJ0cLHvxQNvJQc8TkzR23B+2jdGu5168MdWLMfeqW+66\nn40taqfcctf9w54P3U7t6/QaT+NHW4laRBwF/CvQB/x7Zn6gqf804MvAk4C7gFdk5i1jG+r402kC\nMNpHzJ0cLMfiQNuNC7VODnqemHpDLyYAvRhzJ9wfto1O1nOn56Vu6GbME20f7qR2Si+WLejeNu6k\nGmEvlstejHlbGDZRi4g+4FPA84BlwNURsSAzr2sY7FTgnszcPyJOBD4IvGJrBDyedJIAdKKTg+VY\nPF3q5EKtkx15tAe9iXhi6kQ3Dra9mAD0Ysydmoj7Qzd0sp67dV7qRLdi7vY+3I1jbSe1U3qxbHV7\nG4+GMY8v7TxROxRYkpk3A0TEBcBxQGOidhxwVvX/N4BPRkSkTeQNqVsvi3ZysOz0QNvJBUS3duSJ\ndmLqRLe2US9WT+3FmDs10faHTo12G3eynjs9L3WjXHYr5m4/yevGsbaT2indbCCjF7fxaPXiuaUX\n1/O2EsPlUhHxMuCozHxN9f1VwGGZ+caGYa6thllWff99Ncyqwaa7y74H5fPece4YLMLYWfTrRQDM\ne8K8lv0f6N/Eugf6W/YbjczkD3evY+2GB4AgJgXTp/Sxzy7TiWhvp7jpumsBOODgx7U93/vWP8ht\nq9dt1tR0BOw9czo7bjd07t7JuAAr79vAqjUbt+g+a8ZUdttx2ladN4xufUHZVr9bcjP0TWWvvfZk\nxrS+trbRWMTcDZnJmg39rH+gn+2m9NV+eTspVwP74boH+sks8Y50PxyNXoy5U726P3RDJ9u4k/Xc\nyXmp03I52uNOt2LuZB/uVLf3pdGcS8fimmc0urmNR1umB4xmPXf73LKtYx6Jnbar508bXPj6p16T\nmfNb9dumZ8aIOA04DWDGnvtty1m3ZbAErR2jKZgRwT67TGfNhqlseKCfaaPYiUeacADMmNbH9Cl9\nW+yIM6b1bdVxAbab0kcEW5xcpk0Zfvz11TwbZcKGB/rbPjGNZn1B2VaPPmDkZbbT9QWjTy5HO24n\nJ9Ox2EajibmTcrVmQ/9D22cg3nUP9LNmw/iOebRxdzJup/vDaG+YdDoubPt11ck27mQ9d3Je6iTm\nTo473Yq5k3240WjKR7eOtQNGM06n1zyj3Ye7tY3HIjEdzXru9rllW8dc4uzs+F5n7az124A5Dd9n\nV91aDbMsIiYDj6A0KrKZzDwHOAdg/vz5+fXXHT6amLtm1ZoN3LR8zRbdN21KXnfJF+ifsQfH/Onz\ne6IKUrdafeykusYvb72HT1x+02bVJqZNnsQpT31krR+Nd7q+Oilbp3/t7QCc+bYFbY8zsJ6JSUA5\ncPZvSl70+L2HXc9jsY1GE3Mn5eq/frmMb1yzbPOOCYc/aleOP2T2uI15tHF3Om6nrRFu2m4m9E1m\n5X0beMT0kTWCNJpxB2zrddXpNu5G9aVOYu7kuNOJTmIeq8a1RnOM79axtls62Ye7tY27VabrcG7Z\nljGPpGzU9QevL3z94P3aSdSuBg6IiEdSErITgT9vGmYBcDLwM+BlwOUT5f20gQKy5uAXQ99kPnH5\nTT3xAuSkScEh++48qoNFp+O+4+iDOvrtuOYdue6/KzLa9dVp2dq0Kdm46/70z9iDX956zzZ5v6XT\nbTTamDspV2PxfkuvxdxNo90fBt5hYPJUYHSNII1m3G7pdBt3cpwerU5i7tb7i53E3Mk+DJ0d47t1\nrO2WTvbhbm3jbpXpXjy3dBJzLx7fR2LYRC0zH4yINwKXUJrnPzczF0fE2cDCzFwAfAH4SkQsAe6m\nJHMTwngvIFvDaC8gOj0p9ppOylYnFwDdOql1mpiOtlx1csHTizH3qm42gtSJ0V4Q9+I27iTmbl0g\ndrqeO0mIOznGd/NY2w3dvIE42m3czRtqvXhuGW3M472RqrYqnGbmxcDFTd3ObPh/PfDysQ2tN4z3\nAlI33bhL3C2dlK1OLgC6dVLr1k2PXryTNxY3LTq5o96Nu/GdXPR064KpkwviXrwx1Ys1Jrq5nju9\nfui1Y20nuvnkc7R68WZLLx53ermGSTtsZqtD472AqHu6VY2oWwfqbt706MU7eZ3ctOgkgejW3fhO\nLnq6VU2s0wviXrwx1Ys1Jrq1nnvxJ3q6pZtPPkerF5Me6L3jTi8mxCNhotah8V5A1D3drEbUa++3\ndEsvxgy9+b5XJxc93aom1osXxN3UaxeInerW9UMvHrdMejSYXi0b7TJR69B4LyDqnl6sRtQJY952\nevV9r240gtStRgw0/lklb2RMejSY8Vw2TNRGYMftJnPgHjNa9nvMnjt2PP0J0Uxmj+tGW6YH/sno\nytYXTn4yP/39Kn63/D4O3GNHnrrfbvTV/AbCRI45t+ER4Cn77cp3fnMH6x7of6jbdlP6eMp+u7Lf\n7kMnEZ2Mu5keOeBdeeOKlonpvesfYL9ZQy/v3F2358obV3Dt7fc+9IO3j9trJ44/ZO8Rl5EeWV0a\nhf0Hua7Ymj7/6vn87Pd3PXTcOny/XbfpsXZitAuuASM9v22t8tGLP60W3WpFf/78+blw4cKuzFuS\nJrL+TcmrvnAVi5auZt3GfqZP7WPenJl85dTDhr1Y62TcXnTZ9cv5m/N/xdqNDyem20/t499OeiJH\nHrTHsOP3b0quvHEF191+LwfvtRNHPHr3cbmeJEmjExHXZOb8lv1M1CRp4ukkgZhIycdES0wlSduW\niZokSaM0kRJTSdK2NVSi5jtqkiQNoW9ScORBe7RV1VGSpLEyqdsBSJIkSZI2Z6ImSZIkSTVjoiZJ\nkiRJNWOiJkmSJEk1Y6ImSZIkSTVjoiZJkiRJNWOiJkmSJEk1Y6ImSZIkSTUTmdmdGUesBG7tysyH\nthuwqttBaFyybGlrsFxpa7FsaWuxbGlr6NVytW9mzmrVo2uJWl1FxMLMnN/tODT+WLa0NViutLVY\ntrS1WLa0NYzHcmXVR0mSJEmqGRM1SZIkSaoZE7UtndPtADRuWba0NViutLVYtrS1WLa0NYy7cuU7\napIkSZJUMz5RkyRJkqSaMVFrEBFHRcSNEbEkIs7odjzqXRFxbkSsiIhrG7rtEhGXRsRN1d+duxmj\nek9EzImIKyLiuohYHBF/W3W3bKkjEbFdRPwiIn5dla33VN0fGRFXVefFr0fE1G7Hqt4TEX0R8auI\n+E713XKljkXELRHx24hYFBELq27j6nxoolaJiD7gU8DRwMHASRFxcHejUg87DziqqdsZwGWZeQBw\nWfVdGokHgbdk5sHAU4DTq+OUZUud2gA8JzOfAMwDjoqIpwAfBD6WmfsD9wCndjFG9a6/Ba5v+G65\n0lh5dmbOa2iWf1ydD03UHnYosCQzb87MjcAFwHFdjkk9KjN/BNzd1Pk44EvV/18CXrxNg1LPy8w7\nMvOX1f/3US589saypQ5lsab6OqX6JPAc4BtVd8uWRiwiZgMvBP69+h5YrrT1jKvzoYnaw/YGljZ8\nX1Z1k8bKHpl5R/X/ncAe3QxGvS0i5gJPBK7CsqUxUFVPWwSsAC4Ffg+szswHq0E8L2o0Pg68DdhU\nfd8Vy5XGRgLfj4hrIuK0qtu4Oh9O7nYA0kSUmRkRNrmqUYmIGcA3gTdl5r3lBnVh2dJoZWY/MC8i\nZgIXAY/pckjqcRFxDLAiM6+JiCO6HY/Gnadn5m0RsTtwaUTc0NhzPJwPfaL2sNuAOQ3fZ1fdpLGy\nPCL2BKj+ruhyPOpBETGFkqR9NTP/q+ps2dKYyczVwBXA4cDMiBi4qet5USP1NODYiLiF8krJc4B/\nxXKlMZCZt1V/V1BuLh3KODsfmqg97GrggKoloqnAicCCLsek8WUBcHL1/8nAf3cxFvWg6t2OLwDX\nZ+ZHG3pZttSRiJhVPUkjIqYDz6O8A3kF8LJqMMuWRiQz356ZszNzLuW66vLMfCWWK3UoInaIiB0H\n/geeD1zLODsf+oPXDSLiBZS61H3AuZn5vi6HpB4VEecDRwC7AcuBdwPfAi4E9gFuBU7IzOYGR6RB\nRcTTgR8Dv+Xh9z3eQXlPzbKlUYuIx1NevO+j3MS9MDPPjohHUZ6E7AL8CviLzNzQvUjVq6qqj3+f\nmcdYrtSpqgxdVH2dDHwtM98XEbsyjs6HJmqSJEmSVDNWfZQkSZKkmjFRkyRJkqSaMVGTJEmSpJox\nUZMkSZKkmjFRkyRJkqSaMVGTJEmSpJoxUZMkSZKkmjFRkyRJkqSa+f9eYeKVCFTrIQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#statistical functions \n",
    "ts_plot(model1_sressq, model1_sres)\n",
    "arch_test = het_arch(model1_sressq, maxlag=50) #Engle's test for heteroskedasticity in the standardised residuals\n",
    "shapiro_test = shapiro(model1_sres)            #Shapiro-Wilk Test / normality of the standardised residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "id": "eAK-6kWb9-kN",
    "outputId": "22cca895-4be7-4742-ec49-958b098ede6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lagrange mulitplier p-value(arch_test): 0.6513430492133535\n",
      "F test p-value(arch_test): 0.6536747230567738\n",
      "Shapiro-Wilks p-value: 2.746271132713778e-16\n"
     ]
    }
   ],
   "source": [
    "#statistical functions for model selection \n",
    "print(f'F test p-value(arch_test): {arch_test[3]}')\n",
    "print(f'Shapiro-Wilks p-value: {shapiro_test[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "sA6fIJvf9-kV",
