MSc_Dissertation_GARCH(ver_2).ipynb

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    "colab": {
      "name": "MSc Dissertation - GARCH(ver 2).ipynb",
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      "collapsed_sections": []
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KV6Glpx7GZ7Y"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-Uh_aXNVs2U"
      },
      "source": [
        "# Generalized Autoregressive Conditional Heteroskedasticity (GARCH Model)\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gABE0OdQgtVS"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndV-sEkzta-p"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (12,6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xd3KcOIHGnPr"
      },
      "source": [
        "## Import Data"
      ]
    },
    {
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "C6b4Gqmmk1Or",
        "outputId": "0dc6bd37-1df3-4e8c-f173-ddec9bca60a5"
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      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/data/ADBL_data.csv')\n",
        "df['Date'] = pd.to_datetime(df.Date)\n",
        "# df = df[df['Date'] >= '2015-01-01']\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date   Open   High    Low  Close\n",
              "0 2012-01-01  115.0  115.0  113.0  114.0\n",
              "1 2012-01-02  114.0  118.0  115.0  118.0\n",
              "2 2012-01-03  118.0  116.0  114.0  115.0\n",
              "3 2012-01-04  115.0  115.0  113.0  114.0\n",
              "4 2012-01-05  114.0  114.0  112.0  113.0"
            ],
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
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              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
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              "      <th>0</th>\n",
              "      <td>2012-01-01</td>\n",
              "      <td>115.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-02</td>\n",
              "      <td>114.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>118.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>115.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>114.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>113.0</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-wUxT0uGtdq"
      },
      "source": [
        "## Time Series chart of ADBL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
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        "id": "CK-w6ZwDmgTM",
        "outputId": "f979a195-350c-47eb-adb9-7ef9fa8934ec"
      },
      "source": [
        "plt.plot(df[\"Date\"], df[\"Close\"])\n",
        "plt.title(\"ADBL Stock Trend\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Price\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GBwFp0O1Kkun",
        "outputId": "d364f525-236d-4768-8f59-9cf169582fcf"
      },
      "source": [
        "df = df[['Date', 'Close']]\n",
        "df['returns'] = df.Close.pct_change().dropna() * 100\n",
        "df.dropna(inplace=True)\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  Close   returns\n",
              "0    2012-01-02  118.0  3.508772\n",
              "1    2012-01-03  115.0 -2.542373\n",
              "2    2012-01-04  114.0 -0.869565\n",
              "3    2012-01-05  113.0 -0.877193\n",
              "4    2012-01-08  114.0  0.884956\n",
              "...         ...    ...       ...\n",
              "1905 2020-03-16  382.0  0.000000\n",
              "1906 2020-03-17  378.0 -1.047120\n",
              "1907 2020-03-18  374.0 -1.058201\n",
              "1908 2020-03-19  376.0  0.534759\n",
              "1909 2020-03-22  368.0 -2.127660\n",
              "\n",
              "[1910 rows x 3 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Close</th>\n",
              "      <th>returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012-01-02</td>\n",
              "      <td>118.0</td>\n",
              "      <td>3.508772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>115.0</td>\n",
              "      <td>-2.542373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>114.0</td>\n",
              "      <td>-0.869565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>113.0</td>\n",
              "      <td>-0.877193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-08</td>\n",
              "      <td>114.0</td>\n",
              "      <td>0.884956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1905</th>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>382.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1906</th>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>378.0</td>\n",
              "      <td>-1.047120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1907</th>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>374.0</td>\n",
              "      <td>-1.058201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1908</th>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>376.0</td>\n",
              "      <td>0.534759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1909</th>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>368.0</td>\n",
