VIX_Indiv_minorp_Sem3_part_1_AA_SC_P2MCA2107470.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "143DJFkfpKywWH-P3XiEqj3NsmaJ3-9w5",
      "authorship_tag": "ABX9TyMm4LMx+ogDvlzgcWUgbtjs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/krish-Algometrix/Machine-Learning-Projects/blob/main/VIX_Indiv_minorp_Sem3_part_1_AA_SC_P2MCA2107470.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kjb-zniGug4v"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import datetime\n",
        "import statsmodels as st"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install statsmodels"
      ],
      "metadata": {
        "id": "SS9KwRFKvkkX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cf5c361-bbf5-49f9-bf4c-529dc32aa599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.13.5)\n",
            "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (0.5.3)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (23.1)\n",
            "Requirement already satisfied: scipy>=1.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.10.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.22.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->statsmodels) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->statsmodels) (2022.7.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install garch"
      ],
      "metadata": {
        "id": "hmZprFFV9nNV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c9ba7ba-53c3-45c9-ea3e-816d0369366e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting garch\n",
            "  Downloading garch-0.1.tar.gz (1.0 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: garch\n",
            "  Building wheel for garch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for garch: filename=garch-0.1-py3-none-any.whl size=1588 sha256=bd05a004daccb89831560fe0a3df576512d0f7b57be4a9430ada8c92802aacd7\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/d6/a0/63b764a634cda23c28afc1c0341fc0443ae758ae5fd2bcfebe\n",
            "Successfully built garch\n",
            "Installing collected packages: garch\n",
            "Successfully installed garch-0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install arch"
      ],
      "metadata": {
        "id": "paSvlRqiwSW-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70f498fe-1903-453f-db6f-9c31ec26bb92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting arch\n",
            "  Downloading arch-6.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (916 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m916.4/916.4 kB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.10/dist-packages (from arch) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from arch) (1.10.1)\n",
            "Requirement already satisfied: pandas>=1.1 in /usr/local/lib/python3.10/dist-packages (from arch) (1.5.3)\n",
            "Requirement already satisfied: statsmodels>=0.12 in /usr/local/lib/python3.10/dist-packages (from arch) (0.13.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1->arch) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1->arch) (2022.7.1)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.12->arch) (0.5.3)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.12->arch) (23.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.2->statsmodels>=0.12->arch) (1.16.0)\n",
            "Installing collected packages: arch\n",
            "Successfully installed arch-6.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from arch import arch_model"
      ],
      "metadata": {
        "id": "nnvmIJjT-No6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/drive/MyDrive/cvix.csv\""
      ],
      "metadata": {
        "id": "yq2aWZKTF1fD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vix = pd.read_csv(path)\n",
        "type(vix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0CyKqD0H32o",
        "outputId": "edcfd860-f2ec-44fc-89c7-2bfb39668c33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix.info(),vix.describe(),vix.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "iHwDbLVkXYhr",
        "outputId": "ca6152d2-9ee4-4bf5-f0e9-44b424a2747c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8374 entries, 0 to 8373\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8374 non-null   object \n",
            " 1   cvs     8374 non-null   float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 131.0+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None,\n",
              "                cvs\n",
              " count  8374.000000\n",
              " mean     19.675254\n",
              " std       7.969064\n",
              " min       9.140000\n",
              " 25%      13.910000\n",
              " 50%      17.880000\n",
              " 75%      23.140000\n",
              " max      82.690000,\n",
              " array([[<Axes: title={'center': 'cvs'}>]], dtype=object))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvret'] = vix['cvs'].pct_change()"
      ],
      "metadata": {
        "id": "2blcSQiGvGOq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvar'] = vix['cvret']**2\n",
        "vix['cvar'].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVOicrvSs6vi",
        "outputId": "c970fb0b-34d7-48ef-9f3f-194a8365628b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         NaN\n",
              "1    0.003036\n",
              "2    0.003206\n",
              "3    0.002144\n",
              "4    0.000056\n",
              "5    0.009169\n",
              "6    0.000117\n",
              "7    0.011344\n",
              "8    0.052408\n",
              "9    0.004760\n",
              "Name: cvar, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvret'].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B56e_Kv-xTct",
        "outputId": "c3beb1dc-a0b8-4d3c-ff89-3fb85e2ae06a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         NaN\n",
              "1    0.055104\n",
              "2    0.056625\n",
              "3    0.046306\n",
              "4    0.007459\n",
              "5    0.095755\n",
              "6    0.010811\n",
              "7   -0.106506\n",
              "8    0.228928\n",
              "9    0.068994\n",
              "Name: cvret, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix.tail(10),vix.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7VE5_0SIPty",
        "outputId": "cacf61b6-9aed-4572-b3b2-4d805be21b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(            date    cvs     cvret      cvar\n",
              " 8364  26-11-2012  24.80  0.096860  0.009382\n",
              " 8365  27-11-2012  26.52  0.069355  0.004810\n",
              " 8366  28-11-2012  23.73 -0.105204  0.011068\n",
              " 8367  29-11-2012  26.14  0.101559  0.010314\n",
              " 8368  30-11-2012  22.99 -0.120505  0.014521\n",
              " 8369  01-12-2012  25.51  0.109613  0.012015\n",
              " 8370  02-12-2012  24.15 -0.053312  0.002842\n",
              " 8371  03-12-2012  21.38 -0.114700  0.013156\n",
              " 8372  04-12-2012  22.26  0.041160  0.001694\n",
              " 8373  05-12-2012  22.61  0.015723  0.000247,\n",
              "          date    cvs     cvret      cvar\n",
              " 0  02-01-1990  17.24       NaN       NaN\n",
              " 1  03-01-1990  18.19  0.055104  0.003036\n",
              " 2  04-01-1990  19.22  0.056625  0.003206\n",
              " 3  05-01-1990  20.11  0.046306  0.002144\n",
              " 4  06-01-1990  20.26  0.007459  0.000056\n",
              " 5  07-01-1990  22.20  0.095755  0.009169\n",
              " 6  08-01-1990  22.44  0.010811  0.000117\n",
              " 7  09-01-1990  20.05 -0.106506  0.011344\n",
              " 8  10-01-1990  24.64  0.228928  0.052408\n",
              " 9  11-01-1990  26.34  0.068994  0.004760)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWJaBJh-H6Qy",
        "outputId": "c6546717-a8c8-453d-c228-f92307c12691"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8374 entries, 0 to 8373\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    8374 non-null   object \n",
            " 1   cvs     8374 non-null   float64\n",
            " 2   cvret   8373 non-null   float64\n",
            " 3   cvar    8373 non-null   float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 261.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvret'][0] = vix['cvret'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggBRxISPIY5B",
        "outputId": "72335c00-f21f-456f-9d90-09718c359afe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-1c2f619339d1>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  vix['cvret'][0] = vix['cvret'][1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvret'].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mb-T23OiJkoN",
        "outputId": "e21caa76-aef4-4ec7-8f7c-84914e65db2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.055104\n",
              "1    0.055104\n",
              "2    0.056625\n",
              "3    0.046306\n",
              "4    0.007459\n",
              "5    0.095755\n",
              "6    0.010811\n",
              "7   -0.106506\n",
              "8    0.228928\n",
              "9    0.068994\n",
              "Name: cvret, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvret'].hist(), vix['cvret'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "Fno53m9sxjkS",
        "outputId": "5ea225ae-1dc6-4fa2-877e-e4e41b07bcdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Axes: >,\n",
              " count    8374.000000\n",
              " mean        0.002353\n",
              " std         0.070226\n",
              " min        -0.295726\n",
              " 25%        -0.037234\n",
              " 50%        -0.003929\n",
              " 75%         0.033110\n",
              " max         1.155979\n",
              " Name: cvret, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvar'].hist(), vix['cvar'].describe()\n",
        "vix['cvar'][0] = vix['cvar'][1]"
      ],
      "metadata": {
        "id": "Aj7JetvPtqdA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "0793d1ca-8d20-450f-adcb-d2ec4308414a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-e1c394cdb857>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  vix['cvar'][0] = vix['cvar'][1]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vix['cvar'].info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2Lurom2v_RP",
        "outputId": "bbe8ad04-b2d3-467e-87fe-c48e9ad406fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.info of 0       0.003036\n",
              "1       0.003036\n",
              "2       0.003206\n",
              "3       0.002144\n",
              "4       0.000056\n",
              "          ...   \n",
              "8369    0.012015\n",
              "8370    0.002842\n",
              "8371    0.013156\n",
              "8372    0.001694\n",
              "8373    0.000247\n",
              "Name: cvar, Length: 8374, dtype: float64>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "sm.graphics.tsa.plot_acf(vix['cvs'].values.squeeze(), alpha = 0.01, lags = 40)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "RiJZMwpqGyRz",
        "outputId": "65d5b6a5-3fab-4a5d-d531-319bab60ab73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "understanding the ACF of vix returns"
      ],
      "metadata": {
        "id": "fusfbwgyLCrQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sm.graphics.tsa.plot_acf(vix['cvret'].values.squeeze(), alpha = 0.01, lags = 40)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "tXvVnjGBK8tD",
        "outputId": "9ccfe68c-b607-45ce-c0cc-848ca15436da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "understanding ACF on the square of the returns proxy for variance cvar"
      ],
      "metadata": {
        "id": "br00oKMxLWRP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sm.graphics.tsa.plot_acf(vix['cvar'].values.squeeze(), alpha = 0.01, lags = 40)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "jMQCxJYFLfxW",
        "outputId": "14dcf89b-fc83-40c1-9b61-284e86fd6247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.plotting.lag_plot(vix['cvret'], lag= 40),pd.plotting.lag_plot(vix['cvret'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "flE--v40CB9h",
        "outputId": "349a6962-edb2-4bde-f217-130cf34b1e90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Axes: xlabel='y(t)', ylabel='y(t + 1)'>,\n",
              " <Axes: xlabel='y(t)', ylabel='y(t + 1)'>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "sm.graphics.tsa.plot_acf(vix['cvret'].values.squeeze(), alpha = 0.01, lags =5, title = \"ACF of lag in Vix returns\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "pYjFRcOrDCOr",
        "outputId": "b4230f2a-7f6f-4a51-ec4a-4446602fb03d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.plotting.lag_plot(vix['cvar'], lag= 40),pd.plotting.lag_plot(vix['cvar'], lag= 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "Xe26rWUcu_ea",
        "outputId": "2355f9ce-73cf-42e2-e74e-03bc3688334b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Axes: xlabel='y(t)', ylabel='y(t + 5)'>,\n",
              " <Axes: xlabel='y(t)', ylabel='y(t + 5)'>)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sm.graphics.tsa.plot_acf(vix['cvar'].values.squeeze(), alpha = 0.01, lags =5, title =\"ACF of lagged vix returns squared\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "QX8Y4uTJue5p",
        "outputId": "ed8ea7d6-9197-46c9-8804-249cbf1819ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interstingly we see that in the first difference of VIX series, we have some autocorrelation although very small and eventually dies out . Similarly in the square of the vix return series too, we see that there is autocorrelation over five lags or more. This means there is some variance which changes over time which is also in line with theory."