    "outputId": "1c687bd1-17da-4ecb-88ce-b62dfced9e66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To test for autocorrelation in the squared standardized residuals\n",
      "Ljung-Box p-values: [0.40834956 0.55303347 0.16939238 0.2503503  0.34808593 0.45362937\n",
      " 0.57109735 0.38016129 0.40203635 0.47633427 0.47616371 0.45792826\n",
      " 0.51606453 0.59404677 0.57836617 0.64621028 0.70891031 0.67107516\n",
      " 0.7256253  0.7778491  0.67335845 0.55479731 0.5917276  0.61201258\n",
      " 0.63461227 0.46930594 0.4211486  0.47158091 0.51426021 0.53067893]\n",
      "\n",
      "Box-Pierce p-values: [0.40856178 0.55337193 0.16990109 0.25105024 0.34895693 0.45461732\n",
      " 0.57210374 0.38169886 0.40377693 0.47820052 0.4782826  0.46034418\n",
      " 0.51859319 0.59654045 0.5812195  0.64896529 0.71148815 0.67424004\n",
      " 0.72859132 0.78052766 0.67743535 0.56016039 0.59714346 0.61757861\n",
      " 0.64028305 0.47655153 0.42887137 0.47949465 0.52229087 0.5389538 ]\n"
     ]
    }
   ],
   "source": [
    "#statistical functions / Ljung-Box test of autocorrelation in residuals.\n",
    "print(\"To test for autocorrelation in the squared standardized residuals\")\n",
    "ljung_sressq = acorr_ljungbox(model1_sressq, lags= 30, boxpierce=True)\n",
    "print(f'Ljung-Box p-values: {ljung_sressq[1]}')\n",
    "print(f'\\nBox-Pierce p-values: {ljung_sressq[3]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "ARXmURTW9-kY",
    "outputId": "42de85b1-f427-4ab8-a653-aabdddec8ad9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To test whether autocorrelation in the squared standardized residuals is significantly different from zero\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False]])"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"To test whether autocorrelation in the squared standardized residuals is significantly different from zero\")\n",
    "ljung_res_ar = np.array(ljung_sressq)\n",
    "ljung_res_ar <= 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "QFz0T9j49-kc",
    "outputId": "6e5b9eb5-1ecc-400f-cefb-42726c9648bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ljung-box test: test is:If  p < α  (0.05) we reject the null hypothesis of no auto-correlation between each of its lags at the 95% CL\n",
      "\n",
      "Otherwise expressed:\n",
      "Ho: The data are independently distributed, no autocorrelation.\\ \n",
      "Ha: The data are not independently distributed; they exhibit serial correlation.\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "\"\"\"ljung-box test: test is:\\\n",
    "If  p < α  (0.05) we reject the null hypothesis of no auto-correlation between each of its lags at the 95% CL\n",
    "\n",
    "Otherwise expressed:\n",
    "Ho: The data are independently distributed, no autocorrelation.\\ \n",
    "Ha: The data are not independently distributed; they exhibit serial correlation.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "udtUs87y9-ki",
    "outputId": "9ece9d90-815d-448a-f9d1-160bc5cbe8d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot reject the null of no auto-correlation between series an each of its lags at 95% confidence level    that is, no autocorrelation detected\n"
     ]
    }
   ],
   "source": [
    "if np.any(ljung_res_ar <= 0.05):\n",
    "    print(\"Reject the null(there is auto-correlation)\")\n",
    "else:\n",
    "    print(\"Cannot reject the null of no auto-correlation between series an each of its lags at 95% confidence level\\\n",
    "    that is, no autocorrelation detected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "46tP6dMh9-kl"
   },
   "source": [
    "### Discussion of results:\n",
    "\n",
    "* The model generates significantly positive results for all variables. This means that every unit increase in return increases now variance in the next period by 0.1055 and every unit increase in variance now increases variance in the next period with 0.8878.\n",
    "\n",
    "* The line graph of the standardized residuals (SR) shows their values are divided quite evenly over time and shows little sign of volatility clustering. The autocorrelation functions of the SR and squared SR show that the autocorrelations of both are relatively low, as it should be when there is no volatility clustering. The Ljung-Box and Box-Pierce values show that the autocorrelation of the SRs is not significantly different from zero.\n",
    "\n",
    "* From the KDE graph it seems that the SRs have a distribution with a high peak and thin tails. This could be an indication of a non-normal distribution. With the Shapiro-test we test for normality. The p-value is 2.75e-16, meaning that the distribution of the SRs is significantly different from a normal distribution. In conclusion, given the nature of the asymmetric model; it is expected to invalidate the shapiro-wilk test by default. The final ljung-box test reaffirms the lack of volatility clustering in the squared SRs which can be visually confirmed with the ACF plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EkpnWjK_tK1M"
   },
   "source": [
    "## Question 3: Fitting an asymmetric GARCH model\n",
    "(a) Extend the model you obtained above with one or more asymmetric terms (leading to a GJR-GARCH model), and estimate this second model using data over the same sub-period. Analogously to Question 2, display and discuss the estimation output, and test for volatility clustering in $\\hat{z}_t$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 797
