The_ARCH_Model_.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "name": "The ARCH Model .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RegaipKURT/Machine-Learning-Python/blob/master/The_ARCH_Model_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jiopaOg_yzmP",
        "colab_type": "text"
      },
      "source": [
        "## Importing the relevant packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCJy-HlcyzmR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "549d0165-2f77-4b67-8495-bcd44498b0da"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.graphics.tsaplots as sgt\n",
        "import statsmodels.tsa.stattools as sts\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from scipy.stats.distributions import chi2 \n",
        "from math import sqrt\n",
        "import seaborn as sns\n",
        "sns.set()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-pLwr9JCyzmX",
        "colab_type": "text"
      },
      "source": [
        "## Importing the Data and Pre-processing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hh5Jh9wRyzmY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "raw_csv_data = pd.read_csv(\"Index2018.csv\") \n",
        "df_comp=raw_csv_data.copy()\n",
        "df_comp.date = pd.to_datetime(df_comp.date, dayfirst = True)\n",
        "df_comp.set_index(\"date\", inplace=True)\n",
        "df_comp=df_comp.asfreq('b')\n",
        "df_comp=df_comp.fillna(method='ffill')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pESVhPoOyzmd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_comp['market_value']=df_comp.ftse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JhjFgA3yzmh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del df_comp['spx']\n",
        "del df_comp['dax']\n",
        "del df_comp['ftse']\n",
        "del df_comp['nikkei']\n",
        "size = int(len(df_comp)*0.8)\n",
        "df, df_test = df_comp.iloc[:size], df_comp.iloc[size:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUdrgx8qyzml",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmOYj-84yzmp",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxeYJQZcyzmq",
        "colab_type": "text"
      },
      "source": [
        "## The LLR Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASqvQHyEyzmr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def LLR_test(mod_1, mod_2, DF = 1):\n",
        "    L1 = mod_1.fit(start_ar_lags = 11).llf\n",
        "    L2 = mod_2.fit(start_ar_lags = 11).llf\n",
        "    LR = (2*(L2-L1))    \n",
        "    p = chi2.sf(LR, DF).round(3)\n",
        "    return p"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IbDw-O-kyzmw",
        "colab_type": "text"
      },
      "source": [
        "## Creating Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "5iYEA_mkyzmx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['returns'] = df.market_value.pct_change(1)*100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9a8E_N2yzm0",
        "colab_type": "text"
      },
      "source": [
        "## Creating Squared Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-OejRluyzm1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['sq_returns'] = df.returns.mul(df.returns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXeceCwLyzm5",
        "colab_type": "text"
      },
      "source": [
        "## Returns vs Squared Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBzovdWayzm6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "ffe7dd1f-fe19-488a-900f-56069b649f50"
      },
      "source": [
        "df.returns.plot(figsize=(20,5))\n",
        "plt.title(\"Returns\", size = 24)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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GgSMiIiIiIqIxwiUIkS4CEY0yDBwREREREREREZEqBo6IiIiIiIiIiEgVA0dERERERERE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\nwAH09fVJf9fW1uLZZ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            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8ImxZEryzm_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "3b9e11ba-7c4b-4d9f-baf7-8eff31a7f4cc"
      },
      "source": [
        "df.sq_returns.plot(figsize=(20,5))\n",
        "plt.title(\"Volatility\", size = 24)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xacq0gqAZoInmNcUsmw/oMbA7Ap8DQmVzptsz7xzSE0sKU7a/51aU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\nuP8PYJP5EY+cOWoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4vqDuYryznE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "938ad9e8-e38b-469d-b0f4-351abbf342aa"
      },
      "source": [
        "sgt.plot_pacf(df.returns[1:], lags = 40, alpha = 0.05, zero = False , method = ('ols'))\n",
        "plt.title(\"PACF of Returns\", size = 20)\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lJUkVFhERdZIkCaKqqgpnzpxBamoqACA1NRVnzpxBdXW1Ubn9+/cjPT0dgiBAqVQiKSkJ\nX375pWH5Bx98gMcffxwxMTFShEVERHaQJEFotVpERERALm8Z6y+XyxEeHg6tVmtSTq1WG16rVCqU\nlZUBAM6ePYvDhw/j2WeflSIkIiKyk1uMYmpqasLbb7+NlStXGpJMZ4SGBtpUPizMuQ+q+foqrNqu\ns+OyFuOyDeOyDeOyjTPikiRBqFQqlJeXQ6fTQS6XQ6fToaKiAiqVyqScRqNBQkICgDs1isrKSly6\ndAlz584FANTW1kIURdTX12PZsmVWx1FVVW/1z/CFhQWhstK5U100NjYDgMXtuiIuazAu2zAu2zAu\n20gVlyDILN5YS5IgQkNDER8fj5ycHKSlpSEnJwfx8fFQKpVG5VJSUpCdnY2RI0eipqYG+fn52LZt\nG9RqNY4ePWoot379ety8eRMLFiyQIjwiIuoEyUYxvfPOO9i6dStGjRqFrVu3YunSpQCAOXPm4NSp\nUwCAtLQ0REVFYeTIkZg8eTLmz5+P6OhoqUIgIiIJSdYH0adPH7PPNXz44YeGf8vlckPisOTll1+W\nKiwiIuokPklNRERmMUEQEZFZTBBERGQWEwQREZnFBEFERGa5xZPURORYer2Ik8VVqDqhQWigLxJi\nQ/mDVdQhJgiiLk6vF7Em6wSKNbVobNLB95efvOWvGlJH2MRE1MWdLK5CsaYWDU06iAAamnQo1tTi\nZHGVq0MjN8cEQdTFXSqvQ2OTzui9xiYdSsvdb44hci9MEERd3D0RQfD1MZ4l2ddHjugI95yllNwH\nEwRRF5cQG4pYdTCgbwJEEX6/9EEkxIYayuj1Ik6cu4q9f7uAE+euWj0rMnVt7KSmLqd1xM6l8jrc\nExHk9SN2BEGG16cMwuxXF0EeEIYXZj9jtE8c2YnNY+HZmCCoS+GIHfMEQQbZjctQNJVhUN9eRsva\ndmIDxp3Yd5e1BY+F52MTE3UpHLFjO0d1YvNYeD7WIH6h04uGOyhH0Yst7bo3G5rbLVNde9viclfx\nlLjOX7luchxbLkzX0S86xGVxuQO9KKKpWW8SV4QyAL4+Ahqa9Ib3fH0EhCsD7PoOthyLzuwvvV7E\nTyXVuFxRj6jwQPSPUUpeM3HH4wiYxuXnI4fcAbUyJohfNDTpcKnMscP+bje2/LFY2k7IbR1qam46\nNI7O8JS4/Hzk8FEIaGq+c7HzUQjwVcgdfnwtxeUObjfqoFCYnn9B/j6IUAbgouYaICjg6yNHhDIA\nQf4+du0zW46FrftLrxex/Ztz0FbdRFOzHj4KAarQAEx+vK+kScIdjyNgGtc9vYMQ4Cf95ZxNTNSl\nxKqCoQoNgKhrgiiKhgtHrCrY1aG5LUGQYfLjfdF4uQDNFd9j7L/ESHKhdeSxKNbWGpIDADQ166Gt\nuoliba3d66Y7mCCoS3HUxa6rEwQZ9HWX0Fx5HH0je0iyvxx5LCqu3TKqmQAtSaLi2i271013MEFQ\nl+OIix11jqOORXjPbvBRGF++fBQCwnt2k2T91IIJgog8DpsSnYMJgog8DpsSnYOjmIjII7U2X+nr\nLqFv5BxXh+MUer2IYm0tas9VIbibArGqYIcmRckSxIULF7Bw4ULU1NQgJCQEmZmZiImJMSqj0+mw\nfPlyHDp0CDKZDHPnzkV6ejoAYOPGjdi/fz8EQYCPjw9ee+01DB8+XKrwvFrrSVVx7RbCe3Zz+ElF\nRNKzNLTXUSRLEEuWLMG0adOQlpaGPXv2YPHixfj444+Nyuzbtw+XLl1CXl4eampqMH78ePzmN79B\nVFQUEhIS8Pzzz6Nbt244e/YsZsyYgcOHD8Pf31+qEL2Ss8aLE5FjWRraG6N2TN+LJH0QVVVVOHPm\nDFJTUwEAqampOHPmDKqrq43K7d+/H+np6RAEAUqlEklJSfjyyy8BAMOHD