      ],
      "metadata": {
        "id": "OBBO1-uawSQx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After having tested the lagged vix return series and the variance of the lagged return series for serial autocorrelation, we try to a fit a arch model the variance of vix return series."
      ],
      "metadata": {
        "id": "XXsJJNPxyTxZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we are interested in the volatility modeling of VIX index, we create a new object univariate series called by assigning cvar series to vix_cvar which the volatility of the Vix Index or variance of vix index."
      ],
      "metadata": {
        "id": "FzD-qHBAkZjj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vix_cvar = vix['cvar']\n",
        "vix_cvar.head(10)\n",
        "vix_cvar.tail(10)\n",
        "vix_cvar.info()\n",
        "plt.plot(vix_cvar)  # plot of the squared servies of first differences of vix index proxy historical observed variance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "TZOqDWBdjz7Q",
        "outputId": "a510dfd4-3f52-4207-d759-224d6e851960"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "RangeIndex: 8374 entries, 0 to 8373\n",
            "Series name: cvar\n",
            "Non-Null Count  Dtype  \n",
            "--------------  -----  \n",
            "8374 non-null   float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 65.5 KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f69e3ac31c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next step is to split the data into test and trainning sample . We therefore split the data vix_cvar"
      ],
      "metadata": {
        "id": "RYwUfmJQ-rVt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vix_cvar_test = 50"
      ],
      "metadata": {
        "id": "zCnf6VIskVw4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train,test =  vix_cvar[:-vix_cvar_test], vix_cvar[-vix_cvar_test:]"
      ],
      "metadata": {
        "id": "fgAjvWxS-p5m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now define the model ARCH(1,1) and fit the model to the vix_cvar training data"
      ],
      "metadata": {
        "id": "ifSXEX-QAt5i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vix_cvar_arch = arch_model(train, mean='Zero', vol='ARCH', p = 1,rescale= False)"
      ],
      "metadata": {
        "id": "l5jgeAZWBWz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vix_cvar_arch_fit = vix_cvar_arch.fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "molNFcLneg4G",
        "outputId": "8fa09f72-c430-4c75-d8dc-799fa99bb9d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:      1,   Func. Count:      3,   Neg. LLF: -22741.26766900653\n",
            "Optimization terminated successfully    (Exit mode 0)\n",
            "            Current function value: -22741.267664219155\n",
            "            Iterations: 5\n",
            "            Function evaluations: 3\n",
            "            Gradient evaluations: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# obtaining the summary of the ARCH model\n",
        "vix_cvar_arch_fit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KbCb53Irg_za",
        "outputId": "783162a7-c1d1-437f-cd26-e7b8c42d5fe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Zero Mean - ARCH Model Results                        \n",
              "==============================================================================\n",
              "Dep. Variable:                   cvar   R-squared:                       0.000\n",
              "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
              "Vol Model:                       ARCH   Log-Likelihood:                22741.3\n",
              "Distribution:                  Normal   AIC:                          -45478.5\n",
              "Method:            Maximum Likelihood   BIC:                          -45464.5\n",
              "                                        No. Observations:                 8324\n",
              "Date:                Mon, Jun 19 2023   Df Residuals:                     8324\n",
              "Time:                        16:07:20   Df Model:                            0\n",
              "                              Volatility Model                              \n",
              "============================================================================\n",
              "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
              "----------------------------------------------------------------------------\n",
              "omega      1.9818e-04  3.344e-05      5.926  3.104e-09 [1.326e-04,2.637e-04]\n",
              "alpha[1]       0.5500      0.248      2.215  2.677e-02   [6.329e-02,  1.037]\n",
              "============================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "ARCHModelResult, id: 0x7f69e2eae5f0"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_res = vix_cvar_arch_fit.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "dMG_UuZ4afEz",
        "outputId": "15bf81da-12ad-421b-864e-454d11ac520c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interpretation of the results after modelling the vix return series on ARCH mean model."
      ],