    },
    "colab_type": "code",
    "id": "TT_mZiVDtK1Q",
    "outputId": "1d34fe72-688d-43ed-8ef2-40fe2f5f69bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      1,   Func. Count:      7,   Neg. LLF: 4647.738718984186\n",
      "Iteration:      2,   Func. Count:     18,   Neg. LLF: 4647.625420665267\n",
      "Iteration:      3,   Func. Count:     28,   Neg. LLF: 4647.086398519826\n",
      "Iteration:      4,   Func. Count:     36,   Neg. LLF: 4643.764963964767\n",
      "Iteration:      5,   Func. Count:     44,   Neg. LLF: 4639.577942485128\n",
      "Iteration:      6,   Func. Count:     52,   Neg. LLF: 4637.69902947297\n",
      "Iteration:      7,   Func. Count:     60,   Neg. LLF: 4634.921399966093\n",
      "Iteration:      8,   Func. Count:     67,   Neg. LLF: 4634.1762021243285\n",
      "Iteration:      9,   Func. Count:     74,   Neg. LLF: 4633.075167911845\n",
      "Iteration:     10,   Func. Count:     82,   Neg. LLF: 4632.692908780818\n",
      "Iteration:     11,   Func. Count:     89,   Neg. LLF: 4632.371161583704\n",
      "Iteration:     12,   Func. Count:     96,   Neg. LLF: 4632.363201970469\n",
      "Iteration:     13,   Func. Count:    103,   Neg. LLF: 4632.362810327551\n",
      "Iteration:     14,   Func. Count:    110,   Neg. LLF: 4632.36280752562\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 4632.362807524163\n",
      "            Iterations: 14\n",
      "            Function evaluations: 110\n",
      "            Gradient evaluations: 14\n",
      "                   Constant Mean - GJR-GARCH Model Results                    \n",
      "==============================================================================\n",
      "Dep. Variable:                      R   R-squared:                      -0.000\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                 -0.000\n",
      "Vol Model:                  GJR-GARCH   Log-Likelihood:               -4632.36\n",
      "Distribution:                  Normal   AIC:                           9274.73\n",
      "Method:            Maximum Likelihood   BIC:                           9305.25\n",
      "                                        No. Observations:                 3312\n",
      "Date:                Wed, Feb 19 2020   Df Residuals:                     3307\n",
      "Time:                        21:43:36   Df Model:                            5\n",
      "                                  Mean Model                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|        95.0% Conf. Int.\n",
      "------------------------------------------------------------------------------\n",
      "mu            -0.0315  1.422e-02     -2.214  2.683e-02 [-5.935e-02,-3.612e-03]\n",
      "                               Volatility Model                              \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "omega          0.0133  3.789e-03      3.513  4.426e-04  [5.885e-03,2.074e-02]\n",
      "alpha[1]       0.0000  1.200e-02      0.000      1.000 [-2.352e-02,2.352e-02]\n",
      "gamma[1]       0.1464  2.386e-02      6.136  8.442e-10    [9.965e-02,  0.193]\n",
      "beta[1]        0.9148  1.844e-02     49.602      0.000      [  0.879,  0.951]\n",
      "=============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "#Estimating a GJR-GARCH model:\n",
    "index = R.index\n",
    "returns = R.dropna()   \n",
    "am_2 = arch_model(returns, vol='Garch',p=1,o=1,q=1)\n",
    "res2 = am_2.fit(last_obs='2012-12-31')\n",
    "print(res2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4wPFD1ab9-kv"
   },
   "outputs": [],
   "source": [
    "resi = res2.resid                  #model residuals\n",
    "hsq = res2.conditional_volatility  #square root of conditional variance\n",
    "sres = resi/hsq                    #standardised residuals\n",
    "Zt = R / hsq                       #standardised returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uvtMAHzO9-kz"
   },
   "outputs": [],
   "source": [
    "model2_zt = Zt.dropna()\n",
    "model2_resi = resi.dropna()\n",
    "model2_hsq = hsq.dropna()\n",
    "model2_sres = sres.dropna()\n",
    "model2_sressq = model2_sres**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HiRcewGg9-k4"
   },
   "outputs": [],
   "source": [
    "model_2_LLF = res2.loglikelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1881
    },
    "colab_type": "code",
    "id": "av1uWr5b9-k7",
    "outputId": "050be977-bfe2-4516-811d-82e8f1b90ea6"
   },
   "outputs": [
    {
     "data": {
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IgigQpKQRBEEQBEEQBEEUCFLSCIIgCIIgCIIgCgQpaQRBEARBEARBJIZzjtsW7UNz50De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9UFVu1afettHGVFmDfRy2xgzF1RC86FtGQijSh4VfXZX3PMEtg4L54KeUZvGyyfSXfh4cUb2PR2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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vy02RBaokkyePMd4N3N3jsS/1sfagTNYiaXh5aW0Lu42voKioj7cVtkNNVRJA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\nBRd0nJo1C5qbu3ktYL+aJOUWwyNJfdLaGllWU88xrx6bdClDRkfnkTu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L1ioqXLImSUqe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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zBmqSJEmSVDMGapIkSZJUMwZqkiRJklQzBmqSJEmSVDMGapIkSZJUMwZqkiRJ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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pZgzUJEmSJKlmDNQkSZIkqWYM1CRJkiSpZgzUJEmSJKlmDNQkSZIkqWYM1CRJ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\nkyRJkqSa+f+aGrTtWeIdfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#statistical functions & graphs\n",