0e3bi0jEOLi4iCKImpq\naqQIz6txvDhR1+CKob2SJAitVouIiAjI5S1zzsvlcoSHh0Or1ZqUU6vVhtcqlQplZWUm6/v8889x\nzz33oHfv3lKE59U4Xpyoa3DF0F6366T+9ttvsW7dOnz00Uc2fzY0NNCm8mFhd34wpbr2NkJuO3Yu\nJoWiJYGGhARYLNfRclvERoXg25/L0dgmSfgqBMRGhdi8HSnjkpK5uKzd147kbvuro33iqH3myPPe\nGcfZXY7jkOBu+PF8Fc6VXgVkCvj98qNPQ+J7Iyi4G5TB0jfHS5IgVCoVysvLodPpIJfLodPpUFFR\nAZVKZVJOo9EgISEBgGmN4vjx43jjjTewadMmxMbG2hxHVVW91T90EhYWhMrKO/PB3GxodvicK83N\nLQnI0nZCQgIkjSM82A+9Q43n2ekdGoDwYD+btiN1XFJpLy5r9rUjueP+am7WQaGQtxuXo/aZI897\nRx9ndzuOE357H1as3g5FQC+kPpWOWFUwamtvoS5AAV1Dk83rEwSZxRtrSZqYQkNDER8fj5ycHABA\nTk4O4uPjoVQqjcqlpKQgOzsber0e1dXVyM/Px6hRowAAJ0+exGuvvYb33nsP/fv3lyIsAseLE3Ul\nhqG9VT86ZZYAyZqY3nnnHSxcuBCbNm1CcHAwMjMzAQBz5szBH/7wBzz44INIS0vDjz/+iJEjRwIA\n5s+fj+joaADA0qVLcfv2bSxevNiwztWrVyMuLk6qEL2WN44XJyL7SZYg+vTpg+zsbJP3P/zwQ8O/\n5XI5li5davbzO3fulCoUIiKSAKfaICIis9xuFBMReQ9nTx1BtmGCIJfhFCDejU/5uz8mCHIJXhzI\n0lP+fSN7uDg6y7zl5oYJglzCky8OZKyzF0tLT/m78zngTTc3TBDkEp56cSBj9lwsW6eOaHseeMKv\nwnnTzQ1HMXUBer2Ic1euo+h0Gc5duW710+Su5K0/GemJx8oSeyaD9NRfhfOm+c1Yg/Bwnlrdbb04\ntJ0CxBMuDvbw1GNliT01wdan/FeszoQioBfG/jJ1hLvvC0+t+XQGaxAezlOn8/bGKUA89VhZYm9N\n0NlTR0jBU2s+ncEE4eE8ubrbenForjzuMRcHe3jysWqPN10sW3nTzQ0ThIfz1rZ8T9QVj5U3XSzb\n8pabGyYID+eNd3CeqqseK2+5WHojJggP5613cJ6Ix4o8DRNEF8A7OM/BY0WehAmCiIjMYoIgIiKz\n+KAcdZq3TFhG5K2YIKhTuuJTwURkjE1M1Cld8algIjLGBEGd0hWfCiYiY0wQ1Cld8algIjLGPgjq\nFE+ejdVdO9fdNS7yXpIliAsXLmDhwoWoqalBSEgIMjMzERMTY1RGp9Nh+fLlOHToEGQyGebOnYv0\n9PQOl5H7aTtVs+AfiqfSJ3vEBc1dO9fdNS7ybpI1MS1ZsgTTpk1Dbm4upk2bhsWLF5uU2bdvHy5d\nuoS8vDxkZWVh/fr1uHz5cofLyD154lPB7tq57q5xkXeTiaJo909aVVVVYdSoUTh69Cjkcjl0Oh2G\nDRuGvLw8KJVKQ7m5c+di4sSJSElJAQBkZGRArVZj9uzZFpdZa8VHR3CttsGqsj6+cjQ16gyvdaKI\nhjavHaHkwv8DAMTc16/dMgqFgOa7On+lWLc127akvbjsXa+z47p+oxG1NxpNygd390WP7r6disGW\nuNqLzRlxlVz4f5DJZLg35ldWx2XLutv7rDXnpqW4OrtdKdh6HJ3B3P7y85VDLrP9Bq1nsB/+9Pyv\n210uSROTVqtFREQE5HI5AEAulyM8PBxardYoQWi1WqjVasNrlUqFsrKyDpdZS+Ejh4+v3OrybcvK\nmvXQKUScP/cPAECfvnFmP2PP8r6/ut9iPJY+29F2O1q3peWu+k6uiKubnxx1N4G2t0UyWcv7ijad\n7vYci858Z2fE5chzxNJn7dluR9t25N+Uq87dzmxXIReMzhNrKXwsXy+7VCf17DHxVv/Gb1hYECor\n6wyvbzY041JZHZbmrQEApM8aY/Zz9i63ZGneGigUcrOftWe9luj1Ilb8PRuCfygG933UbD+Cpbg6\nYu/+sPTZzsRlbVu/pW17alwAEBISgJqam1bHZcu67WEpLkedQ444joD1f1OdjQsw3V/39A5CgJ/t\nl/OOmoUlSRAqlQrl5eXQ6XSGJqaKigqoVCqTchqNBgkJCQCMaw2WlpFjtF6UfKMSAUGBfUUlXb5j\ntLVz3d1GC7lrXGSbrvY3JUkndWhoKOLj45GTkwMAyMnJQXx8vFHzEgCkpKQgOzsber0e1dXVyM/P\nx6hRozpcRo7R2jEqk/tAJpN5TceoIMjQN7IH/mVAb7fqXHfXuMh69v5N6fUihKB7oAgbjHNXrlvd\nIuIokjUxvfPOO1i4cCE2bdqE4OBgZGZmAgDmzJmDP/zhD3jwwQeRlpaGH3/8ESNHjgQAzJ8/H9HR\n0QBgcRk5hqWnoftG9nBRVESey56/KXesfUiWIPr06YPs7GyT9z/88EPDv+VyOZYuXWr285aWkWO0\nPg3d9oTm09BEnWfP31Tb2gdgPNTZVTdsnGrDi7U+Dd06ZUZX+Y1kIlex52/KHec361KjmMg27Bgl\nkpY9f1PuWKNngvByrR2j7HMgkkZn/6Zaax93D3V2ZY2eCYKIyA24Y42eCYLcUutwP8E/FOeuXHf5\nHwqRM7hbjZ4JgtyOOw73I/JGHMVEFhnu5EMHOu3BHW99gI/I3TBBULva3skLvYZgX1EJtn9zzuFJ\nwh2H+xF5IyYIaper7uT5c6bUlbnbdBqWMEFQu1x1J88H+KiralsrV4QPdVqtvLPYSU3tctWDO9YM\n9zP0jQT04ign8hjuOJ2GJUwQ1C5XPrhjabgfRzmRp/K0CTKZIKhdbe/ka283I9hfYdOduqOeZfC0\nuzCiVu44nYYlTBBkUeudvK2/RObIu3xPuwsjauWO02lYwgRBDuHIu3xPuwsjz+OoPi53nE7DEo5i\nIodw5AgojnIy5UlDJ92do5//8aRfDmQNghzCkXf59vaNdDXstJcW+7juYA2CHMLRd/mtd2GJQ6Pd\n6i6MU5N4Pj7JfwdrEOQQntbWKgVX3cmz015a7OO6gzUIchhPamuVAqcm6RrYx3UHaxBEEnHVnbyn\nDZ10d+zjuoMJgkgi7jw1Cdmms8//dDV2J4hbt27hzTffxE8//QS5XI4FCxZgxIgRZstu374dH374\nIURRxGOPPYa33noLgiAgPz8fmzZtQmNjI0RRxFNPPYXnn3/e3tCInMpdpyYh6iy7E8SWLVsQGBiI\nr776CiUlJZg+fTry8vLQvXt3o3KlpaXYsGEDPv/8c4SEhGDOnDnYu3cvxo8fj7CwMLz//vuIiIhA\nXV0dJk6ciISEBDz00EP2hkfkNGyaoK7G7k7qAwcOYMqUKQCAmJgYDBgwAIWFhSblcnNzkZSUBKVS\nCUEQkJ6ejv379wMABg4ciIiICABAUFAQ+vTpgytXrtgbGpHTuevwW6LOsLsGodFoEBkZaXitUqlQ\nVlZmUk6r1UKtVhteq9VqaLVak3Lnz5/HiRMnsHTpUptjCQ0NtKl8WFiQ4d/VtbcRclsHhUIOAAgJ\nCTD7GXuXW2Lps/asVyqu3LYljohLimPh7LisJfW5KRV3/ZvyhPM+KLgblMH+km+jwwQxYcIEaDQa\ns8uKiookDaaiogLz5s3DkiVLDDUKW1RV1Vv9YFJYWBAqK+sMr282NKOm5iaam3UA0G7HlL3LLWlu\nbklQ5j5rz3ql4K6ddY6Ky9L+tuZYuCIua3Q2Lkeff5bisvdvqr3PuvI42uvuuIL95dA1NNm8HkGQ\nWbyx7jBB7N692+JytVqNK1euQKlUAmipKQwbNsyknEqlMko0Go0GKpXK8LqqqgrPPfccZs+ejdGj\nR3cUlkM4anpqIiJPZHcfREpKCrKysgAAJSUlOHXqFIYPH25SbtSoUcjPz0d1dTX0ej2ys7MNieDa\ntWt47rnnMH36dKSnp9sbUqd42k8BEhE5mt0JYtasWaitrUVycjJeeOEFZGRkIDCwpcqybt06fPrp\npwCA6OhozJs3D5MnT8bIkSMRFRWFcePGAQA++OADlJSUICsrC2lpaUhLS8POnTvtDc0mP5VUcz4b\n6pA3zprqjd+ZWtjdSR0QEID33nvP7LJXXnnF6PXUqVMxdepUk3ILFizAggUL7A3FLpcr6jmfDVnk\njbOmeuN3pjs4F9MvosIDOZ8NWeSNs6Z643emO5ggftE/RskJusgib5wG2hu/M93BuZh+wflsqCPe\nOA20N35nuoM1iDa8bXpqsmjwYZIAABGmSURBVI2rp4F2RWexN35nuoM1CCIrubKW6arOYm/8znQH\nEwSRDVw1a6orfyfZG78ztWCC+IWfjxz39A7qsJy/b8v8Le2V7Wh5R+uWy83HYc96pRAU3A3B/nKX\nbNsSR8Vl7/6WOq6fSqrNdhY3NutsitGTjqMU39nScbTmGHvK/vLzcUyMTBC/kAsyBPh1vDsEWUvV\ntr2yHS3vaN0+CsHsZ+1ZrxSUwf6dmuvF0RwVl737W+q4+kT2gJ+PHA1NOsN7fj5yxKp72BSjJx1H\nKb6zpeNozTH2pP3lCOykJvIACbGhiFUHw89HDhlaL5TBSIgNdXVoDuON39ndsAZB5AEEQYbXpwzC\nyeIqlJbXIToiCAmxoV26s9Ybv7O7YYIg8hCCIMOgvr0wqG8vV4fiNN74nd0Jm5iIiMgsJggiIjKL\nCYKIiMxigiCiLkmvFyF2j4KoHIgT565ymo5OYIIgoi5HrxexJusE0PtxQDkY/7XnJ6zJOsEkYSMm\nCCLqck4WV6FYUwsIPoBMhoYmHYo1tThZXOXq0DwKEwQRdTmXyuvQ2OYJbABobNKhtLzORRF5JiYI\nIupy7okIgu9d8xP5+sgRHeGaucw8FRMEEXU5nKZDGnySmoi6HE7TIQ0mCCLqkixN09E6BBZ+oThx\n7iqTRzvsbmK6desWXn31VSQnJyMlJQUHDx5st+z27duRnJyMpKQkZGRkQK83nuu9oaEBTz75JCZO\nnGhvWEREZnEIrPXsThBbtmxBYGAgvvrqK2zevBlvvfUWbty4YVKutLQUGzZsQFZWFvLy8nDx4kXs\n3bvXqMx//ud/YuDAgfaGRETULg6BtZ7dCeLAgQOYMmUKACAmJgYDBgxAYWGhSbnc3FwkJSVBqVRC\nEASkp6dj//79huXHjh1DSUkJ0tLS7A2JiKhdHAJrPbv7IDQaDSIjIw2vVSoVysrKTMpptVqo1WrD\na7VaDa1WCwC4efMm/uM//gPvv/8+SkpKOh1LaGigTeXDwmwf8ubrq7D42Y6Wd3bd9qxXKq7ctiWO\niEuK/e1N+0sKzorrwX7h+PLoJdxubPNLdb5yDOgXbjYGb95fHSaICRMmQKPRmF1WVFQkSRCrV6/G\ntGnTEBERYVeCqKqqt7odMSwsCJWVtt8xNDY2A0C7n+1oeUfr9vVVmP2sPeuVQmf3l6M5Ki5797e3\n7S97OTOue0MDcJ8qGMWaWjQ26eDrI8d9qmDcGxpgEkNX31+CILN4Y91hgti9e7fF5Wq1GleuXIFS\nqQTQUlMYNmyYSTmVSmWUaDQaDVQqFQDg+++/R2FhITZt2oSGhgZcv34dY8eOxb59+zoKj0hyHOHS\ntXEIrPXs7oNISUlBVlYWAKCkpASnTp3C8OHDTcqNGjUK+fn5qK6uhl6vR3Z2NkaPHg0A2LdvHwoK\nClBQUIB3330X/fr1Y3Igl+AIF+/QOgR27KP3YVDfXkwO7bA7QcyaNQu1tbVITk7GCy+8gIyMDAQG\ntlRZ1q1bh08//RQAEB0djXnz5mHy5MkYOXIkoqKiMG7cOHs3TyQpjnAhusPuTuqAgAC89957Zpe9\n8sorRq+nTp2KqVOnWlzfsGHDsGvXLnvDIuoUSyNc+LvI5G04FxNRG5zkjegOJggPwF/Gch5O8kZ0\nB+dicnNGnaYyBf5rz0+IVQfj9SmD2LHmABzhQnQHE4SbM+o0BYw6Tdkm7hiWJnkj8iZsYnJznBaA\niFyFCcLNsdOUiFyFCcLNsdOUiFyFfRA2cMUUDOw0JSJXYYKwkitHE7HTlIhcgU1MVuIUDETkbZgg\nrMTRRETkbZggrMTRRETkbZggrMTRRETkbdhJbSWOJiIib8MEYQOOJiIib8ImJiIiMosJgoiIzGKC\nICIis5ggiIjILCYIIiIyiwmCiIjMYoIgIiKz7E4Qt27dwquvvork5GSkpKTg4MGD7Zbdvn07kpOT\nkZSUhIyMDOj1esOyn3/+GdOnT8eYMWMwZswY/PWvf7U3NI/SOpV4c48HceLcVej1oqtDIiIvZ/eD\nclu2bEFgYCC++uorlJSUYPr06cjLy0P37t2NypWWlmLDhg34/PPPERISgjlz5mDv3r0YP348bt68\niZdeeglr1qzBoEGD0NzcjLo675kEr+1U4jonTyVORNQeu2sQBw4cwJQpUwAAMTExGDBgAAoLC03K\n5ebmIikpCUqlEoIgID09Hfv37wcA5OTkYOjQoRg0aBAAQKFQoGfPnvaG5jE4lTgRuSO7axAajQaR\nkZGG1yqVCmVlZSbltFot1Gq14bVarYZWqwUAnDt3DgqFAnPmzEFFRQX69++PBQsWoEePHjbFEhoa\naFP5sDDpZ2L19VXYvO6qExqzU4lX1zc6JMbOcqdY2mJctmFctvHmuDpMEBMmTIBGozG7rKioSJIg\n9Ho9jhw5gs8++wy9evXCypUrsWrVKqxcudKm9VRV1Vvddh8WFoTKSumbsRobmwHApnWHBvrC10eO\nhjZJwtdHDmWgr0Ni7AxH7S97MS7bMC7bdPW4BEFm8ca6wwSxe/dui8vVajWuXLkCpVIJoKWmMGzY\nMJNyKpXKKNFoNBqoVCrDsmHDhiE8PBwAMHbsWCxatKij0LqM1qnEizW1aGzSwZdTiRORG7C7DyIl\nJQVZWVkAgJKSEpw6dQrDhw83KTdq1Cjk5+ejuroaer0e2dnZGD16NABg9OjROHnyJOrr6wEAhYWF\niIuLszc0j9E6lfgLaf0xPeV+vJDWnx3URORydvdBzJo1CwsXLkRycjIEQUBGRgYCA1uqLOvWrUN4\neDiefvppREdHY968eZg8eTIA4NFHH8W4ceMAtNRC5syZg6lTp0ImkyEqKgrLli2zNzSP0jqVuLtW\naYnI+8hEUewyA+7doQ9i1qyZAIAtW/7Sqc+7a4JgXLZhXLZhXLZxVh8En6QmIiKzmCCIiMgsJggi\nIjKLCYKIiMxigiAiIrOYIIiIyCwmCCIiMosJgoiIzGKCICIis5ggiIjILCYIIiIyiwmCiIjMYoIg\nIiKzmCCIiMgsJggiIjKLCYKIiMxigiAiIrOYIIiIyCwmCCIiMosJgoiIzGKCICIis+xOELdu3cKr\nr76K5ORkpKSk4ODBg+2W3b59O5KTk5GUlISMjAzo9XoAgF6vx/LlyzFmzBiMHTsWs2bNQnl5ub2h\nERGRHexOEFu2bEFgYCC++uorbN68GW+99RZu3LhhUq60tBQbNmxAVlYW8vLycPHiRezduxcAUFBQ\ngJMnT2Lv3r3Yt28f+vbti/fff9/e0IiIyA52J4gDBw5gypQpAICYmBgMGDAAhYWFJuVyc3ORlJQE\npVIJQRCQnp6O/fv3G5Y3NjaioaEBer0eN27cQO/eve0NjYiI7KCwdwUajQaRkZGG1yqVCmVlZSbl\ntFot1Gq14bVarYZWqwUAJCYm4ttvv8Vvf/tb+Pv7IzY2FosXL7Y3NKfT60WI3aMAv1CcOHcVCbGh\nEASZq8MiIuqUDhPEhAkToNFozC4rKiqSJIiffvoJ58+fR2FhIbp3744VK1Zg1apVNieJ0NBAm8qH\nhQXZVN4SnV7Ekg+KgN6PAzIFPtj7E+Lu7Ymlc/8FchuThJRxSYlx2YZx2YZx2cYZcXWYIHbv3m1x\nuVqtxpUrV6BUKgG01BSGDRtmUk6lUhklGo1GA5VKZdjGr3/9awQFtXzhcePGYdGiRdZ/i19UVdVD\nrxetKhsWFoTKyjqbt9GeE+eu4mzJNUDwAQDcbtThbMk1FBwtwaC+vaxej9RxSYVx2YZx2YZx2Uaq\nuARBZvHG2u4+iJSUFGRlZQEASkpKcOrUKQwfPtyk3KhRo5Cfn4/q6mro9XpkZ2dj9OjRAICoqCgc\nOXIETU1NAIC//vWv+NWvfmVvaE51qbwOjU06o/cam3QoLXe/k4uIyBp290HMmjULCxcuRHJyMgRB\nQEZGBgIDWzLSunXrEB4ejqeffhrR0dGYN28eJk+eDAB49NFHMW7cOADA9OnT8c9//hPjxo2DQqGA\nSqXCsmXL7A3Nqe6JCIKvjxwNbZKEr48c0RHuWT0lIuqITBRF69pkPIArm5j0ehFrsk6gWFOLxiYd\nfH3kiFUH4/Upg2zqqO7qVVqpMS7bMC7bdPW4OmpisrsGQS0EQYbXpwzCyeIqlJbXIToiiKOYiMij\nMUFISBBkGNS3l02d0kRE7opzMRERkVlMEEREZBYTBBERmcUEQUREZnWpTmpbRwy56wgjxmUbxmUb\nxmWbrhxXR+voUs9BEBGRdNjEREREZjFBEBGRWUwQRERkFhMEERGZxQRBRERmMUEQEZFZTBBERGQW\nEwQREZnFBEFERGZ1qak2rHHhwgUsXLgQNTU1CAkJQWZmJmJiYlwdFhITE+Hr6ws/Pz8AwB//+Eez\nv+3taJmZmcjNzcWVK1ewb98+9OvXD4Dr91t7cbl6v127dg3//u//jkuXLsHX1xf33nsvMjIyoFQq\nceLECSxevBgNDQ2IjIzEn//8Z4SGhro8rri4OPTr1w+C0HJ/uHr1asTFxTklLgCYN28eLl++DEEQ\nEBAQgLfffhvx8fEuP8fai8vV51irDRs2YP369Ybz3ynnl+hlZs6cKX7++eeiKIri559/Ls6cOdPF\nEbUYMWKE+I9//MPVYYjfffedqNFoTOJx9X5rLy5X77dr166JR44cMbxetWqV+Oabb4o6nU5MSkoS\nv/vuO1EURXHjxo3iwoULXR6XKIpiv379xPr6eqfFcrfa2lrDv7/66itx/Pjxoii6/hxrLy5Xn2Oi\nKIqnT58WZ82aZYjFWeeXVzUxVVVV4cyZM0hNTQUApKam4syZM6iurnZxZO7joYcegkqlMnrPHfab\nubjcQUhICIYNG2Z4PWjQIGg0Gpw+fRp+fn546KGHAABTp07Fl19+6fK43EFQUJDh3/X19ZDJZG5x\njpmLyx00NjYiIyMD77zzjuE9Z51fXtXEpNVqERERAblcDgCQy+UIDw+HVquFUql0cXQtVVdRFDF0\n6FD827/9G4KDg10dEgDuN2vp9Xp8+umnSExMhFarhVqtNixTKpXQ6/WG5hNXxdVq5syZ0Ol0eOyx\nx/Dyyy/D19fXqTH96U9/wt/+9jeIooj//u//dptz7O64WrnyHFu3bh3GjRuHqKgow3vOOr+8qgbh\nzrZt24a9e/di586dEEURGRkZrg7JI7jTflu2bBkCAgIwY8YMl8Vgzt1xffPNN9i1axe2bduGc+fO\nYePGjU6PacWKFfjmm2/w2muvYfXq1U7ffnvMxeXKc+z48eM4ffo0pk2b5rRttuVVCUKlUqG8vBw6\nnQ4AoNPpUFFR4RZNF60x+Pr6Ytq0afjhhx9cHNEd3G8dy8zMxMWLF7F27VoIggCVSmXUpFNdXQ1B\nEJxee7g7LuDOPgsMDER6erpLz7Xx48fj6NGj6N27t1udY61xXbt2zaXn2HfffYfz58/jiSeeQGJi\nIsrKyjBr1ixcvHjRKeeXVyWI0NBQxMfHIycnBwCQk5OD+Ph4lzeT3Lx5E3V1dQAAURSxf/9+xMfH\nuzSmtrjfLHv33Xdx+vRpbNy40dBUM2DAANy+fRvHjh0DAHz22WdISUlxeVzXr1/H7du3AQDNzc3I\nzc116j67ceMGtFqt4XVBQQF69Ojh8nOsvbj8/Pxceo7NnTsXhw8fRkFBAQoKCtC7d29s2bIFs2fP\ndsr55XU/GHT+/HksXLgQtbW1CA4ORmZmJmJjY10aU2lpKV5++WXodDro9Xr06dMHb731FsLDw50e\ny/Lly5GXl4erV6+iZ8+eCAkJwRdffOHy/WYurs2bN7t8v/3zn/9EamoqYmJi4O/vDwCIiorCxo0b\n8cMPP2DJkiVGwxB79erl0rhmz56NxYsXQyaTobm5GYMHD8aiRYvQvXt3p8R19epVzJs3D7du3YIg\nCOjRowcWLFiA/v37u/Qcay+u4OBgl59jbSUmJmLz5s3o16+fU84vr0sQRERkHa9qYiIiIusxQRAR\nkVlMEEREZBYTBBERmcUEQUREZjFBEBGRWUwQRERkFhMEERGZ9f8BB7c0uPAEiV0AAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ht9TzjaayznL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "6f1e819f-fa8e-4b1d-a477-c1dc41fd70a2"
      },
      "source": [
        "sgt.plot_pacf(df.sq_returns[1:], lags = 40, alpha = 0.05, zero = False , method = ('ols'))\n",
        "plt.title(\"PACF of Squared Returns\", size = 20)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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89dZbHD9+HLVajaenJ4sWLbK5qmjMOgX54OWpsV5BAHh5aggJcs1LVyGEAAcm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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKqBuc1AyznO",
        "colab_type": "text"
      },
      "source": [
        "## The arch_model() Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSjLhvd4zbfD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "9f49e6e1-55cc-4bcd-e694-b95eff5f276d"
      },
      "source": [
        "!pip3 install arch"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting arch\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e7/8c/75fa4d36a8cb85dd20b4ca08f742e357c3f2a3fce499da31ed37f4b1622f/arch-4.13-cp36-cp36m-manylinux1_x86_64.whl (720kB)\n",
            "\u001b[K     |████████████████████████████████| 727kB 1.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: statsmodels>=0.9 in /usr/local/lib/python3.6/dist-packages (from arch) (0.10.2)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.6/dist-packages (from arch) (1.0.3)\n",
            "Requirement already satisfied: cython>=0.29.14 in /usr/local/lib/python3.6/dist-packages (from arch) (0.29.16)\n",
            "Collecting property-cached>=1.6.3\n",
            "  Downloading https://files.pythonhosted.org/packages/5c/6c/94d8e520b20a2502e508e1c558f338061cf409cbee78fd6a3a5c6ae812bd/property_cached-1.6.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy>=1.14 in /usr/local/lib/python3.6/dist-packages (from arch) (1.18.2)\n",
            "Requirement already satisfied: scipy>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from arch) (1.4.1)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from statsmodels>=0.9->arch) (0.5.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23->arch) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23->arch) (2.8.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.4.0->statsmodels>=0.9->arch) (1.12.0)\n",
            "Installing collected packages: property-cached, arch\n",
            "Successfully installed arch-4.13 property-cached-1.6.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2nGWWGByznP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from arch import arch_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqqltgPfyznS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "outputId": "27de8ca5-8f28-445d-94de-85f3bde690e5"
      },
      "source": [
        "model_arch_1 = arch_model(df.returns[1:])\n",
        "results_arch_1 = model_arch_1.fit(update_freq = 5)\n",