      "metadata": {
        "id": "CE24ikCnkIWB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Forecasting the arch model on the test set of data vix_cvar"
      ],
      "metadata": {
        "id": "oIm04GerD1_Q"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vzGdQaBMa-1p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vix_cvar_hat_fit = vix_cvar_arch_fit.forecast(horizon = 1, start =50,reindex = False)"
      ],
      "metadata": {
        "id": "CFMmPUlii1FU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(vix_cvar_hat_fit.mean)\n",
        "print(vix_cvar_hat_fit.residual_variance)\n",
        "print(vix_cvar_hat_fit.variance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrRJPnPheenN",
        "outputId": "c83f0344-aeb4-4dfe-9df4-1944ee8c17f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      h.1\n",
            "50    0.0\n",
            "51    0.0\n",
            "52    0.0\n",
            "53    0.0\n",
            "54    0.0\n",
            "...   ...\n",
            "8319  0.0\n",
            "8320  0.0\n",
            "8321  0.0\n",
            "8322  0.0\n",
            "8323  0.0\n",
            "\n",
            "[8274 rows x 1 columns]\n",
            "           h.1\n",
            "50    0.000209\n",
            "51    0.000200\n",
            "52    0.000207\n",
            "53    0.000199\n",
            "54    0.000200\n",
            "...        ...\n",
            "8319  0.000205\n",
            "8320  0.000200\n",
            "8321  0.000207\n",
            "8322  0.000198\n",
            "8323  0.000271\n",
            "\n",
            "[8274 rows x 1 columns]\n",
            "           h.1\n",
            "50    0.000209\n",
            "51    0.000200\n",
            "52    0.000207\n",
            "53    0.000199\n",
            "54    0.000200\n",
            "...        ...\n",
            "8319  0.000205\n",
            "8320  0.000200\n",
            "8321  0.000207\n",
            "8322  0.000198\n",
            "8323  0.000271\n",
            "\n",
            "[8274 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(vix_cvar_hat_fit.variance.tail())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UKExKCzgqqY",
        "outputId": "a4aa9efc-0728-47b9-d41d-c14df5d21d1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           h.1\n",
            "8319  0.000205\n",
            "8320  0.000200\n",
            "8321  0.000207\n",
            "8322  0.000198\n",
            "8323  0.000271\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(vix_cvar_hat_fit.variance.tail())  # conditional volatility over the tail of the dist\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "OilxYMLaheNA",
        "outputId": "f4ec6bfe-b21c-46de-99e9-d728f422e994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f69ddd9a1a0>]"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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OHTsmK1askEGDBsmRI0eUmJUrV4qPj49s3bpVCgsLJT4+XkJDQ+XatWtKzOzZs2XGjBmSm5sre/fulXHjxkliYqJyfteuXTJ48GBZtmyZXLp0SXlUVlYqMefOnZMhQ4bI888/LyUlJZKWlib+/v7y6quvdrn/XMVGRER0w5W6BhmzrGX12snyGkc3p0P2fn7bnSCJiLzzzjsyYsQIUavVEhkZKfv371fOxcTESHJysk385s2bZfz48aJWqyUsLEzS09NtzlutVlm+fLnodDrRaDQya9YsKSkpsYmprKyUxMRE8fLyEq1WKwaDQWpqbvwikpOTBcAtj5iYGJvn2bdvn0RFRYlGo5HRo0fLa6+9Js3NzV3uOxMkIiKiGzYdOCcjl6bJ7Lf2OLopnbL389tFRMRhw1d9kNlshre3N0wmE7RaraObQ0RE5FBJHx3Anm8v45exE7Do0bGObk6H7P387per2IiIiKjnXalrxNcnW1avzelP84/ABImIiIju0JdHjbBYBZMDtQj183R0c7oVEyQiIiK6I+lH+tHea20wQSIiIiK7VdY2YN+pSgD9bHn/dUyQiIiIyG5fHi2DxSqYEqzFqH5WXgOYIBEREdEdSD/Sv/Zea4sJEhEREdmlsrYBOf24vAYwQSIiIiI77ThqhFWAafd4Y8SwwY5uTo9ggkRERER2ad17rb/d++hmTJCIiIioyy7XNGD/d/27vAYwQSIiIiI7tJbXpt/jjRDf/lleA5ggERERkR3SC6+vXuuHN4e8GRMkIiIi6pLymnrknq4C0L/nHwFMkIiIiKiLdhQZIQKEh/jgnqH9t7wGMEEiIiKiLkq7vnptbj8vrwFMkIiIiKgLys31OHimpbz2RD8vrwFMkIiIiKgLtl8vr907wgfBPh6Obk6PY4JEREREtzUQbg55MyZIRERE1CmjqR4Hzw6M1WutmCARERFRp7YXXYIIMHPkUAQNgPIawASJiIiIbqO1vNaftxZpiwkSERERdeiS6RoOnb0CYOCU1wAmSERERNSJbUeMAID7Rg1FgLe7g1vTe5ggERERUYeUvdcG0OgRwASJiIiIOnCx+hoOn6uGi8vAuDnkzZggERERUbu2HWmZnH3fKF/otAOnvAYwQSIiIqIOpB8ZeKvXWjFBIiIioltcuHIV+a3ltSkBjm5Or2OCRERERLfYfn31WuQoX/gPsPIawASJiIiI2pF2vbw2d9rAK68BTJCIiIiojfNVV/HN+WqoXIDYAVheA5ggERERURutq9eiQofBf8jAK68BTJCIiIiojdYEKW6AltcAJkhERER0k/NVV/HNBRNULsDsAVpeA5ggERER0U1a730UPWYY/Lw0Dm6N4zBBIiIiIkV6YUuCNGcA3hzyZneUIL377rsYNWoU3N3dERUVhQMHDnQav2XLFkycOBHu7u6YOnUqtm3bZnNeRPDyyy8jMDAQHh4e0Ov1OHHihE1MVVUVFixYAK1WCx8fH6SkpKC2tlY5n52djfj4eAQGBsLT0xPh4eFYv369zXN88skncHFxsXm4uw/MyWdERERtna2sw5HS6+W1sIFbXgPuIEHatGkTlixZghUrVuDw4cOYPn06YmNjUV5e3m78vn37kJiYiJSUFOTn5yMhIQEJCQkoKipSYlavXo23334ba9euRW5uLjw9PREbG4v6+nolZsGCBTh69CgyMjKQlpaGPXv2YOHChTavM23aNHz22WcoLCyEwWBAUlIS0tLSbNqj1Wpx6dIl5XH27Fl7fwRERET9Umt57YExfhg2gMtrAACxU2RkpCxatEj53mKxSFBQkKSmprYb//TTT0tcXJzNsaioKHnuuedERMRqtUpAQIC88cYbyvnq6mrRaDSyYcMGEREpLi4WAHLw4EElZvv27eLi4iKlpaUdtnXOnDliMBiU7z/++GPx9vbuemfbYTKZBICYTKa7eh4iIiJnM+cPe2Tk0jT5NPeso5vS7ez9/LZrBKmxsRF5eXnQ6/XKMZVKBb1ej5ycnHavycnJsYkHgNjYWCX+9OnTMBqNNjHe3t6IiopSYnJycuDj44OIiAglRq/XQ6VSITc3t8P2mkwm+Pr62hyrra3FyJEjERISgvj4eBw9erTTPjc0NMBsNts8iIiI+pvTFXU4etEMV5ULYgd4eQ2ws8RWUVEBi8UCnU5nc1yn08FoNLZ7jdFo7DS+9evtYvz9/W3Ou7m5wdfXt8PX3bx5Mw4ePAiDwaAcmzBhAj766CNs3boVf/vb32C1WvHAAw/gwoULHfY5NTUV3t7eyiMkJKTDWCIior5qm1JeGwZfT7WDW+N4/XIVW1ZWFgwGA9atW4ewsDDleHR0NJKSkhAeHo6YmBh8/vnnGD58ON5///0On2vZsmUwmUzK4/z5873RBSIiol7VunptoO691pZdCZKfnx9cXV1RVlZmc7ysrAwBAe0PxwUEBHQa3/r1djFtJ4E3NzejqqrqltfdvXs35s2bhzVr1iApKanT/gwaNAgzZszAyZMnO4zRaDTQarU2DyIiov7ku8u1KL5khpvKBY9PZnkNsDNBUqvVmDlzJjIzM5VjVqsVmZmZiI6Obvea6Ohom3gAyMjIUOJDQ0MREBBgE2M2m5Gbm6vEREdHo7q6Gnl5eUrMrl27YLVaERUVpRzLzs5GXFwcVq1aZbPCrSMWiwVHjhxBYCCzZSIiGrhay2vfG+uHoSyvAQDc7L1gyZIlSE5ORkREBCIjI/HWW2+hrq5OmeuTlJSE4OBgpKamAgAWL16MmJgYvPnmm4iLi8PGjRtx6NAhfPDBBwAAFxcXvPDCC3j11Vcxbtw4hIaGYvny5QgKCkJCQgIAYNKkSZg9ezaeffZZrF27Fk1NTXj++ecxf/58BAUFAWgpq82dOxeLFy/GU089pcxNUqvVykTtV155Bffffz/Gjh2L6upqvPHGGzh79ix+8pOf3N1PkYiIqA9Lu15eixvgN4e0cSdL5d555x0ZMWKEqNVqiYyMlP379yvnYmJiJDk52SZ+8+bNMn78eFGr1RIWFibp6ek2561Wqyxfvlx0Op1oNBqZNWuWlJSU2MRUVlZKYmKieHl5iVarFYPBIDU1Ncr55ORkAXDLIyYmRol54YUXlHbrdDqZM2eOHD582K6+c5k/ERH1JyfKamTk0jQZsyxdrtQ1OLo5Pcbez28XEREH5md9jtlshre3N0wmE+cjERFRn/d25gn8d8a3eGTCcHxiiHR0c3qMvZ/f/XIVGxEREXVNOstr7WKCRERENECdKKtBSVkNBrly9VpbTJCIiIgGqNa91x4aNxzegwc5uDXOhQkSERHRANW6vJ/ltVsxQSIiIhqAvi2rwbdltVC7qqCfrLv9BQMMEyQiIqIBqHVy9sPj/eDtwfJaW0yQiIiIBhgRUeYfzWF5rV1MkIiIiAaYb8tqcbKc5bXOMEEiIiIaYNILLwIAHh4/HFp3ltfawwSJiIhoABERpF0vr82dxvJaR5ggERERDSDHjTX47nId1G4qzJrk7+jmOC0mSERERANI6+q1R8YPxxCW1zrEBImIiGiAEJEbN4dkea1TTJCIiIgGiGOXavBdRR00birMmsTVa51hgkRERDRApB9pWb326AR/eGncHNwa58YEiYiIaAAQEWX+0RyW126LCRIREdEAcPSiGWcqr7aU1yZy9drtMEEiIiIaAFq3Fvn+RH94srx2W0yQiIiI+rmby2tcvdY1TJCIiIj6uaJSM85VXYX7IBW+z/JalzBBIiIi6uday2uzJuowWM3yWlcwQSIiIurHRERZ3s/yWtcxQSIiIurHjpSacL7qGjwGueLRCSyvdRUTJCIion6sdXL2rEn+8FC7Org1fQcTJCIion5KRJDWunptKstr9mCCRERE1E99c8GE0uprGKx2xSMsr9mFCRIREVE/lV7YMjl71iQdy2t2YoJERETUD9ncHJLlNbsxQSIiIuqH8s9X46KpHp5qVzwyYbijm9PnMEEiIiLqh7ZdHz3ST9bBfRDLa/ZigkRERNTPWK2CbUdYXrsbTJCIiIj6mdbympfGDQ+PZ3ntTjBBIiIi6mdaJ2frJ/mzvHaHmCARERH1IzbltWlBDm5N38UEiYiIqB85fO4KjOZ6DNG44aFxfo5uTp91RwnSu+++i1GjRsHd3R1RUVE4cOBAp/FbtmzBxIkT4e7ujqlTp2Lbtm0250UEL7/8MgIDA+Hh4QG9Xo8TJ07YxFRVVWHBggXQarXw8fFBSkoKamtrlfPZ2dmIj49HYGAgPD09ER4ejvXr13fYpo0bN8LFxQUJCQn2/wCIiIicVOvWIo9x9dpdsTtB2rRpE5YsWYIVK1bg8OHDmD59OmJjY1FeXt5u/L59+5CYmIiUlBTk5+cjISEBCQkJKCoqUmJWr16Nt99+G2vXrkVubi48PT0RGxuL+vp6JWbBggU4evQoMjIykJaWhj179mDhwoU2rzNt2jR89tlnKCwshMFgQFJSEtLS0m5p05kzZ/CLX/wCDz30kL3dJyIiclq25TWuXrsbLiIi9lwQFRWF++67D3/84x8BAFarFSEhIfjZz36Gl1566Zb4H/7wh6irq7NJVO6//36Eh4dj7dq1EBEEBQXhxRdfxC9+8QsAgMlkgk6nwyeffIL58+fj2LFjmDx5Mg4ePIiIiAgAwI4dOzBnzhxcuHABQUHt11jj4uKg0+nw0UcfKccsFgsefvhh/PjHP8ZXX32F6upqfPHFF13uv9lshre3N0wmE7RabZevIyIi6mkHTlfh6fdzMMTdDYd+o4fGjSNIrez9/LZrBKmxsRF5eXnQ6/U3nkClgl6vR05OTrvX5OTk2MQDQGxsrBJ/+vRpGI1Gmxhvb29ERUUpMTk5OfDx8VGSIwDQ6/VQqVTIzc3tsL0mkwm+vr42x1555RX4+/sjJSWlS31uaGiA2Wy2eRARETmj1tGjxycHMDm6S3YlSBUVFbBYLNDpdDbHdTodjEZju9cYjcZO41u/3i7G3992F2I3Nzf4+vp2+LqbN2/GwYMHYTAYlGN79+7Fhx9+iHXr1t2uq4rU1FR4e3srj5CQkC5fS0RE1FssN5XX5rK8dtf65Sq2rKwsGAwGrFu3DmFhYQCAmpoaPPPMM1i3bh38/Lo+q3/ZsmUwmUzK4/z58z3VbCIiojt26EwVymsaoHV3w/fGcvXa3XKzJ9jPzw+urq4oKyuzOV5WVoaAgIB2rwkICOg0vvVrWVkZAgMDbWLCw8OVmLaTwJubm1FVVXXL6+7evRvz5s3DmjVrkJSUpBw/deoUzpw5g3nz5inHrFYrgJbRqJKSEowZM+aW9ms0Gmg0mnb7RkRE5CzSW8trYQFQu/XL8Y9eZddPUK1WY+bMmcjMzFSOWa1WZGZmIjo6ut1roqOjbeIBICMjQ4kPDQ1FQECATYzZbEZubq4SEx0djerqauTl5Skxu3btgtVqRVRUlHIsOzsbcXFxWLVqlc0KNwCYOHEijhw5goKCAuXx5JNP4tFHH0VBQQFLZ0RE1Ge1lNdappxw9Vr3sGsECQCWLFmC5ORkREREIDIyEm+99Rbq6uqUuT5JSUkIDg5GamoqAGDx4sWIiYnBm2++ibi4OGzcuBGHDh3CBx98AABwcXHBCy+8gFdffRXjxo1DaGgoli9fjqCgIOUeRZMmTcLs2bPx7LPPYu3atWhqasLzzz+P+fPnKyvYsrKyMHfuXCxevBhPPfWUMjdJrVbD19cX7u7umDJlik1ffHx8AOCW40RERH3JgdNVqKhtgLfHIHxvDMtr3cHuBOmHP/whLl++jJdffhlGoxHh4eHYsWOHMsn63LlzUKluDEw98MAD+PTTT/Gb3/wGv/rVrzBu3Dh88cUXNknJf/7nf6Kurg4LFy5EdXU1HnzwQezYsQPu7u5KzPr16/H8889j1qxZUKlUeOqpp/D2228r5//85z/j6tWrSE1NVZIzAIiJiUF2dra93SQiIuoz0o9cBADEhulYXusmdt8HaaDjfZCIiMiZNFusuD81ExW1jfjzjyMRM364o5vklHr0PkhERETkXFrKa43wGTwID4wZ5ujm9BtMkIiIiPqw1tVrs8MCMMiVH+vdhT9JIiKiPqrZYsWOIq5e6wlMkIiIiPqo3NNVqKxrxNDBgxA9muW17sQEiYiIqI9KK7xeXpsSADeW17oVf5pERER9UEt5rSVBipsa5ODW9D9MkIiIiPqgnO8qceVqE3w91bh/tK+jm9PvMEEiIiLqg9JZXutR/IkSERH1MU0WK3YcbVm9NncqV6/1BCZIREREfUzOqUpUX22Cn5cakaEsr/UEJkhERER9DMtrPY8/VSIioj7k5vIaV6/1HCZIREREfcjXJytgutYEPy8Ny2s9iAkSERFRH9JaXntiSgBcVS4Obk3/xQSJiIioj2hstuLLo9x7rTcwQSIiIuojvj5ZAXN9M4YP0eC+USyv9SQmSERERH1E695rc1he63FMkIiIiPqAhmYL/re4tbzG1Ws9jQkSERFRH/D1yQrU1DfDf4gGESOHOro5/R4TJCIioj5AKa9NDYSK5bUexwSJiIjIyTU0W5BxtAwAMJer13oFEyQiIiIn99W3FahpaEaA1h33jmB5rTcwQSIiInJy6Ueu3xxyagDLa72ECRIREZETq2+yIKOY5bXexgSJiIjIie359jJqG5oR6O2OGSEsr/UWJkhEREROrLW8xtVrvYsJEhERkZOqb7Jg5/XyGvde611MkIiIiJzU7m8vo67RgmAfD8wI8XF0cwYUJkhEREROKl25OWQAXFxYXutNTJCIiIicUH2TBTuPtZbXuPdab2OCRERE5ISyS8px9Xp5bfo93o5uzoDDBImIiMgJte69FjctkOU1B2CCRERE5GSuNVqQeawcABA3lavXHIEJEhERkZPJKinHtSYL7hnqgWksrzkEEyQiIiInk87ymsPdUYL07rvvYtSoUXB3d0dUVBQOHDjQafyWLVswceJEuLu7Y+rUqdi2bZvNeRHByy+/jMDAQHh4eECv1+PEiRM2MVVVVViwYAG0Wi18fHyQkpKC2tpa5Xx2djbi4+MRGBgIT09PhIeHY/369TbP8fnnnyMiIgI+Pj5KzF//+tc7+REQERH1iKuNzcg8fn3vtalcveYodidImzZtwpIlS7BixQocPnwY06dPR2xsLMrLy9uN37dvHxITE5GSkoL8/HwkJCQgISEBRUVFSszq1avx9ttvY+3atcjNzYWnpydiY2NRX1+vxCxYsABHjx5FRkYG0tLSsGfPHixcuNDmdaZNm4bPPvsMhYWFMBgMSEpKQlpamhLj6+uLX//618jJyVFiDAYDvvzyS3t/DERERD0i6/hl1DdZMcJ3MKYEax3dnIFL7BQZGSmLFi1SvrdYLBIUFCSpqantxj/99NMSFxdncywqKkqee+45ERGxWq0SEBAgb7zxhnK+urpaNBqNbNiwQUREiouLBYAcPHhQidm+fbu4uLhIaWlph22dM2eOGAyGTvszY8YM+c1vftNpzM1MJpMAEJPJ1OVriIiIuurf/3ZIRi5Nk5Xbjzm6Kf2KvZ/fdo0gNTY2Ii8vD3q9XjmmUqmg1+uRk5PT7jU5OTk28QAQGxurxJ8+fRpGo9EmxtvbG1FRUUpMTk4OfHx8EBERocTo9XqoVCrk5uZ22F6TyQRfX992z4kIMjMzUVJSgocffvg2PSciIup5dQ3N2HWcq9ecgZs9wRUVFbBYLNDpdDbHdTodjh8/3u41RqOx3Xij0aicbz3WWYy/v79tw93c4Ovrq8S0tXnzZhw8eBDvv/++zXGTyYTg4GA0NDTA1dUVf/rTn/DYY4912OeGhgY0NDQo35vN5g5jiYiI7sau4+Wob7Ji1LDBCAtiec2R7EqQ+oqsrCwYDAasW7cOYWFhNueGDBmCgoIC1NbWIjMzE0uWLMHo0aPxyCOPtPtcqamp+O1vf9sLrSYiooHuxt5rXL3maHaV2Pz8/ODq6oqysjKb42VlZQgICGj3moCAgE7jW7/eLqbtJPDm5mZUVVXd8rq7d+/GvHnzsGbNGiQlJd3SHpVKhbFjxyI8PBwvvvgi/vVf/xWpqakd9nnZsmUwmUzK4/z58x3GEhER3anahmZklVwvr01jec3R7EqQ1Go1Zs6ciczMTOWY1WpFZmYmoqOj270mOjraJh4AMjIylPjQ0FAEBATYxJjNZuTm5iox0dHRqK6uRl5enhKza9cuWK1WREVFKceys7MRFxeHVatW2axw64zVarUpobWl0Wig1WptHkRERN0t81gZGpqtCPXzxORAftY4mt0ltiVLliA5ORkRERGIjIzEW2+9hbq6OhgMBgBAUlISgoODlVGZxYsXIyYmBm+++Sbi4uKwceNGHDp0CB988AEAwMXFBS+88AJeffVVjBs3DqGhoVi+fDmCgoKQkJAAAJg0aRJmz56NZ599FmvXrkVTUxOef/55zJ8/H0FBLfeIyMrKwty5c7F48WI89dRTytwktVqtTNROTU1FREQExowZg4aGBmzbtg1//etf8d57793dT5GIiOguKTeHZHnNOdzJUrl33nlHRowYIWq1WiIjI2X//v3KuZiYGElOTraJ37x5s4wfP17UarWEhYVJenq6zXmr1SrLly8XnU4nGo1GZs2aJSUlJTYxlZWVkpiYKF5eXqLVasVgMEhNTY1yPjk5WQDc8oiJiVFifv3rX8vYsWPF3d1dhg4dKtHR0bJx40a7+s5l/kRE1N3M1xpl3K+3ycilaVJ8kZ8vPcHez28XEREH5md9jtlshre3N0wmE8ttRETULbYWlGLxxgKMHu6JzCUxHEHqAfZ+fnMvNiIiIgdLu15em8vymtNggkRERORANfVN2F1yGQAQN417rzkLJkhEREQOtPNYGRotVowZ7onxOi9HN4euY4JERETkQMrqtWlBLK85ESZIREREDmK61oQ931YAAOby5pBOhQkSERGRg+wsbimvjfP3wnjdEEc3h27CBImIiMhB0o+0ltc4euRsmCARERE5gOlaE746cX312lQmSM6GCRIREZEDZBSXockimKAbgnEsrzkdJkhEREQOkF54EQDLa86KCRIREVEvM11twlcnWlavzWF5zSkxQSIiIuplXxYb0WwVTAwYgrH+vDmkM2KCRERE1MuUm0Ny9MhpMUEiIiLqRVfqGvH1yevlNc4/clpMkIiIiHrR/14vr00K1GLMcJbXnBUTJCIiol6Udr28xq1FnBsTJCIiol5ypa4R+05VAuDqNWfHBImIiKiXfHnUCItVEBakRaifp6ObQ51ggkRERNRLuPda38EEiYiIqBdU1jYo5TUu73d+TJCIiIh6wZdHy2CxCqYEazFyGMtrzo4JEhERUS9IP3J977WpQQ5uCXUFEyQiIqIeVlHbgByW1/oUJkhEREQ9bEeREVYBpt3jjRHDBju6OdQFTJCIiIh6GPde63uYIBEREfWgyzUNyD3Nm0P2NUyQiIiIetCOoy3ltekhPgjxZXmtr2CCRERE1IPSC1tWr83l6FGfwgSJiIioh5TX1CP3dBUA4ImpAQ5uDdmDCRIREVEP2VFkhAgQHuKDe4ayvNaXMEEiIiLqIWnXV6/N5d5rfQ4TJCIioh5QZq7HwTOt5TUmSH0NEyQiIqIesP3IJYgA947wQbCPh6ObQ3ZigkRERNQD0o9cvznkNO691hcxQSIiIupmRlM9Dp29AgCYw9VrfRITJCIiom62vailvBYxcigCvVle64vuKEF69913MWrUKLi7uyMqKgoHDhzoNH7Lli2YOHEi3N3dMXXqVGzbts3mvIjg5ZdfRmBgIDw8PKDX63HixAmbmKqqKixYsABarRY+Pj5ISUlBbW2tcj47Oxvx8fEIDAyEp6cnwsPDsX79epvnWLduHR566CEMHToUQ4cOhV6vv23biYiI7KXsvcbVa32W3QnSpk2bsGTJEqxYsQKHDx/G9OnTERsbi/Ly8nbj9+3bh8TERKSkpCA/Px8JCQlISEhAUVGRErN69Wq8/fbbWLt2LXJzc+Hp6YnY2FjU19crMQsWLMDRo0eRkZGBtLQ07NmzBwsXLrR5nWnTpuGzzz5DYWEhDAYDkpKSkJaWpsRkZ2cjMTERWVlZyMnJQUhICB5//HGUlpba+2MgIiJq1yXTNRw6ewUuLsATU5gg9Vlip8jISFm0aJHyvcVikaCgIElNTW03/umnn5a4uDibY1FRUfLcc8+JiIjVapWAgAB54403lPPV1dWi0Whkw4YNIiJSXFwsAOTgwYNKzPbt28XFxUVKS0s7bOucOXPEYDB0eL65uVmGDBkif/7znzvpsS2TySQAxGQydfkaIiIaOP7vV9/JyKVp8q/vfe3optBN7P38tmsEqbGxEXl5edDr9coxlUoFvV6PnJycdq/JycmxiQeA2NhYJf706dMwGo02Md7e3oiKilJicnJy4OPjg4iICCVGr9dDpVIhNze3w/aaTCb4+vp2eP7q1atoamrqNKahoQFms9nmQURE1JHWvdfieO+jPs2uBKmiogIWiwU6nc7muE6ng9FobPcao9HYaXzr19vF+Pv725x3c3ODr69vh6+7efNmHDx4EAaDocP+LF26FEFBQbckcDdLTU2Ft7e38ggJCekwloiIBrbS6ms4fK66pbzGBKlP65er2LKysmAwGLBu3TqEhYW1G7Ny5Ups3LgR//M//wN3d/cOn2vZsmUwmUzK4/z58z3VbCIi6uO2X7/30X2jfKHTdvzZQs7PzZ5gPz8/uLq6oqyszOZ4WVkZAgLav89DQEBAp/GtX8vKyhAYGGgTEx4ersS0nQTe3NyMqqqqW1539+7dmDdvHtasWYOkpKR22/T73/8eK1euxM6dOzFt2rRO+6zRaKDRaDqNISIiAm7cHJJ7r/V9do0gqdVqzJw5E5mZmcoxq9WKzMxMREdHt3tNdHS0TTwAZGRkKPGhoaEICAiwiTGbzcjNzVVioqOjUV1djby8PCVm165dsFqtiIqKUo5lZ2cjLi4Oq1atslnhdrPVq1fjd7/7HXbs2GEzp4mIiOhuXLhyFfnXy2uzp/DmkH2evbPAN27cKBqNRj755BMpLi6WhQsXio+PjxiNRhEReeaZZ+Sll15S4r/++mtxc3OT3//+93Ls2DFZsWKFDBo0SI4cOaLErFy5Unx8fGTr1q1SWFgo8fHxEhoaKteuXVNiZs+eLTNmzJDc3FzZu3evjBs3ThITE5Xzu3btksGDB8uyZcvk0qVLyqOystLmddRqtfz973+3iampqely/7mKjYiI2vPB7lMycmma/PD9fY5uCrXD3s9vuxMkEZF33nlHRowYIWq1WiIjI2X//v3KuZiYGElOTraJ37x5s4wfP17UarWEhYVJenq6zXmr1SrLly8XnU4nGo1GZs2aJSUlJTYxlZWVkpiYKF5eXqLVasVgMNgkNsnJyQLglkdMTIwSM3LkyHZjVqxY0eW+M0EiIqL2PPnHvTJyaZr8JeeMo5tC7bD389tFRMQRI1d9ldlshre3N0wmE7RaraObQ0RETuB81VU8tDoLKhcg91d6DB/CuavOxt7P7365io2IiKg3bbs+OTsqdBiTo36CCRIREdFdal29xr3X+g8mSERERHfhXOVVFF4wQcXVa/0KEyQiIqK70Dp6FD1mGPy8WF7rL5ggERER3YXW+UdxU4Mc3BLqTkyQiIiI7tDZyjocKTXBVeWC2DDd7S+gPoMJEhER0R1qLa89MGYYhrG81q8wQSIiIrpD6YWt5TWuXutvmCARERHdgdMVdTh60Xy9vMbVa/0NEyQiIqI7sO2m8tpQT7WDW0PdjQkSERHRHUi7Xl6by5tD9ktMkIiIiOx06nItjl0yw03lgscns7zWHzFBIiIistO266NH3xvrx/JaP8UEiYiIyE7ce63/Y4JERERkh5PltThurMEgVxfEsrzWbzFBIiIiskPr6rUHx/rBe/AgB7eGegoTJCIiIjsoN4ecxr3X+jMmSERERF10oqwGJWUt5bXHJnPvtf6MCRIREVEXtU7OfmjccHh7sLzWnzFBIiIi6iLuvTZwMEEiIiLqgm/LanCivBZqVxX0LK/1e0yQiIiIuqB1a5GHx/uxvDYAMEEiIiK6DRFRlvfz5pADAxMkIiKi2/i2rBYny2uhdlNBP4nltYGACRIREdFtpBdeBADEjB+OIe4srw0ETJCIiIg6ISJIu15em8vy2oDBBImIiKgTx401+O5yHdRuKsxieW3AYIJERETUidZ7Hz0yfji8NG4Obg31FiZIREREHRAR5e7ZXL02sDBBIiIi6kDxJTNOV9RBw/LagMMEiYiIqAOt9z56dII/y2sDDBMkIiKidojIjb3XWF4bcJggERERtePoRTPOVF6F+yAVvj/R39HNoV7GBImIiKgdrZOzvz/RH54srw04TJCIiIjasCmvTQ1ycGvIEe4oQXr33XcxatQouLu7IyoqCgcOHOg0fsuWLZg4cSLc3d0xdepUbNu2zea8iODll19GYGAgPDw8oNfrceLECZuYqqoqLFiwAFqtFj4+PkhJSUFtba1yPjs7G/Hx8QgMDISnpyfCw8Oxfv16m+c4evQonnrqKYwaNQouLi5466237qT7RETUzxWVmnGuqqW89ujE4Y5uDjmA3QnSpk2bsGTJEqxYsQKHDx/G9OnTERsbi/Ly8nbj9+3bh8TERKSkpCA/Px8JCQlISEhAUVGRErN69Wq8/fbbWLt2LXJzc+Hp6YnY2FjU19crMQsWLMDRo0eRkZGBtLQ07NmzBwsXLrR5nWnTpuGzzz5DYWEhDAYDkpKSkJaWpsRcvXoVo0ePxsqVKxEQEGBv14mIaIBIO9Ky99qsiToMVrO8NiCJnSIjI2XRokXK9xaLRYKCgiQ1NbXd+Kefflri4uJsjkVFRclzzz0nIiJWq1UCAgLkjTfeUM5XV1eLRqORDRs2iIhIcXGxAJCDBw8qMdu3bxcXFxcpLS3tsK1z5swRg8HQ7rmRI0fKmjVrOu9sO0wmkwAQk8lk97VEROT8rFarfG9lpoxcmibphRcd3RzqJvZ+fts1gtTY2Ii8vDzo9XrlmEqlgl6vR05OTrvX5OTk2MQDQGxsrBJ/+vRpGI1Gmxhvb29ERUUpMTk5OfDx8UFERIQSo9froVKpkJub22F7TSYTfH197ekiERENcIUXTLhw5Ro8Brni0QlcvTZQ2TVuWFFRAYvFAp3O9m6iOp0Ox48fb/cao9HYbrzRaFTOtx7rLMbf3/aP1M3NDb6+vkpMW5s3b8bBgwfx/vvvd7F37WtoaEBDQ4PyvdlsvqvnIyIi59Z6c8hZk/zhoXZ1cGvIUfrlKrasrCwYDAasW7cOYWFhd/Vcqamp8Pb2Vh4hISHd1EoiInI2IoK066vX5vLmkAOaXQmSn58fXF1dUVZWZnO8rKysw0nPAQEBnca3fr1dTNtJ4M3NzaiqqrrldXfv3o158+ZhzZo1SEpKsqd77Vq2bBlMJpPyOH/+/F0/JxEROadvLphQWn0Ng9WueITltQHNrgRJrVZj5syZyMzMVI5ZrVZkZmYiOjq63Wuio6Nt4gEgIyNDiQ8NDUVAQIBNjNlsRm5urhITHR2N6upq5OXlKTG7du2C1WpFVFSUciw7OxtxcXFYtWqVzQq3u6HRaKDVam0eRETUP6UXtqxe00/SwX0Qy2sDmd1rF5csWYLk5GREREQgMjISb731Furq6mAwGAAASUlJCA4ORmpqKgBg8eLFiImJwZtvvom4uDhs3LgRhw4dwgcffAAAcHFxwQsvvIBXX30V48aNQ2hoKJYvX46goCAkJCQAACZNmoTZs2fj2Wefxdq1a9HU1ITnn38e8+fPR1BQyw28srKyMHfuXCxevBhPPfWUMjdJrVYrE7UbGxtRXFys/HdpaSkKCgrg5eWFsWPH3sWPkYiI+jrh3mt0sztZKvfOO+/IiBEjRK1WS2RkpOzfv185FxMTI8nJyTbxmzdvlvHjx4tarZawsDBJT0+3OW+1WmX58uWi0+lEo9HIrFmzpKSkxCamsrJSEhMTxcvLS7RarRgMBqmpqVHOJycnC4BbHjExMUrM6dOnbxtzO1zmT0TUP+WdrZKRS9Nk8vLtcq2x2dHNoW5m7+e3i4iIY1KzvslsNsPb2xsmk4nlNiKifuR3acX4cO9pxIcH4Q/zZzi6OdTN7P387per2IiIiOxhtYqyvD9uKstrxASJiIgI+eev4JKpHl4aNzw8nnuvERMkIiIipBe2LOx5bDJXr1ELJkhERDSgsbxG7WGCREREA9rhc1dgNNdjiMYND433c3RzyEkwQSIiogGtdWuRx8J00LixvEYt7L5RJBFRf2O1CvLPV2P/d5UYNcwTsyb5cx7KAHFzeY17r9HNmCAR0YB0rdGCr09WIKO4DJnHy1BR26ic89K4ITYsAAkzghA9ehjcXDnY3l8dOnsF5TUNGOLuhgfHcvUa3cAEiYgGjIraBuw6Vo6MY2X46sRl1DdZlXNDNG6IHjMMRy+aUVp9DZ8dvoDPDl+An5cG86YHIiE8GNPu8YaLi4sDe0DdrXXvtccnB0DtxkSYbmCCRET92snyWuw8VoaM4jIcPncFN+8dEOzjAf0kfzw2OQCRob5Qu6lgtQryzl3B1oJSpBdeQkVtAz7++gw+/voMQv08ER8ehPjwYIT6eTquU9QtLFbBtqKW5f0sr1Fb3GrETtxqhMi5WayCvLNXsPNYGXYWl+G7ijqb81OCtXhsUgD0k/0xOVDb6YhQY7MVX524jC8KLiKj2Ggz4jT9Hm/Ehwdj7vRA+A9x77H+UM/Z/10l5n+wH1p3Nxz6zWMcQern7P385ggSEfV5VxubsefbCuw8VoZdx8tRVXdjPtEgVxdEj/HDY5P8oZ+sQ6C3R5efV+2mwqxJOsyapENtQzMyio34Iv8i9p6swDcXTPjmggmvphfje2P9EB8ejNgwHYa4D+qJLlIPaJ2cHRvG8hrdiiNIduIIEpFzKDfXI/N4OTKKy7D3ZAUam2+M7nh7DML3J/pDP0mHh8f7dXvScrmmAemFF/FFwUUUnK9WjmvcVNBP1iEhPBgx44fzQ9eJWayCqNczUVHbgE8M9+GRCf6ObhL1MHs/v5kg2YkJEpFjiAhOlNcio7hlPtHNiQkAhPh64LFJAXhssg4Ro4ZiUC+tPDtbWYetBRfxRUEpvrt8o5zn7TEIc6YGIiE8CPeN8oVKxcndziTnVCUS1+2Ht8cgHPqNvtf+XshxWGIjon6j2WLFwTNXlEnW56qu2pyfHuKDxyfroJ+kw3idl0NWmI0c5on/f9Y4/Oz7Y1FUasYXBaX45zcXUV7TgA0HzmHDgXMI9vHAvOlBiA8PwqRA/sPKGaQfaVm9NjssgMkRtYsjSHbqqRGk8pp6eHsM4l1cacCrbWjGnm8vI6O4ZT6R6VqTck7tpsKDY/2gn6SDfpI//LXOOTnaYhXs/64SX+SXYkeRETUNzcq5CbohiJ8RhCenB+GeoYMd2MqBq9lixf2pmaiobcRffhyJh8fz/kcDAUtsPaynEqRn/3IIu0suIyxYi3tHDG15jPSxa0IpUV9lNNUj4/qqs5xTlWi03JhPNHTwIHx/og6PTdbhoXF+8NT0rYHv+iYLdh0vx9aCUmQdv2zTt8hRvngyPAhxUwMx1FPtwFYOLPtOVuD/+7+58Bk8CAd/zfLaQMESWx91trIOjRYr8s9VI/9cNT7EaQBAoLc77h0xFDNG+GDGiKGYEqzlKBP1eSKCY5dqkFFchp3HynCk1GRzPtTPE49dL53NHDkUrn14/o77IFfMmRqIOVMDYbrahO1Fl/BFQSlyT1fhwJmWx2//eRQx44cjPjwY+kk6eKj5Hu9JaddXr7G8Rp3hCJKdemoESURwtvIqDp+70vI4W43jRjOsbX47alcVR5moT2qyWJH7XZUyn6i0+ppyzsUFuHfEUOgn6fDYZH+MGe6Y+US96ZLpGv75zUV8kX8RxZfMynFPtStiwwIQPyMY3xvDbU66W7PFisjXM1FV14i/pkTioXEsrw0ULLH1sN5cxVbX0IzCCyYcPncF+eeu4PC5apv7u7QK9HbHjBE+10eaOMpEzsN0rQm7r88nyi4pR039jbk47oNUeHDscDw+WYdHJ/pj+BCNA1vqWCfKavBFQSm2FlzEhSs3Ekc/Lw3mTgtEwoxgTOc2J91i74kK/NuHuRh6vbzGBHTgYILUwxy5zP/mUab8c9U4fO4KjhtrYGkzzMRRJnKkC1euYmdxGXYeK8f+7yrRfNPfp5+XGrMm6qCfrMODY/1YSmpDRHD43BV8kX8RaYUXceXqjQnqo4YNxpPhwUgID8Lo4V4ObGXftuzzI9hw4BwSI0cg9V+mOro51IuYIPUwZ7sP0tXGZnxz/vajTAFad9w7kqNM1P1EBEWlZmRcL50du6lcBABj/b2ul850CA/x6dPziXpTk6Vlm5OtBRfxv0fLcK3Jopybdo83npzeshLOWVfyOaNmixX3vbYTV642Yf1PovC9sX6ObhL1IiZIPczZEqS2RATnqq4q85i6MsrUWp4L8uEoE3VNQ7MF+7+ruj5SVIZLpnrlnMoFiBjp2zLJerKOm7p2g7qGZmQUl+GLglJ8daJCeT+rXIAHxvghPjwIs6cEcJuT2/jqxGU88+EBDPNUI/dXs1heG2CYIPUwZ0+Q2tM6ypR/viVpyj93BZUcZSI7VV9tRHZJy3yi3d9eRu1N9/YZrHbFw+OGQz9Zh+9P9Icvl6z3mIraBqQXXsLWglIcPletHFe7qaCf5I/48GA8MmE437vteOmzQmw8eB4LokbgtR+wvDbQMEHqYX0xQWrLnlGmyUFaZR4TR5kGnnOVV6+Xzow4eOaKzd+I/xANZk3S4fHJOkSPGQb3QfxA7m3nKq9ia0EpvigoxalbtjkJQHx4MCK5zQmAlpLlfa/tRPXVJnz6bBQeGMPy2kDDBKmH9YcEqT1XG2+smOv6KJMPwoK8+cHYj1itgsJSEzKKjdhZXI6Sshqb8xN0Q5TS2bRgb37wOgkRwdGLZmwtKMU/vrmIMnODci7Q2x1PTg9CfHgwJgUOGbAr4XZ/exnJHx2An5caub/Scy7cAMQEqYf11wSprZtHmVpXzB27xFGm/qi+yYJ9pyqQUVyOzGNlKK+58eHqqnJB5Chf6Cfr8NgkHUYM49YYzs5iFeR+V4kvCkqxvchoc2uF8TovxIcH48npQQjxHVi/y//8+zfYfOgC/u3+EXg1geW1gYgJUg8bKAlSe+wdZZoR0pI0cZTJ+VTVNWLX8XJkFBvx1YkKXG28sULKS+OGmPHD8dhkHR6ZMBw+gzmfqK+qb7Igu6QcX+RfxK7j5TbbnESMHIr4GcGImxrY7+eMNVmsiHh1J0zXmrDh2fsRPWaYo5tEDsAEqYcN5ASpLRHB+aprN+7+zVEmp3a6ok4pnR06W2Vzl/ZAb/eWDWAn63D/aF9O8O2HTNeasKPoEr7Iv4j9pyvR+n9+N5ULHh4/HPHhQXhssg6D1f1vB6rsknL86OOD8PPSIPdXs1heG6CYIPUwJkidu3mUqWVfuSuoqG1/lKn19gIcZeoZFqug4PwVZBS3jBTdPIkXACYHaqGf3DLJOixIO2DnpgxERlN9yzYnBaU4evHGfasGX9/m5MnwIDw01q/fLIP/5ZZvsCXvApKiR+KV+CmObg45CBOkHsYEyT53Oso0Y8RQBHm780PbTtcaLdh7sgIZxUbsOl5uk5y6qVwQPWaYMlIUzFE8AnCyvAZbC1qSpfNVN7Y5GeapxtxpgYifEYwZIT599r3Y2GxFxKsZMNc3Y9PC+xE1muW1gYoJUg9jgnT3rjY248gFEw5fn/zd0SiTTqux2S6Fo0ztu1zTgF3Hy5BRXI69Jy+jvunGPJMh7m54dII/HpusQ8yE4dDyRoLUgZZtTqqxtaAUaYWXbO7IP3LYYMRPD8KT4cEY69+3tjnJOl4OwycH4T9Eg5xlLK8NZEyQehgTpO7XdpQp/1w1ii+ZbxllGuTqgrAg7xt3/