    "ts_plot(model2_sressq, model2_sres)\n",
    "arch_test = het_arch(model2_sressq, maxlag=50) #Engle's test for heteroskedasticity in the standardised residuals\n",
    "shapiro_test = shapiro(model2_sres)            #Shapiro-Wilk Test / normality of the standardised residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "id": "Uwkbx4oD9-k_",
    "outputId": "edf2a76a-0de9-4413-97a0-ba2d8dcfd4f2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lagrange mulitplier p-value: 0.9710781952189249\n",
      "F test p-value: 0.9720079462473528\n",
      "Shapiro-Wilks p-value: 1.4875459804913893e-13\n"
     ]
    }
   ],
   "source": [
    "print(f'F test p-value: {arch_test[3]}')\n",
    "print(f'Shapiro-Wilks p-value: {shapiro_test[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "TI6IG56G9-lI",
    "outputId": "ee3f2607-764a-4f9d-d562-fc0e4f8978b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To test for autocorrelation in the squared standardized residuals\n",
      "Ljung-Box p-values: [0.04849861 0.14216079 0.08433642 0.13638225 0.21916103 0.31899269\n",
      " 0.41211116 0.44837966 0.51278186 0.60182615 0.59691295 0.43143857\n",
      " 0.50401464 0.58062748 0.58289495 0.64109876 0.70691065 0.6782168\n",
      " 0.67716657 0.73025363 0.60121888 0.52568593 0.57836163 0.61632807\n",
      " 0.63593046 0.42142028 0.4045218  0.45674032 0.47304784 0.47694348]\n",
      "\n",
      "Box-Pierce p-values: [0.04860047 0.14241226 0.08462186 0.13682305 0.21978021 0.31976352\n",
      " 0.41300139 0.44945697 0.51397736 0.60302113 0.59840186 0.43369084\n",
      " 0.50635798 0.58295949 0.58550847 0.64367804 0.70930974 0.68113238\n",
      " 0.68038576 0.73326501 0.60570221 0.53099591 0.58365179 0.62162728\n",
      " 0.64136829 0.42859145 0.41205244 0.46449163 0.48108763 0.48532995]\n"
     ]
    }
   ],
   "source": [
    "#statistical functions / Ljung-Box test of autocorrelation in residuals.\n",
    "print(\"To test for autocorrelation in the squared standardized residuals\")\n",
    "ljung_sressq = acorr_ljungbox(model2_sressq, lags= 30, boxpierce=True)\n",
    "print(f'Ljung-Box p-values: {ljung_sressq[1]}')\n",
    "print(f'\\nBox-Pierce p-values: {ljung_sressq[3]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "szZi7lB99-lP",
    "outputId": "6bb06366-4bcb-4c5e-b33c-cc52bbb1c60d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To test whether autocorrelation in the squared standardized residuals is significantly different from zero\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False],\n",
       "       [ True, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False],\n",
       "       [ True, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False]])"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"To test whether autocorrelation in the squared standardized residuals is significantly different from zero\")\n",
    "ljung_res_ar = np.array(ljung_sressq)\n",
    "ljung_res_ar <= 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "Tv2RJ9pg9-lS",
    "outputId": "79bff1ba-84e8-40ab-bfaf-8f1e9d7b81e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ljung-box test: test is:If  p < α  (0.05) we reject the null hypothesis of no auto-correlation between each of its lags at the 95% CL\n",
      "\n",
      "Otherwise expressed:\n",
      "Ho: The data are independently distributed, no autocorrelation.\\ \n",
      "Ha: The data are not independently distributed; they exhibit serial correlation.\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "\"\"\"ljung-box test: test is:\\\n",
    "If  p < α  (0.05) we reject the null hypothesis of no auto-correlation between each of its lags at the 95% CL\n",
    "\n",
    "Otherwise expressed:\n",
    "Ho: The data are independently distributed, no autocorrelation.\\ \n",
    "Ha: The data are not independently distributed; they exhibit serial correlation.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "ZRreOHfQ9-lW",
    "outputId": "337ecfb6-70bb-4f13-b93e-3b0e18cf0197"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot reject the null of no auto-correlation at the 95% confidence level; allowing for a small number of significant lags per chance\n"
     ]
    }
   ],
   "source": [
    "if (np.count_nonzero(ljung_res_ar <= 0.05) <=3):\n",
    "    print(\"Cannot reject the null of no auto-correlation at the 95% confidence level; allowing for a small number of significant lags per chance\")\n",
    "elif (np.count_nonzero(ljung_res_ar <= 0.05) >3):\n",
    "    print(\"Reject the null-- there is auto-correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eAwHN8iL9-lZ"
   },
   "source": [
    "### Discussion of results:\n",
    "\n",
    "* The model generates significantly positive results for all variables except alpha. This means that there is not significant effect of return now on variance in the next period and every unit increase in variance now increases variance in the next period by 0.9148. In this model we added gamma that takes care of the leverage effect caused by negative returns. The coefficient tells us that for every unit change increase in negative returns now, variance in the next period increases by 0.1464.\n",