        "results_arch_1.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration:      5,   Func. Count:     39,   Neg. LLF: 6972.734736835859\n",
            "Iteration:     10,   Func. Count:     73,   Neg. LLF: 6970.088042271316\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 6970.058366189893\n",
            "            Iterations: 13\n",
            "            Function evaluations: 91\n",
            "            Gradient evaluations: 13\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Constant Mean - GARCH Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>returns</td>      <th>  R-squared:         </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -6970.06</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   13948.1</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   13974.2</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>5020</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Wed, Apr 01 2020</td>  <th>  Df Residuals:      </th>    <td>5016</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>22:13:46</td>      <th>  Df Model:          </th>      <td>4</td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Mean Model</caption>\n",
              "<tr>\n",
              "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>     <th>95.0% Conf. Int.</th>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mu</th> <td>    0.0466</td> <td>1.183e-02</td> <td>    3.939</td> <td>8.187e-05</td> <td>[2.342e-02,6.981e-02]</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Volatility Model</caption>\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>     <th>95.0% Conf. Int.</th>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>omega</th>    <td>    0.0109</td> <td>3.004e-03</td> <td>    3.640</td> <td>2.724e-04</td> <td>[5.048e-03,1.682e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[1]</th> <td>    0.0835</td> <td>1.071e-02</td> <td>    7.794</td> <td>6.476e-15</td>  <td>[6.249e-02,  0.104]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>beta[1]</th>  <td>    0.9089</td> <td>1.148e-02</td> <td>   79.168</td>   <td>0.000</td>     <td>[  0.886,  0.931]</td>  \n",
              "</tr>\n",
              "</table><br/><br/>Covariance estimator: robust"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                     Constant Mean - GARCH Model Results                      \n",
              "==============================================================================\n",
              "Dep. Variable:                returns   R-squared:                      -0.001\n",
              "Mean Model:             Constant Mean   Adj. R-squared:                 -0.001\n",
              "Vol Model:                      GARCH   Log-Likelihood:               -6970.06\n",
              "Distribution:                  Normal   AIC:                           13948.1\n",
              "Method:            Maximum Likelihood   BIC:                           13974.2\n",
              "                                        No. Observations:                 5020\n",
              "Date:                Wed, Apr 01 2020   Df Residuals:                     5016\n",
              "Time:                        22:13:46   Df Model:                            4\n",
              "                                 Mean Model                                 \n",
              "============================================================================\n",
              "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
              "----------------------------------------------------------------------------\n",
              "mu             0.0466  1.183e-02      3.939  8.187e-05 [2.342e-02,6.981e-02]\n",
              "                              Volatility Model                              \n",
              "============================================================================\n",
              "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
              "----------------------------------------------------------------------------\n",
              "omega          0.0109  3.004e-03      3.640  2.724e-04 [5.048e-03,1.682e-02]\n",
              "alpha[1]       0.0835  1.071e-02      7.794  6.476e-15   [6.249e-02,  0.104]\n",
              "beta[1]        0.9089  1.148e-02     79.168      0.000     [  0.886,  0.931]\n",
              "============================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MC01nqvDyznW",
        "colab_type": "text"
      },
      "source": [
        "## The Simple ARCH(1) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKoeTDf3yznW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "99752498-3575-4c80-a35d-1007eea5387a"
      },
      "source": [
        "model_arch_1 = arch_model(df.returns[1:], mean = \"Constant\", vol = \"ARCH\", p = 1)\n",
        "results_arch_1 = model_arch_1.fit(update_freq = 5)\n",
        "results_arch_1.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration:      5,   Func. Count:     34,   Neg. LLF: 7660.088789377838\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 7660.088782040875\n",
            "            Iterations: 6\n",
            "            Function evaluations: 39\n",
            "            Gradient evaluations: 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Constant Mean - ARCH Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>returns</td>      <th>  R-squared:         </th>  <td>  -0.000</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>  -0.000</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Vol Model:</th>            <td>ARCH</td>        <th>  Log-Likelihood:    </th> <td>  -7660.09</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   15326.2</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   15345.7</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>5020</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Wed, Apr 01 2020</td>  <th>  Df Residuals:      </th>    <td>5017</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>22:13:47</td>      <th>  Df Model:          </th>      <td>3</td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Mean Model</caption>\n",
              "<tr>\n",
              "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>     <th>95.0% Conf. Int.</th>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mu</th> <td>    0.0345</td> <td>1.603e-02</td> <td>    2.151</td> <td>3.147e-02</td> <td>[3.063e-03,6.590e-02]</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Volatility Model</caption>\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>        <th>P>|t|</th>   <th>95.0% Conf. Int.</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>omega</th>    <td>    0.9206</td> <td>4.309e-02</td> <td>   21.365</td> <td>2.855e-101</td> <td>[  0.836,  1.005]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[1]</th> <td>    0.3684</td> <td>5.040e-02</td> <td>    7.310</td>  <td>2.673e-13</td> <td>[  0.270,  0.467]</td>\n",