x45MEeZRASnLtcqpbP889W4+V0c7OOBxya3bO0RGeqLQf2kTEK9p8lixd4TFdhaUIov22xzMiVYi4TwYMybHgRdH9jm5MXN3+CzwxfwowdG4b+eDHN0c8iBmCD1MCZIveNaowWFF6o5ynSdxSrIO3ulZZL1sXKcrrCdTzTtHm9lv7OJAQP3XjfU/a42Xt/mJL8Ue27a5sTFBXhgzDDETw/G7KkBTjk62dBsQcSrO1FT34wtP43GfaN8Hd0kciAmSD2MCZJjtI4ytWyX0rIpb0ejTJODvHGvMgG8744y1TU046sTl5FRXI5dx8tsdnZXu6oQPWYYHpusw6xJ/gj05nwi6nmVtQ3YduQSvii4iLyzV5TjajcVZk1s2ebk0YnOs81J5rEypPz5EHRaDXJemsUbmw5wTJB6GBMk59EfR5nKzfXYeayldPb1qUo0Nt+YT+QzeBC+P8Ef+sk6PDx+OLw0/W85NvUd56tatzm5iJPltcrxIe5umDMlEPEzgnB/6DCHJiVLNhXg8/xSltcIQC8lSO+++y7eeOMNGI1GTJ8+He+88w4iIyM7jN+yZQuWL1+OM2fOYNy4cVi1ahXmzJmjnBcRrFixAuvWrUN1dTW+973v4b333sO4ceOUmKqqKvzsZz/DP//5T6hUKjz11FP4wx/+AC+vlgmD2dnZWLNmDQ4cOACz2Yxx48bhl7/8JRYsWGBXW26HCZLzEhFcuHJ9LtP1UaZjl8xoduJRJhHBt2W1yCg2IuNYOb45X21zfoTvYGU+UcTIof1m2TX1HyKC4ktmbC24iH8UXITRXK+cC9C648nwIDw5PajXbyXR0GxBxO92oqahGX//aTQiWF4b8Ho8Qdq0aROSkpKwdu1aREVF4a233sKWLVtQUlICf3//W+L37duHhx9+GKmpqZg7dy4+/fRTrFq1CocPH8aUKS33o1i1ahVSU1Px5z//GaGhoVi+fDmOHDmC4uJiuLu3TAJ84okncOnSJbz//vtoamqCwWDAfffdh08//RQA8Prrr+PatWt44oknoNPpkJaWhiVLlmDr1q2YO3dul9tyO0yQ+pbWUab889VK0lRR23BLXOsoU+u9maYE99woU7PFigNnqrCzuBwZx4w2S6sBIDzER0mKxvl79cnyIA1MFqsg93QltuZfxLaiSzbbnIz190JCeMuecL2xzcnO4jL85C+HEKB1x76Xvs/yGvV8ghQVFYX77rsPf/zjHwEAVqsVISEh+NnPfoaXXnrplvgf/vCHqKurQ1pamnLs/vvvR3h4ONauXQsRQVBQEF588UX84he/AACYTCbodDp88sknmD9/Po4dO4bJkyfj4MGDiIiIAADs2LEDc+bMwYULFxAUFNRuW+Pi4qDT6fDRRx91qS1dwQSpb3PUKFNNfRP2fNtyf6KsksswXbsxn0jjpsKDY/2gvz6fyH+I868MIrqdhmYLso5fxtaCUmQeL7cpF88cORTx4UGImxqIYV6aHnn9n28qwP/kl+LH3wvFy/Mm98hrUN9i7+e3XZMYGhsbkZeXh2XLlinHVCoV9Ho9cnJy2r0mJycHS5YssTkWGxuLL774AgBw+vRpGI1G6PV65by3tzeioqKQk5OD+fPnIycnBz4+PkpyBAB6vR4qlQq5ubn4wQ9+0O5rm0wmTJo0qcttaU9DQwMaGm6MOJjN5g5jyfm5uLggxHcwQnwHIz48GEDLKNORUpNN0lRR24Bvzlfjm/PV+PjrMwAA/yEam+1SbjfKdMl0DTuLy5BxrBw5pyrQZLmRhPl6qvH9iS33J3ponF+/3N6BBjaNmytmTwnA7CkBMNc3YUeREVsLSrHvVCXyzl5B3tkreOWfxXhonB8SZgR36zYn9U0WZBSXAQDipgV2y3PSwGPXX2NFRQUsFgt0Op3NcZ1Oh+PHj7d7jdFobDfeaDQq51uPdRbTtnzn5uYGX19fJaatzZs34+DBg3j//fe73Jb2pKam4re//W2H56nv81C7IjLUF5GhLXMUbh5lyr8+Abz4ohnlNQ3YcdSIHUdb/l5uHmWaMWIo7h3hA9O1JqV0VlRqm0yP9vPEY5Nb7mJ974ihvGEdDRha90F4OiIET0eEoMx8Y5uTolIzskouI6vkMjwGueLxMB0SwoPx4Di/u7p/155vL6O2oRlB3u6YEeLTfR2hAaVf/rM1KysLBoMB69atQ1jY3a1cWLZsmc2ok9lsRkhIyN02kZzY3Ywy2T4PMHPEUCUpGjO8b92BmKgn6LTu+MlDo/GTh0bjZHkt/nF9Jdy5qqvYWnARWwsuwrd1m5PwINw7Yqjdpe30I5cAAHOmBnLuEd0xuxIkPz8/uLq6oqyszOZ4WVkZAgIC2r0mICCg0/jWr2VlZQgMDLSJCQ8PV2LKy8ttnqO5uRlVVVW3vO7u3bsxb948rFmzBklJSXa1pT0ajQYaTc/UyKnv6Oook5urCx4eNxz6yTp8f6I//HpofgVRfzDW3wtLHp+Anz82Hvnnq7E1v2Wbk8q6Rvwl5yz+knMWIb4eiJ8ejIQZQRjrP+S2z1nfZMFOlteoG9iVIKnVasycOROZmZlISEgA0DJJOzMzE88//3y710RHRyMzMxMvvPCCciwjIwPR0dEAgNDQUAQEBCAzM1NJiMxmM3Jzc/Hv//7vynNUV1cjLy8PM2fOBADs2rULVqsVUVFRyvNmZ2dj7ty5WLVqFRYuXGh3W4i6qr1RpvomC1QuLlC7cSk+kT1cXFyUe5UtnzsZe09WYGvBRXx5tGWV5x+zTuKPWScRFnRjm5MA7/YXM2SXXEZdowXBPh4IZ3mN7obYaePGjaLRaOSTTz6R4uJiWbhwofj4+IjRaBQRkWeeeUZeeuklJf7rr78WNzc3+f3vfy/Hjh2TFStWyKBBg+TIkSNKzMqVK8XHx0e2bt0qhYWFEh8fL6GhoXLt2jUlZvbs2TJjxgzJzc2VvXv3yrhx4yQxMVE5v2vXLhk8eLAsW7ZMLl26pDwqKyvtasvtmEwmASAmk8neHx0REdmhrqFJvsi/IIaPD8iYZekycmmajFyaJqNeSpP57+fIxgNnpfpqo801z396WEYuTZNX0446qNXkrOz9/LY7QRIReeedd2TEiBGiVqslMjJS9u/fr5yLiYmR5ORkm/jNmzfL+PHjRa1WS1hYmKSnp9uct1qtsnz5ctHpdKLRaGTWrFlSUlJiE1NZWSmJiYni5eUlWq1WDAaD1NTUKOeTk5MFwC2PmJgYu9pyO0yQiIh6X2Vtg/wl54w89aevlURp5NI0GferbbLwLwdlW+FFqa5rlEnLt8vIpWmSf+6Ko5tMTsbez29uNWIn3geJiMixzlddxT++uYgv8ktx4qZtTtRuKjQ2WxHs44G9Sx/lTVbJRo/eB4mIiMjRQnwHY9GjY/Efj4zBsUs12FpQin98cxGXTC3bnMybHsTkiO4aR5DsxBEkIiLnY7UKDpypQvFFM+ZHhvDmq3QLjiAREdGAo1K54P7Rw3D/6GGObgr1E1yPTERERNQGEyQiIiKiNpggEREREbXBBImIiIioDSZIRERERG0wQSIiIiJqgwkSERERURtMkIiIiIjaYIJERERE1AYTJCIiIqI2mCARERERtcEEiYiIiKgNJkhEREREbbg5ugF9jYgAAMxms4NbQkRERF3V+rnd+jl+O0yQ7FRTUwMACAkJcXBLiIiIyF41NTXw9va+bZyLdDWVIgCA1WrFxYsXMWTIELi4uHTb85rNZoSEhOD8+fPQarXd9rzOpL/3kf3r+/p7H9m/vq+/97En+yciqKmpQVBQEFSq288w4giSnVQqFe65554ee36tVtsv/+hv1t/7yP71ff29j+xf39ff+9hT/evKyFErTtImIiIiaoMJEhEREVEbTJCchEajwYoVK6DRaBzdlB7T3/vI/vV9/b2P7F/f19/76Ez94yRtIiIiojY4gkRERETUBhMkIiIiojaYIBERERG1wQSJiIiIqA0mSHayWCxYvnw5QkND4eHhgTFjxuB3v/udzd4u//Vf/4WJEyfC09MTQ4cOhV6vR25urs3zvPbaa3jggQcwePBg+Pj4tPtamZmZeOCBBzBkyBAEBARg6dKlaG5u7rR99fX1WLRoEYYNGwYvLy889dRTKCsr6zf9e+SRR+Di4mLz+OlPf9rl/nVXH8+cOYOUlBSb51ixYgUaGxttXquwsBAPPfQQ3N3dERISgtWrV9+2fefOnUNcXBwGDx4Mf39//PKXv7ztz6Uv9a/t78/FxQUbN27scv96s4/19fX40Y9+hKlTp8LNzQ0JCQldal9VVRUWLFgArVYLHx8fpKSkoLa2tt/0b9SoUbf8DleuXOl0/cvOzkZ8fDwCAwPh6emJ8PBwrF+//rbtu9v3YF/o492+D3urfyUlJXj00Ueh0+ng7u6O0aNH4ze/+Q2ampo6bV93/A4hZJfXXntNhg0bJmlpaXL69GnZsmWLeHl5yR/+8AclZv369ZKRkSGnTp2SoqIiSUlJEa1WK+Xl5UrMyy+/LP/93/8tS5YsEW9v71tep6CgQNRqtfz2t7+VEydOSHZ2tkycOFFefPHFTtv305/+VEJCQiQzM1MOHTok999/vzzwwAP9pn8xMTHy7LPPyqVLl5SHyWTqcv+6q4/bt2+XH/3oR/Lll1/KqVOnZOvWreLv72/TfpPJJDqdThYsWCBFRUWyYcMG8fDwkPfff7/DtjU3N8uUKVNEr9dLfn6+bNu2Tfz8/GTZsmX9on8iIgDk448/tvkdXrt2rcv9680+1tbWyk9/+lP54IMPJDY2VuLj47vUvtmzZ8v06dNl//798tVXX8nYsWMlMTGx3/Rv5MiR8sorr9j8Dmtra52uf6+99pr85je/ka+//lpOnjwpb731lqhUKvnnP//ZYdu64z3o7H0Uufv3YW/179SpU/LRRx9JQUGBnDlzRonp7PfRXb9DJkh2iouLkx//+Mc2x/7lX/5FFixY0OE1JpNJAMjOnTtvOffxxx+3m0AsW7ZMIiIibI794x//EHd3dzGbze2+TnV1tQwaNEi2bNmiHDt27JgAkJycnM66pXDm/om0JEiLFy/uvBO30d19bLV69WoJDQ1Vvv/Tn/4kQ4cOlYaGBuXY0qVLZcKECR0+x7Zt20SlUonRaFSOvffee6LVam2epzPO3D+Rlv8x/8///M9tetG53urjzZKTk7uUQBQXFwsAOXjwoHJs+/bt4uLiIqWlpbe9XsS5+yfSkiCtWbOmS7HtcUT/Ws2ZM0cMBkOH57vjPSji3H0Uufv3oSP79/Of/1wefPDBDs931++QJTY7PfDAA8jMzMS3334LAPjmm2+wd+9ePPHEE+3GNzY24oMPPoC3tzemT5/e5ddpaGiAu7u7zTEPDw/U19cjLy+v3Wvy8vLQ1NQEvV6vHJs4cSJGjBiBnJycLr2uM/ev1fr16+Hn54cpU6Zg2bJluHr1apdfF+i5PppMJvj6+irf5+Tk4OGHH4ZarVaOxcbGoqSkBFeuXGn3OXJycjB16lTodDqba8xmM44ePdrn+9dq0aJF8PPzQ2RkJD766CObYfmu6K0+3omcnBz4+PggIiJCOabX66FSqW4pRXfEmfvXauXKlRg2bBhmzJiBN954w67yhSP7d7uY7ngPAs7dx1Z38z50VP9OnjyJHTt2ICYmpsOY7vodcgTJThaLRZYuXSouLi7i5uYmLi4u8vrrr98S989//lM8PT3FxcVFgoKC5MCBA+0+X0cjLF9++aWoVCr59NNPpbm5WS5cuCAPPfSQAJBPP/203edav369qNXqW47fd9998p//+Z99vn8iIu+//77s2LFDCgsL5W9/+5sEBwfLD37wgy71raf6KCJy4sQJ0Wq18sEHHyjHHnvsMVm4cKFN3NGjRwWAFBcXt/s8zz77rDz++OM2x+rq6gSAbNu2rc/3T0TklVdekb1798rhw4dl5cqVotFobIblnamPN+vqCMtrr70m48ePv+X48OHD5U9/+tNtrxdx7v6JiLz55puSlZUl33zzjbz33nvi4+MjP//5z7t0rYhj+icismnTJlGr1VJUVNRhTHe8B0Wcu48id/8+7O3+RUdHi0ajEQCycOFCsVgsHT5Pd/0OmSDZacOGDXLPPffIhg0bpLCwUP7yl7+Ir6+vfPLJJzZxtbW1cuLECcnJyZEf//jHMmrUKCkrK7vl+TpKIERa/iek1WrF1dVVBg8eLKmpqQJANm7c2G58dyRIzty/9mRmZgoAOXnyZJev6e4+XrhwQcaMGSMpKSk2xx2VIDlz/9qzfPlyueeee7ocL9J7fbxZbyZIzty/9nz44Yfi5uYm9fX1XYp3RP927dolgwcPlj//+c+dtq27PlyduY/tsfd92Nv9O3funBw9elQ+/fRTCQ4OllWrVnXYNiZIDnLPPffIH//4R5tjv/vd724772Ls2LHtZtedJRAiIlarVUpLS+Xq1avK3IaOMvDWZOHKlSs2x0eMGCH//d//3Wn7Wjlz/9pTW1srAGTHjh1dvqY7+1haWirjxo2TZ5555pZ/0TzzzDO3fODs2rVLAEhVVVW7r7F8+XKZPn26zbHvvvtOAMjhw4c7bV8rZ+5fe9LS0gRAlz9cRXqvjzfragLx4Ycfio+Pj82xpqYmcXV1lc8///y214s4d//aU1RUJADk+PHjXYrv7f5lZ2eLp6fnbRcQiHTPe1DEufvYHnvfh474G23117/+VTw8PKS5ubnd8931O+QcJDtdvXoVKpXtj83V1RVWq7XT66xWKxoaGux+PRcXFwQFBcHDwwMbNmxASEgI7r333nZjZ86ciUGDBiEzM1M5VlJSgnPnziE6OrpLr+fM/WtPQUEBACAwMLDL13RXH0tLS/HII49g5syZ+Pjjj295zujoaOzZs8dmOWpGRgYmTJiAoUOHtvsa0dHROHLkCMrLy22u0Wq1mDx5cp/vX3sKCgowdOhQuzan7K0+3ono6GhUV1fbzKXbtWsXrFYroqKiuvQczty/9hQUFEClUsHf379L8b3Zv+zsbMTFxWHVqlVYuHDhbdvWHe9BwLn72B5734eO/Bu1Wq1oamrq8LW663fIESQ7JScnS3BwsLK08fPPPxc/Pz+lhFVbWyvLli2TnJwcOXPmjBw6dEgMBoNoNBqbmvDZs2clPz9ffvvb34qXl5fk5+dLfn6+1NTUKDGrV6+WwsJCKSoqkldeeUUGDRpks+rgwoULMmHCBMnNzVWO/fSnP5URI0bIrl275NChQxIdHS3R0dH9on8nT56UV155RQ4dOiSnT5+WrVu3yujRo+Xhhx/ucv+6q48XLlyQsWPHyqxZs+TChQs2S2VbVVdXi06nk2eeeUaKiopk48aNMnjwYJt/4X3++ec2/+JqXZ76+OOPS0FBgezYsUOGDx9u1/JUZ+7fP/7xD1m3bp0cOXJETpw4IX/6059k8ODB8vLLL3e5f73ZR5GWsmF+fr7MmzdPHnnkEeVvuVVubq5MmDBBLly4oBybPXu2zJgxQ3Jzc2Xv3r0ybtw4u5b5O3P/9u3bJ2vWrJGCggI5deqU/O1vf5Phw4dLUlKS0/WvteS0bNkym/OVlZVKTE+8B529j93xPuyt/v3tb3+TTZs2SXFxsZw6dUo2bdokQUFBNqvleup3yATJTmazWRYvXiwjRowQd3d3GT16tPz6179Wlg5eu3ZNfvCDH0hQUJCo1WoJDAyUJ5988payUXJysgC45ZGVlaXEPProo+Lt7S3u7u4SFRV1S+309OnTt1xz7do1+Y//+A8ZOnSoDB48WH7wgx/c8j/Evtq/c+fOycMPPyy+vr6i0Whk7Nix8stf/tLu+yB1Rx8//vjjdvvX9t8c33zzjTz44IOi0WgkODhYVq5caXO+9XludubMGXniiSfEw8ND/Pz85MUXX5SmpqZ+0b/t27dLeHi4eHl5iaenp0yfPl3Wrl3bpWF1R/Vx5MiRncZkZWUJADl9+rRyrLKyUhITE8XLy0u0Wq0YDAabfxz05f7l5eVJVFSU8t6dNGmSvP7663aVSHurfx39fygmJuaW57nZ3b4Hnb2P3fE+7K3+bdy4Ue69916lrZMnT5bXX3/d5p5NPfU7dBGxc30tERERUT/HOUhEREREbTBBIiIiImqDCRIRERFRG0yQiIiIiNpggkRERETUBhMkIiIiojaYIBERERG1wQSJiIiIqA0mSERERERtMEEiIiIiaoMJEhEREVEbTJCIiIiI2vh/ECSEquihhO4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(vix_cvar_hat_fit.variance)  # forecasted conditional volatility for all time steps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "PJyZhjyLidxs",
        "outputId": "ac053470-b6bf-47b1-bd5b-b6e48bb2a996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f69ddcdc370>]"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cvarF = vix_cvar_hat_fit.variance\n",
        "cvarF"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "US7OWVVVa4Cy",
        "outputId": "bc78f668-8a5f-4a5e-a84e-779304a1c55c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           h.1\n",
              "50    0.000209\n",
              "51    0.000200\n",
              "52    0.000207\n",
              "53    0.000199\n",
              "54    0.000200\n",
              "...        ...\n",
              "8319  0.000205\n",
              "8320  0.000200\n",
              "8321  0.000207\n",
              "8322  0.000198\n",
              "8323  0.000271\n",
              "\n",
              "[8274 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ba71b25-1040-45b3-917d-95684e367d24\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>h.1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.000209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0.000207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.000199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8319</th>\n",
              "      <td>0.000205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8320</th>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8321</th>\n",
              "      <td>0.000207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8322</th>\n",
              "      <td>0.000198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8323</th>\n",
              "      <td>0.000271</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8274 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ba71b25-1040-45b3-917d-95684e367d24')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ba71b25-1040-45b3-917d-95684e367d24 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ba71b25-1040-45b3-917d-95684e367d24');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    }
  ]
}