    "\n",
    "* We can conclude that the type of model makes a notable difference in the coefficients. When the leverage effect (gamma) is included, the effect of squared returns (alpha) becomes insignificant. This might be because the squared returns and the dummy of negative returns are correlated so that the dummy will take part of the effect of squared returns and leave it insignificant.\n",
    "\n",
    "* We see that the type of model does not make much difference for the charachteristics of the SRs of both models. The line graph of the SRs still shows their values are divided quite evenly over time and shows little sign of volatility clustering. The autocorrelation function of the SR shows that the autocorrelation is relatively low, as it should be when there is no volatility clustering. The Ljung-Box and Box-Pierce values show that the autocorrelation of the standardized residuals is not significantly different from zero.\n",
    "\n",
    "* From the KDE graph it seems that the standardized rediduals have a  distribution with a high peak and thin tails. With the Shapiro-test we test for normality. The p-value is 1.49e-13, meaning that the distribution of the standardized residuals is significantly different form a normal distribution.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xfyoWylOMMhq"
   },
   "source": [
    "(b) Carry out a likelihood ratio test for the symmetric model of Question 2 (the null hypothesis) against the asymmetric model of Question 3 (the alternative hypothesis). Obtain the p-value of the test from the `scipy.stats` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "ACcEa4ZeNGNy",
    "outputId": "0863514e-a5d4-4d72-cef3-1163d5cfba1d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4698.385055876145 -4632.362807524163\n"
     ]
    }
   ],
   "source": [
    "print(model_1_LLF, model_2_LLF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "EGtbyFQ79-lq",
    "outputId": "4de8c6fc-0810-41e1-fd6c-724e1ca4162f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR test, p value: -132.04, 1.0000\n"
     ]
    }
   ],
   "source": [
    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)\n",
    "\n",
    "\n",
    "## likelihood ratio tests of the two models\n",
    "def lrtest(model_1_LLF, model_2_LLF):\n",
    "    lr = 2 * (model_1_LLF - model_2_LLF)\n",
    "    p = stats.chisqprob(lr, 1)\n",
    "    return lr, p\n",
    "\n",
    "lr, p = lrtest(model_1_LLF, model_2_LLF)\n",
    "\n",
    "## print the LR ratio + associated p_value \n",
    "print('LR test, p value: {:.2f}, {:.4f}'.format(lr, p)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HX07E3_U9-lw"
   },
   "source": [
    "### Discussion of results:\n",
    "* The null hypothesis for a likelihood ratio test is that the smaller/nested (less predictors) model is the “better” model. This is rejected for a p-value smaller than 0.05 (for a 95% confidence interval). \n",
    "* Our p-value shows up as 1.0, so we fail to reject the Null hypothesis. This test thus indicates that the asymmetric GJR-GARCH model does not significantly fit better than the symmetrical GARCH model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O6tQe0TbOf7i"
   },
   "source": [
    "## Question 4: Comparing the out-of-sample fit\n",
    "From the two models estimated above, obtain the out-of-sample (one-step-ahead) conditional variance predictions over the period January 2013 – February 2020. These are based on coefficient estimates from data until 2012, but they use the most recent returns $R_t$ to obtain the predictions for $\\sigma_{t+1}^2$ in the period after 2012 (consult the ARCH package documentation for details).\n",
    "\n",
    "(a) Make a new data-frame containing the two sets of variance predictions, as wel as the realized variance for the same index and over the same sub-period (January 2013 – February 2020). Make a plot of the two predicted volatility series (square root of the predicted variances) in one figure, and discuss similarities and differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "nlxTtsvl9-lz",
    "outputId": "f337e23a-c0d5-4502-ebd8-e53ca0d36589"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 h.1\n",
      "Date                \n",
      "2020-02-04  0.739857\n",
      "2020-02-05  0.901159\n",
      "2020-02-06  0.819145\n",
      "2020-02-07  0.743688\n",
      "2020-02-10  0.674370\n"
     ]
    }
   ],
   "source": [
    "#forecasts model one\n",
    "model1_forecast = res1.forecast(horizon=1)\n",
    "print(model1_forecast.variance.dropna().tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "yvbwnkIr9-l7",
    "outputId": "52363973-d498-4c40-e935-2dd79064cb30"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 h.1\n",
      "Date                \n",
      "2020-02-04  0.578372\n",
      "2020-02-05  0.542393\n",
      "2020-02-06  0.509479\n",
      "2020-02-07  0.481808\n",
      "2020-02-10  0.454058\n"
     ]
    }
   ],
   "source": [
    "model2_forecast = res2.forecast(horizon=1)\n",
    "print(model2_forecast.variance.dropna().tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "SjKFb8QS9-mA",