              "</tr>\n",
              "</table><br/><br/>Covariance estimator: robust"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                      Constant Mean - ARCH Model Results                      \n",
              "==============================================================================\n",
              "Dep. Variable:                returns   R-squared:                      -0.000\n",
              "Mean Model:             Constant Mean   Adj. R-squared:                 -0.000\n",
              "Vol Model:                       ARCH   Log-Likelihood:               -7660.09\n",
              "Distribution:                  Normal   AIC:                           15326.2\n",
              "Method:            Maximum Likelihood   BIC:                           15345.7\n",
              "                                        No. Observations:                 5020\n",
              "Date:                Wed, Apr 01 2020   Df Residuals:                     5017\n",
              "Time:                        22:13:47   Df Model:                            3\n",
              "                                 Mean Model                                 \n",
              "============================================================================\n",
              "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
              "----------------------------------------------------------------------------\n",
              "mu             0.0345  1.603e-02      2.151  3.147e-02 [3.063e-03,6.590e-02]\n",
              "                            Volatility Model                            \n",
              "========================================================================\n",
              "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
              "------------------------------------------------------------------------\n",
              "omega          0.9206  4.309e-02     21.365 2.855e-101 [  0.836,  1.005]\n",
              "alpha[1]       0.3684  5.040e-02      7.310  2.673e-13 [  0.270,  0.467]\n",
              "========================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8LnRv9qyznb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "2e8bceb6-6629-44da-e9bd-bbe46499dc9d"
      },
      "source": [
        "model_arch_1 = arch_model(df.returns[1:], mean = \"AR\", lags = [2, 3, 6], vol = \"ARCH\", p = 1, dist = \"ged\")\n",
        "results_arch_1 = model_arch_1.fit(update_freq = 5)\n",
        "results_arch_1.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration:      5,   Func. Count:     56,   Neg. LLF: 7392.978753452066\n",
            "Iteration:     10,   Func. Count:    109,   Neg. LLF: 7320.74917620397\n",
            "Iteration:     15,   Func. Count:    156,   Neg. LLF: 7317.119673966012\n",
            "Iteration:     20,   Func. Count:    205,   Neg. LLF: 7317.118107649978\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 7317.118092665535\n",
            "            Iterations: 22\n",
            "            Function evaluations: 228\n",
            "            Gradient evaluations: 22\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>AR - ARCH Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>             <td>returns</td>            <th>  R-squared:         </th>  <td>   0.008</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean Model:</th>                  <td>AR</td>               <th>  Adj. R-squared:    </th>  <td>   0.007</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Vol Model:</th>                  <td>ARCH</td>              <th>  Log-Likelihood:    </th> <td>  -7317.12</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Distribution:</th>  <td>Generalized Error Distribution</td> <th>  AIC:               </th> <td>   14648.2</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>              <td>Maximum Likelihood</td>       <th>  BIC:               </th> <td>   14693.9</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                              <td></td>                <th>  No. Observations:  </th>    <td>5014</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                 <td>Wed, Apr 01 2020</td>        <th>  Df Residuals:      </th>    <td>5007</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                     <td>22:13:48</td>            <th>  Df Model:          </th>      <td>7</td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Mean Model</caption>\n",
              "<tr>\n",
              "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Const</th>      <td>    0.0474</td> <td>6.131e-03</td> <td>    7.732</td> <td>1.058e-14</td>  <td>[3.539e-02,5.943e-02]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>returns[2]</th> <td>   -0.0396</td> <td>2.806e-02</td> <td>   -1.410</td> <td>    0.159</td> <td>[-9.457e-02,1.543e-02]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>returns[3]</th> <td>   -0.0348</td> <td>5.936e-03</td> <td>   -5.858</td> <td>4.677e-09</td> <td>[-4.641e-02,-2.314e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>returns[6]</th> <td>   -0.0283</td> <td>1.728e-03</td> <td>  -16.391</td> <td>2.235e-60</td> <td>[-3.170e-02,-2.493e-02]</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Volatility Model</caption>\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>        <th>P>|t|</th>   <th>95.0% Conf. Int.</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>omega</th>    <td>    0.9166</td> <td>3.760e-02</td> <td>   24.378</td> <td>2.951e-131</td> <td>[  0.843,  0.990]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[1]</th> <td>    0.3428</td> <td>3.939e-02</td> <td>    8.703</td>  <td>3.228e-18</td> <td>[  0.266,  0.420]</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Distribution</caption>\n",
              "<tr>\n",
              "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>        <th>P>|t|</th>   <th>95.0% Conf. Int.</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>nu</th> <td>    1.0675</td> <td>3.251e-02</td> <td>   32.842</td> <td>1.485e-236</td> <td>[  1.004,  1.131]</td>\n",
              "</tr>\n",
              "</table><br/><br/>Covariance estimator: robust"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                 AR - ARCH Model Results                                  \n",
              "==========================================================================================\n",
              "Dep. Variable:                            returns   R-squared:                       0.008\n",