              "      <td>-2.127660</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1910 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8caea02-82a9-4e16-8ffc-11d8bcc38c25')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      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-b8caea02-82a9-4e16-8ffc-11d8bcc38c25 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-b8caea02-82a9-4e16-8ffc-11d8bcc38c25');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "x13EG0Y1e6u-",
        "outputId": "17251144-1d28-4ae2-dc45-6d48d35c24db"
      },
      "source": [
        "df.returns.plot()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziUEJi8L8OyG",
        "outputId": "04245a65-8831-47c5-fff3-c832a0d63ae7"
      },
      "source": [
        "df.returns.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1910.000000\n",
              "mean        0.094762\n",
              "std         2.559813\n",
              "min       -32.160804\n",
              "25%        -1.018685\n",
              "50%         0.000000\n",
              "75%         1.010101\n",
              "max        10.000000\n",
              "Name: returns, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRntopCjOyF0"
      },
      "source": [
        "pip install arch > /dev/null"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVBUChB0OHS6"
      },
      "source": [
        "from arch import arch_model\n",
        "from statsmodels.graphics.tsaplots import plot_acf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "aSTcaFPLN89A",
        "outputId": "bdbe2ee0-15a9-442a-8251-4fb267912522"
      },
      "source": [
        "# Auto correlation plot of squared stock returns\n",
        "plot_acf(df.returns**2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8e_czibGOFET"
      },
      "source": [
        "From ACF plot it can be seen there's significant positive correlation in variance out to 1 lag time steps."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jz25mK5zN9NK"
      },
      "source": [
        "## Identifying p and q terms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDgsG6bvZ7VH",
        "outputId": "6f2a1fde-49fa-4260-e03d-aa1b37b0df6b"
      },
      "source": [
        "# Optimal p and q terms based on minimal AIC\n",
        "dict_aic = {}\n",
        "for p in range(1, 15):\n",
        "  for q in range(1, 15):\n",
        "    try:\n",
        "        model = arch_model(df.returns, vol='Garch', p=p, q=q)\n",
        "        res = model.fit(disp='off')\n",
        "        dict_aic[(p, q)] = res.aic\n",
        "    except:\n",
        "        pass\n",
        "df_aic = pd.DataFrame.from_dict(dict_aic, orient='index', columns=['aic'])\n",
        "p, q = df_aic[df_aic.aic == df_aic.aic.min()].index[0]\n",
        "print(f'GARCH order is ({p}, {q})')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GARCH order is (10, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etZ-pjypiI4F"
      },
      "source": [
        "## Train/Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwXLWs78S-Wl",
        "outputId": "02d5d279-da99-476e-c07f-c5a87648cdd6"
      },
      "source": [
        "# Split Train and Test set into 70/30 percent\n",
        "X_train, X_test = train_test_split(df.returns, test_size=0.30, shuffle=False)\n",
        "print(X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1337,) (573,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "on3fN2BXTaao"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "jFH40oH7Tmk3",
        "outputId": "a0f50603-74bd-431c-9c9f-3634f432de56"
      },
      "source": [
        "model = arch_model(X_train, vol='Garch', p=p, q=q)\n",
        "model = model.fit(disp='off')\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                     Constant Mean - GARCH Model Results                      \n",
              "==============================================================================\n",
              "Dep. Variable:                returns   R-squared:                       0.000\n",
              "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
              "Vol Model:                      GARCH   Log-Likelihood:               -3173.81\n",
              "Distribution:                  Normal   AIC:                           6373.61\n",
              "Method:            Maximum Likelihood   BIC:                           6441.19\n",
              "                                        No. Observations:                 1337\n",
              "Date:                Fri, Apr 01 2022   Df Residuals:                     1336\n",
              "Time:                        10:35:20   Df Model:                            1\n",
              "                               Mean Model                               \n",
              "========================================================================\n",
              "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
              "------------------------------------------------------------------------\n",
              "mu             0.0237  8.210e-02      0.289      0.773 [ -0.137,  0.185]\n",
              "                               Volatility Model                              \n",