    "outputId": "8aa7f1fe-8b09-4a3e-999b-dc0e02670c27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1814\n",
      "5125\n"
     ]
    }
   ],
   "source": [
    "trim_model1_forecast = model1_forecast.variance.dropna()\n",
    "print(len(trim_model1_forecast))\n",
    "print(len(model1_forecast.variance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "p5dnaiIe9-mD",
    "outputId": "cff8504b-c800-4e00-fb55-8807360db2f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1814\n",
      "5125\n"
     ]
    }
   ],
   "source": [
    "#repeat for model2\n",
    "trim_model2_forecast = model2_forecast.variance.dropna()\n",
    "print(len(trim_model2_forecast))\n",
    "print(len(model2_forecast.variance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "colab_type": "code",
    "id": "ei2RXfF09-mI",
    "outputId": "d531518a-3c42-4f34-f0be-5a8c52045d8f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model1_forecast</th>\n",
       "      <th>Model2_forecast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>0.743688</td>\n",
       "      <td>0.481808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>0.674370</td>\n",
       "      <td>0.454058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model1_forecast  Model2_forecast\n",
       "Date                                        \n",
       "2020-02-07         0.743688         0.481808\n",
       "2020-02-10         0.674370         0.454058"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenate trimmed series \n",
    "df_forecasts = pd.concat([trim_model1_forecast, trim_model2_forecast],axis=1)\n",
    "df_forecasts.columns = ['Model1_forecast','Model2_forecast']\n",
    "df_forecasts.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "colab_type": "code",
    "id": "bt7gH3Uk9-mO",
    "outputId": "54f03b7a-c520-4518-8c61-8d8bd84f411e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model1_forecast</th>\n",
       "      <th>Model2_forecast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.357123</td>\n",
       "      <td>0.344782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.331421</td>\n",
       "      <td>0.328710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model1_forecast  Model2_forecast\n",
       "Date                                        \n",
       "2013-01-02         0.357123         0.344782\n",
       "2013-01-03         0.331421         0.328710"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fcast = df_forecasts.drop(df_forecasts.index[:2])\n",
    "df_fcast.head(2)\n",
    "#check if start of series match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "2Ca7CYUq9-mS",
    "outputId": "5ee20459-b2aa-4d35-c510-0644b6ec9cb6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-01-02    0.382839\n",
       "2013-01-03    0.197959\n",
       "Name: RV, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_excel = pd.read_excel('RV_data.xlsx')\n",
    "df_excel['Date'] = pd.to_datetime(df_excel['Date'], format='%Y%m%d')\n",
    "df_excel = df_excel.set_index(['Date'])\n",
    "sel = df['Symbol']=='AEX'   # Boolean array to select index;\n",
    "      \n",
    "## RV = realised variance \n",
    "RV_series = df['RV'].loc[sel]\n",
    "RV_trim = RV_series.loc['2013-1-1':]\n",
    "RV_trim.head(2) #match the length of the RV series with that of the forecast period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 233
    },
    "colab_type": "code",
    "id": "sy9bZsqh9-mX",
    "outputId": "b54bc2f2-de2e-4cea-d3a2-2985fe61a70b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model1_forecast</th>\n",
       "      <th>Model2_forecast</th>\n",
       "      <th>RV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.357123</td>\n",
       "      <td>0.344782</td>\n",
       "      <td>0.382839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.331421</td>\n",
       "      <td>0.328710</td>\n",
       "      <td>0.197959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.317906</td>\n",
       "      <td>0.314007</td>\n",
       "      <td>0.147157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-07</th>\n",
       "      <td>0.300430</td>\n",
       "      <td>0.304961</td>\n",
       "      <td>0.164677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-08</th>\n",
       "      <td>0.288171</td>\n",
       "      <td>0.300533</td>\n",
       "      <td>0.165594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model1_forecast  Model2_forecast        RV\n",
       "Date                                                  \n",
       "2013-01-02         0.357123         0.344782  0.382839\n",
       "2013-01-03         0.331421         0.328710  0.197959\n",
       "2013-01-04         0.317906         0.314007  0.147157\n",
       "2013-01-07         0.300430         0.304961  0.164677\n",
       "2013-01-08         0.288171         0.300533  0.165594"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final DF comparison of 2 models forecasted variance Vs. Realised Variance \n",
    "df_forecast = pd.concat([df_fcast,RV_trim],axis=1)\n",
    "df_forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOT SQUARE ROOT OF THE PREDICTED VARIANCES OVER TIME "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "r6qKOJqZ9-mb",