              "Mean Model:                                    AR   Adj. R-squared:                  0.007\n",
              "Vol Model:                                   ARCH   Log-Likelihood:               -7317.12\n",
              "Distribution:      Generalized Error Distribution   AIC:                           14648.2\n",
              "Method:                        Maximum Likelihood   BIC:                           14693.9\n",
              "                                                    No. Observations:                 5014\n",
              "Date:                            Wed, Apr 01 2020   Df Residuals:                     5007\n",
              "Time:                                    22:13:48   Df Model:                            7\n",
              "                                  Mean Model                                  \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|        95.0% Conf. Int.\n",
              "------------------------------------------------------------------------------\n",
              "Const          0.0474  6.131e-03      7.732  1.058e-14   [3.539e-02,5.943e-02]\n",
              "returns[2]    -0.0396  2.806e-02     -1.410      0.159  [-9.457e-02,1.543e-02]\n",
              "returns[3]    -0.0348  5.936e-03     -5.858  4.677e-09 [-4.641e-02,-2.314e-02]\n",
              "returns[6]    -0.0283  1.728e-03    -16.391  2.235e-60 [-3.170e-02,-2.493e-02]\n",
              "                            Volatility Model                            \n",
              "========================================================================\n",
              "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
              "------------------------------------------------------------------------\n",
              "omega          0.9166  3.760e-02     24.378 2.951e-131 [  0.843,  0.990]\n",
              "alpha[1]       0.3428  3.939e-02      8.703  3.228e-18 [  0.266,  0.420]\n",
              "                              Distribution                              \n",
              "========================================================================\n",
              "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
              "------------------------------------------------------------------------\n",
              "nu             1.0675  3.251e-02     32.842 1.485e-236 [  1.004,  1.131]\n",
              "========================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gikYgol6yznh",
        "colab_type": "text"
      },
      "source": [
        "## Higher-Lag ARCH Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhJyNzsMyzni",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "49ff595a-f6e8-469f-f91e-1c590bfb08a2"
      },
      "source": [
        "model_arch_2 = arch_model(df.returns[1:], mean = \"Constant\", vol = \"ARCH\", p = 2)\n",
        "results_arch_2 = model_arch_2.fit(update_freq = 5)\n",
        "results_arch_2.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration:      5,   Func. Count:     41,   Neg. LLF: 7449.870533644225\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 7449.545164098316\n",
            "            Iterations: 9\n",
            "            Function evaluations: 67\n",
            "            Gradient evaluations: 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Constant Mean - ARCH Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>returns</td>      <th>  R-squared:         </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Vol Model:</th>            <td>ARCH</td>        <th>  Log-Likelihood:    </th> <td>  -7449.55</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   14907.1</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14933.2</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>5020</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Wed, Apr 01 2020</td>  <th>  Df Residuals:      </th>    <td>5016</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>22:13:48</td>      <th>  Df Model:          </th>      <td>4</td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Mean Model</caption>\n",
              "<tr>\n",
              "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>     <th>95.0% Conf. Int.</th>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mu</th> <td>    0.0484</td> <td>1.401e-02</td> <td>    3.455</td> <td>5.495e-04</td> <td>[2.095e-02,7.585e-02]</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Volatility Model</caption>\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>   <th>95.0% Conf. Int.</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>omega</th>    <td>    0.6978</td> <td>3.564e-02</td> <td>   19.580</td> <td>2.272e-85</td> <td>[  0.628,  0.768]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[1]</th> <td>    0.2202</td> <td>3.474e-02</td> <td>    6.339</td> <td>2.311e-10</td> <td>[  0.152,  0.288]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[2]</th> <td>    0.2783</td> <td>3.645e-02</td> <td>    7.635</td> <td>2.260e-14</td> <td>[  0.207,  0.350]</td>\n",
              "</tr>\n",
              "</table><br/><br/>Covariance estimator: robust"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                      Constant Mean - ARCH Model Results                      \n",
              "==============================================================================\n",
              "Dep. Variable:                returns   R-squared:                      -0.001\n",
              "Mean Model:             Constant Mean   Adj. R-squared:                 -0.001\n",
              "Vol Model:                       ARCH   Log-Likelihood:               -7449.55\n",
              "Distribution:                  Normal   AIC:                           14907.1\n",
              "Method:            Maximum Likelihood   BIC:                           14933.2\n",
              "                                        No. Observations:                 5020\n",
              "Date:                Wed, Apr 01 2020   Df Residuals:                     5016\n",
              "Time:                        22:13:48   Df Model:                            4\n",
              "                                 Mean Model                                 \n",
              "============================================================================\n",
              "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
              "----------------------------------------------------------------------------\n",
              "mu             0.0484  1.401e-02      3.455  5.495e-04 [2.095e-02,7.585e-02]\n",
              "                            Volatility Model                            \n",
              "========================================================================\n",