    "outputId": "b41a1e41-d2d1-44d9-e2a9-b670a2b0573c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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nFiYlRDvnb0BVokUkAYYZoi8F/q+Z3QE8B3inmT2z1Qmdc1c45y51zl06Nze3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\nUzscfzvwOOfcg4A3Alf0Me7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wn4u23NMQmKu33Ap0bucwAg4tTA6mEp3LUWDZh+gedtCz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\niIiIiBaliI6IiIiIaNF/AbgqvYfrcITWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqrt_m1fc = np.sqrt(df_forecast['Model1_forecast'])\n",
    "sqrt_m2fc = np.sqrt(df_forecast['Model2_forecast'])\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(sqrt_m1fc,'r',markersize=6, linewidth=1)\n",
    "plt.plot(sqrt_m2fc,'b',markersize=3, linewidth=1)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Volatility\")\n",
    "plt.title(\"Square root of the predicted variances\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gz64LJffX3Mp"
   },
   "source": [
    "### Discussion of results:\n",
    "The two models are quite similiar - which is somewhat expected given the degree of similarities in the GARCH specifications.\n",
    "However, model_one tends to have more instances of deviating significantly from model_two - at the highs particularly.\n",
    "For example, in early 2013, mid-2016 and in particular, early 2017. \n",
    "\n",
    "Moreover, using salient examples of: early 2015 and mid-2016, model_one tends to consistently estimate volatiity above model_two\n",
    "in mid-2016, model_one's predicted volatiltiy stays above model_2 for a significant period of time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8ll_h2p6SAsT"
   },
   "source": [
    "(b) Calculate the mean squared error of the two variance predictions (using the realized variance as the true $\\sigma_{t+1}^2$), and discuss the result; which one of the two models gives the best predictions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "AY0kgGeAT4YQ",
    "outputId": "602dff98-044e-4c3c-993c-5a7b76a27953"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5371004101841075"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model 1 - MSE \n",
    "y_true = df_forecast['Model1_forecast']\n",
    "y_pred = df_forecast['RV']\n",
    "model1_MSE = mean_squared_error(y_true,y_pred)\n",
    "model1_MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "MVbAHWnk9-mw",
    "outputId": "6600b647-5318-48fe-d8e2-3c27524eada6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5168093373433846"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model 2\n",
    "y_true = df_forecast['Model2_forecast']\n",
    "y_pred = df_forecast['RV']\n",
    "model2_MSE = mean_squared_error(y_true,y_pred)\n",
    "model2_MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "kw4xmpai9-m1",
    "outputId": "69c774e8-d283-4d13-ddb9-75bc34b13f00"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020291072840722846"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE_difference = model1_MSE - model2_MSE\n",
    "MSE_difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bHynI0j1R9Ua"
   },
   "source": [
    "### Discussion of results:\n",
    "* Even though the difference is relatively small, the GJR-GARCH model shows a lower mean squared error compared to the standard GARCH model. This means that the GJR-GARCH model should have better predictive power; also iterated as this model has a less-biased estimate of the error variance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O9b613jGT62n"
   },
   "source": [
    "(c) As discussed in Sections 4.6.3 and 5.7 of the book, variance forecasts $\\hat{\\sigma}_{t+1}^2$ can be evaluated in the linear regression\n",
    "\n",
    "$RV_{t+1} = b_0 + b_1\\hat{\\sigma}_{t+1}^2 + e_{t+1}$,\n",
    "\n",
    "by testing the two restrictions $b_0=0, b_1=1$ (separately and jointly).\n",
    "Estimate this regression twice: first, using the symmetric GARCH model prediction (Question 2), and next, using the asymmetric GARCH model prediction (Question 3) as explanatory variable. Report the estimation results and the outcome of the $F$-test for $b_0=0, b_1=1$ (use heteroskedasticity-robust standard errors). What do you conclude?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "colab_type": "code",
    "id": "IV7OhZXh9-m8",
    "outputId": "09114419-31e9-4dcc-c23b-5e68243936f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     RV   R-squared:                       0.274\n",
      "Model:                            OLS   Adj. R-squared:                  0.273\n",
      "Method:                 Least Squares   F-statistic:                     681.9\n",
      "Date:                Wed, 19 Feb 2020   Prob (F-statistic):          7.70e-128\n",
      "Time:                        21:43:45   Log-Likelihood:                -2003.6\n",
      "No. Observations:                1812   AIC:                             4011.\n",
      "Df Residuals:                    1810   BIC:                             4022.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const              -0.0665      0.030     -2.231      0.026      -0.125      -0.008\n",
      "Model1_forecast     1.0272      0.039     26.114      0.000       0.950       1.104\n",
      "==============================================================================\n",