              "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
              "------------------------------------------------------------------------\n",
              "omega          0.6978  3.564e-02     19.580  2.272e-85 [  0.628,  0.768]\n",
              "alpha[1]       0.2202  3.474e-02      6.339  2.311e-10 [  0.152,  0.288]\n",
              "alpha[2]       0.2783  3.645e-02      7.635  2.260e-14 [  0.207,  0.350]\n",
              "========================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADhc9wc8yznl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "bf2541f0-e4a7-43d1-defa-8515a0537a78"
      },
      "source": [
        "model_arch_3 = arch_model(df.returns[1:], mean = \"Constant\", vol = \"ARCH\", p = 3)\n",
        "results_arch_3 = model_arch_3.fit(update_freq = 5)\n",
        "results_arch_3.summary()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration:      5,   Func. Count:     45,   Neg. LLF: 7278.805807411393\n",
            "Iteration:     10,   Func. Count:     84,   Neg. LLF: 7277.050453152619\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 7277.050452148393\n",
            "            Iterations: 11\n",
            "            Function evaluations: 91\n",
            "            Gradient evaluations: 11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Constant Mean - ARCH Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>returns</td>      <th>  R-squared:         </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Vol Model:</th>            <td>ARCH</td>        <th>  Log-Likelihood:    </th> <td>  -7277.05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   14564.1</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14596.7</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>5020</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Wed, Apr 01 2020</td>  <th>  Df Residuals:      </th>    <td>5015</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>22:13:48</td>      <th>  Df Model:          </th>      <td>5</td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Mean Model</caption>\n",
              "<tr>\n",
              "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>     <th>95.0% Conf. Int.</th>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mu</th> <td>    0.0529</td> <td>1.316e-02</td> <td>    4.018</td> <td>5.875e-05</td> <td>[2.707e-02,7.865e-02]</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Volatility Model</caption>\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>   <th>95.0% Conf. Int.</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>omega</th>    <td>    0.5012</td> <td>2.834e-02</td> <td>   17.685</td> <td>5.505e-70</td> <td>[  0.446,  0.557]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[1]</th> <td>    0.1570</td> <td>2.649e-02</td> <td>    5.926</td> <td>3.108e-09</td> <td>[  0.105,  0.209]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[2]</th> <td>    0.2197</td> <td>2.588e-02</td> <td>    8.489</td> <td>2.077e-17</td> <td>[  0.169,  0.270]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[3]</th> <td>    0.2804</td> <td>3.354e-02</td> <td>    8.359</td> <td>6.302e-17</td> <td>[  0.215,  0.346]</td>\n",
              "</tr>\n",
              "</table><br/><br/>Covariance estimator: robust"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                      Constant Mean - ARCH Model Results                      \n",
              "==============================================================================\n",
              "Dep. Variable:                returns   R-squared:                      -0.001\n",
              "Mean Model:             Constant Mean   Adj. R-squared:                 -0.001\n",
              "Vol Model:                       ARCH   Log-Likelihood:               -7277.05\n",
              "Distribution:                  Normal   AIC:                           14564.1\n",
              "Method:            Maximum Likelihood   BIC:                           14596.7\n",
              "                                        No. Observations:                 5020\n",
              "Date:                Wed, Apr 01 2020   Df Residuals:                     5015\n",
              "Time:                        22:13:48   Df Model:                            5\n",
              "                                 Mean Model                                 \n",
              "============================================================================\n",
              "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
              "----------------------------------------------------------------------------\n",
              "mu             0.0529  1.316e-02      4.018  5.875e-05 [2.707e-02,7.865e-02]\n",
              "                            Volatility Model                            \n",
              "========================================================================\n",
              "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
              "------------------------------------------------------------------------\n",
              "omega          0.5012  2.834e-02     17.685  5.505e-70 [  0.446,  0.557]\n",
              "alpha[1]       0.1570  2.649e-02      5.926  3.108e-09 [  0.105,  0.209]\n",
              "alpha[2]       0.2197  2.588e-02      8.489  2.077e-17 [  0.169,  0.270]\n",
              "alpha[3]       0.2804  3.354e-02      8.359  6.302e-17 [  0.215,  0.346]\n",
              "========================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdsrs8SIyzno",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 820
        },
        "outputId": "f0eec50f-f996-41d8-c6d8-1cd6828d1b62"
      },
      "source": [
        "model_arch_13 = arch_model(df.returns[1:], mean = \"Constant\", vol = \"ARCH\", p = 13)\n",
        "results_arch_13 = model_arch_13.fit(update_freq = 5)\n",
        "results_arch_13.summary()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration:      5,   Func. Count:     93,   Neg. LLF: 6996.500043707073\n",
            "Iteration:     10,   Func. Count:    188,   Neg. LLF: 6985.661375673177\n",
            "Iteration:     15,   Func. Count:    283,   Neg. LLF: 6984.395178347233\n",
            "Iteration:     20,   Func. Count:    378,   Neg. LLF: 6984.218261212904\n",
            "Iteration:     25,   Func. Count:    468,   Neg. LLF: 6984.175140343418\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 6984.175062618146\n",
            "            Iterations: 27\n",
            "            Function evaluations: 502\n",