      "Omnibus:                     3649.579   Durbin-Watson:                   1.605\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         10266795.766\n",
      "Skew:                          15.883   Prob(JB):                         0.00\n",
      "Kurtosis:                     370.389   Cond. No.                         3.30\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "y_true = df_forecast['Model1_forecast']\n",
    "y_pred = df_forecast['RV']        \n",
    "y_true = sm.add_constant(y_true)\n",
    "m1_reg1 = sm.OLS(y_pred, y_true)\n",
    "mod_1_results = m1_reg1.fit(covtype='HAC')\n",
    "print(mod_1_results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "wIZaDuQr9-m-",
    "outputId": "f95276d2-d958-419f-dc95-d5f6c2d6f22a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[0.47694002]]), p=0.4899001889613781, df_denom=1.81e+03, df_num=1>\n"
     ]
    }
   ],
   "source": [
    "#note: the F-test will be equivalent to the squared t-stat (F-test relative to the number of K in the model)\n",
    "print(mod_1_results.f_test(\"Model1_forecast=1\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "86hAJ4cP9-nA",
    "outputId": "3183cd32-bdea-4e46-a6e1-00bab0d61851"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[4.97905155]]), p=0.025778084735093118, df_denom=1.81e+03, df_num=1>\n"
     ]
    }
   ],
   "source": [
    "print(mod_1_results.f_test(\"const=0\"))\n",
    "#note: this test is equivalent to squared t-stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "EDE8LLa79-nC",
    "outputId": "93d2a274-ac50-43be-850b-e273b8f067fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[4.42250529]]), p=0.012134112987085034, df_denom=1.81e+03, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "print(mod_1_results.f_test(\"Model1_forecast=1, const=0\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "colab_type": "code",
    "id": "gzRuwx-39-nH",
    "outputId": "4bcb9179-8afb-4f2f-fc39-8643481b6fcb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     RV   R-squared:                       0.302\n",
      "Model:                            OLS   Adj. R-squared:                  0.301\n",
      "Method:                 Least Squares   F-statistic:                     782.5\n",
      "Date:                Wed, 19 Feb 2020   Prob (F-statistic):          2.09e-143\n",
      "Time:                        21:43:46   Log-Likelihood:                -1967.7\n",
      "No. Observations:                1812   AIC:                             3939.\n",
      "Df Residuals:                    1810   BIC:                             3950.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const              -0.0175      0.027     -0.650      0.516      -0.070       0.035\n",
      "Model2_forecast     0.9490      0.034     27.973      0.000       0.882       1.016\n",
      "==============================================================================\n",
      "Omnibus:                     3690.166   Durbin-Watson:                   1.616\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         10942883.012\n",
      "Skew:                          16.280   Prob(JB):                         0.00\n",
      "Kurtosis:                     382.314   Cond. No.                         2.94\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "#model 2: \n",
    "y_true = df_forecast['Model2_forecast']\n",
    "y_pred = df_forecast['RV']\n",
    "y_true = sm.add_constant(y_true)\n",
    "reg2 = sm.OLS(y_pred, y_true)\n",
    "mod_2_results = reg2.fit(covtype='HAC')\n",
    "print(mod_2_results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "_SBUgqa99-nK",
    "outputId": "66962986-8729-48a9-bf20-2f75d827cdb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[2.25806108]]), p=0.1330948307736489, df_denom=1.81e+03, df_num=1>\n"
     ]
    }
   ],
   "source": [
    "print(mod_2_results.f_test(\"Model2_forecast=1\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "inPGsj6b9-nN",
    "outputId": "12268ed5-3310-4946-e294-b41a1de05903"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[0.42229122]]), p=0.515878442951142, df_denom=1.81e+03, df_num=1>\n"
     ]
    }
   ],
   "source": [
    "print(mod_2_results.f_test(\"const=0\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "3wnMceo09-nQ",
    "outputId": "ae297330-c0a8-424b-be25-975246c97207"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[5.36697421]]), p=0.004742822718393386, df_denom=1.81e+03, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "print(mod_2_results.f_test(\"Model2_forecast=1, const=0\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_cV359BPWdeY"
   },
   "source": [
    "## Discussion of results:\n",
    "* For model 1, the the predicted variance is significantly equal to 1, though the constant is significantly different from 0. For model 2 both the predicted variance and constant are significantly equal to 1 and 0, respectively. Both models are, surprisingly for model 2, jointly significantly unequal to 1 for the forecast coefficient and 0 for the constant. This makes it difficult to decide which model would have better predictive power, but as model two has both coefficients seperately significantly equal to 0 and 1 respectively, we would prefer model 2 over model 1 as a predictive model of future variance."
   ]
  }
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