            "            Gradient evaluations: 27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Constant Mean - ARCH Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>returns</td>      <th>  R-squared:         </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>  -0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Vol Model:</th>            <td>ARCH</td>        <th>  Log-Likelihood:    </th> <td>  -6984.18</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   13998.4</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14096.2</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>5020</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Wed, Apr 01 2020</td>  <th>  Df Residuals:      </th>    <td>5005</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>22:13:48</td>      <th>  Df Model:          </th>     <td>15</td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Mean Model</caption>\n",
              "<tr>\n",
              "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>     <th>95.0% Conf. Int.</th>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mu</th> <td>    0.0492</td> <td>1.206e-02</td> <td>    4.077</td> <td>4.566e-05</td> <td>[2.553e-02,7.281e-02]</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Volatility Model</caption>\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>omega</th>     <td>    0.1871</td> <td>2.210e-02</td> <td>    8.466</td> <td>2.538e-17</td>    <td>[  0.144,  0.230]</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[1]</th>  <td>    0.0568</td> <td>1.729e-02</td> <td>    3.282</td> <td>1.029e-03</td>  <td>[2.287e-02,9.065e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[2]</th>  <td>    0.1156</td> <td>2.017e-02</td> <td>    5.733</td> <td>9.840e-09</td>   <td>[7.610e-02,  0.155]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[3]</th>  <td>    0.1265</td> <td>2.625e-02</td> <td>    4.820</td> <td>1.433e-06</td>   <td>[7.509e-02,  0.178]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[4]</th>  <td>    0.0884</td> <td>2.159e-02</td> <td>    4.093</td> <td>4.265e-05</td>   <td>[4.605e-02,  0.131]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[5]</th>  <td>    0.0695</td> <td>1.750e-02</td> <td>    3.970</td> <td>7.189e-05</td>   <td>[3.518e-02,  0.104]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[6]</th>  <td>    0.0865</td> <td>1.926e-02</td> <td>    4.491</td> <td>7.092e-06</td>   <td>[4.874e-02,  0.124]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[7]</th>  <td>    0.0404</td> <td>1.846e-02</td> <td>    2.188</td> <td>2.870e-02</td>  <td>[4.203e-03,7.658e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[8]</th>  <td>    0.0654</td> <td>1.833e-02</td> <td>    3.565</td> <td>3.633e-04</td>   <td>[2.943e-02,  0.101]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[9]</th>  <td>    0.0292</td> <td>1.603e-02</td> <td>    1.820</td> <td>6.877e-02</td> <td>[-2.245e-03,6.058e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[10]</th> <td>    0.0335</td> <td>1.605e-02</td> <td>    2.089</td> <td>3.670e-02</td>  <td>[2.072e-03,6.498e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[11]</th> <td>    0.0775</td> <td>1.760e-02</td> <td>    4.403</td> <td>1.067e-05</td>   <td>[4.300e-02,  0.112]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[12]</th> <td>    0.0473</td> <td>1.873e-02</td> <td>    2.526</td> <td>1.153e-02</td>  <td>[1.060e-02,8.402e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[13]</th> <td>    0.0372</td> <td>1.431e-02</td> <td>    2.600</td> <td>9.317e-03</td>  <td>[9.162e-03,6.526e-02]</td>\n",
              "</tr>\n",
              "</table><br/><br/>Covariance estimator: robust"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                      Constant Mean - ARCH Model Results                      \n",
              "==============================================================================\n",
              "Dep. Variable:                returns   R-squared:                      -0.001\n",
              "Mean Model:             Constant Mean   Adj. R-squared:                 -0.001\n",
              "Vol Model:                       ARCH   Log-Likelihood:               -6984.18\n",
              "Distribution:                  Normal   AIC:                           13998.4\n",
              "Method:            Maximum Likelihood   BIC:                           14096.2\n",
              "                                        No. Observations:                 5020\n",
              "Date:                Wed, Apr 01 2020   Df Residuals:                     5005\n",
              "Time:                        22:13:48   Df Model:                           15\n",
              "                                 Mean Model                                 \n",
              "============================================================================\n",
              "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
              "----------------------------------------------------------------------------\n",
              "mu             0.0492  1.206e-02      4.077  4.566e-05 [2.553e-02,7.281e-02]\n",
              "                               Volatility Model                              \n",
              "=============================================================================\n",
              "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
              "-----------------------------------------------------------------------------\n",
              "omega          0.1871  2.210e-02      8.466  2.538e-17      [  0.144,  0.230]\n",
              "alpha[1]       0.0568  1.729e-02      3.282  1.029e-03  [2.287e-02,9.065e-02]\n",
              "alpha[2]       0.1156  2.017e-02      5.733  9.840e-09    [7.610e-02,  0.155]\n",
              "alpha[3]       0.1265  2.625e-02      4.820  1.433e-06    [7.509e-02,  0.178]\n",
              "alpha[4]       0.0884  2.159e-02      4.093  4.265e-05    [4.605e-02,  0.131]\n",
              "alpha[5]       0.0695  1.750e-02      3.970  7.189e-05    [3.518e-02,  0.104]\n",
              "alpha[6]       0.0865  1.926e-02      4.491  7.092e-06    [4.874e-02,  0.124]\n",
              "alpha[7]       0.0404  1.846e-02      2.188  2.870e-02  [4.203e-03,7.658e-02]\n",
              "alpha[8]       0.0654  1.833e-02      3.565  3.633e-04    [2.943e-02,  0.101]\n",
              "alpha[9]       0.0292  1.603e-02      1.820  6.877e-02 [-2.245e-03,6.058e-02]\n",
              "alpha[10]      0.0335  1.605e-02      2.089  3.670e-02  [2.072e-03,6.498e-02]\n",
              "alpha[11]      0.0775  1.760e-02      4.403  1.067e-05    [4.300e-02,  0.112]\n",
              "alpha[12]      0.0473  1.873e-02      2.526  1.153e-02  [1.060e-02,8.402e-02]\n",
              "alpha[13]      0.0372  1.431e-02      2.600  9.317e-03  [9.162e-03,6.526e-02]\n",
              "=============================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    }
  ]
}