              "=============================================================================\n",
              "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
              "-----------------------------------------------------------------------------\n",
              "omega          2.5647      0.727      3.529  4.166e-04      [  1.140,  3.989]\n",
              "alpha[1]       0.4440      0.132      3.353  8.001e-04      [  0.184,  0.704]\n",
              "alpha[2]       0.0655  5.409e-02      1.210      0.226   [-4.057e-02,  0.171]\n",
              "alpha[3]       0.0493  7.047e-02      0.700      0.484   [-8.880e-02,  0.187]\n",
              "alpha[4]       0.0000  3.672e-03      0.000      1.000 [-7.198e-03,7.198e-03]\n",
              "alpha[5]       0.0568  4.805e-02      1.182      0.237   [-3.738e-02,  0.151]\n",
              "alpha[6]       0.0382  3.115e-02      1.226      0.220 [-2.284e-02,9.925e-02]\n",
              "alpha[7]       0.0000  3.109e-03      0.000      1.000 [-6.093e-03,6.093e-03]\n",
              "alpha[8]       0.0000  4.944e-03      0.000      1.000 [-9.690e-03,9.690e-03]\n",
              "alpha[9]       0.0000  4.816e-03      0.000      1.000 [-9.440e-03,9.440e-03]\n",
              "alpha[10]      0.2453      0.237      1.034      0.301      [ -0.220,  0.710]\n",
              "beta[1]        0.0000  4.436e-02      0.000      1.000 [-8.695e-02,8.695e-02]\n",
              "=============================================================================\n",
              "\n",
              "Covariance estimator: robust\n",
              "\"\"\""
            ],
            "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>   0.000</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>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -3173.81</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   6373.61</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   6441.19</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>1337</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Fri, Apr 01 2022</td>  <th>  Df Residuals:      </th>    <td>1336</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>10:35:20</td>      <th>  Df Model:          </th>      <td>1</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>    0.0237</td> <td>8.210e-02</td> <td>    0.289</td> <td>    0.773</td> <td>[ -0.137,  0.185]</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>    2.5647</td> <td>    0.727</td> <td>    3.529</td> <td>4.166e-04</td>    <td>[  1.140,  3.989]</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[1]</th>  <td>    0.4440</td> <td>    0.132</td> <td>    3.353</td> <td>8.001e-04</td>    <td>[  0.184,  0.704]</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[2]</th>  <td>    0.0655</td> <td>5.409e-02</td> <td>    1.210</td> <td>    0.226</td>  <td>[-4.057e-02,  0.171]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[3]</th>  <td>    0.0493</td> <td>7.047e-02</td> <td>    0.700</td> <td>    0.484</td>  <td>[-8.880e-02,  0.187]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[4]</th>    <td>0.0000</td>   <td>3.672e-03</td>   <td>0.000</td>   <td>    1.000</td> <td>[-7.198e-03,7.198e-03]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[5]</th>  <td>    0.0568</td> <td>4.805e-02</td> <td>    1.182</td> <td>    0.237</td>  <td>[-3.738e-02,  0.151]</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[6]</th>  <td>    0.0382</td> <td>3.115e-02</td> <td>    1.226</td> <td>    0.220</td> <td>[-2.284e-02,9.925e-02]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[7]</th>    <td>0.0000</td>   <td>3.109e-03</td>   <td>0.000</td>   <td>    1.000</td> <td>[-6.093e-03,6.093e-03]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[8]</th>    <td>0.0000</td>   <td>4.944e-03</td>   <td>0.000</td>   <td>    1.000</td> <td>[-9.690e-03,9.690e-03]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[9]</th>    <td>0.0000</td>   <td>4.816e-03</td>   <td>0.000</td>   <td>    1.000</td> <td>[-9.440e-03,9.440e-03]</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha[10]</th> <td>    0.2453</td> <td>    0.237</td> <td>    1.034</td> <td>    0.301</td>    <td>[ -0.220,  0.710]</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>beta[1]</th>     <td>0.0000</td>   <td>4.436e-02</td>   <td>0.000</td>   <td>    1.000</td> <td>[-8.695e-02,8.695e-02]</td>\n",
              "</tr>\n",
              "</table><br/><br/>Covariance estimator: robust"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SP3gY9TbN1S0"
      },
      "source": [
        "## Rolling Forecast on Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6dsz6YlbHxL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71dc62b8-e198-48da-edd0-e820233e9d38"
      },
      "source": [
        "# Predicting volatility based on test set on a rolling basis\n",
        "forecasts = list()\n",
        "for i in range(X_test.shape[0]):\n",
        "    x_test = df.returns[:-(X_test.shape[0] -i)]\n",
        "    model = arch_model(x_test, p=p, q=q)\n",
        "    model = model.fit(disp='off')\n",
        "    forecast = model.forecast(horizon=1)\n",
        "    forecasts.append(np.sqrt(forecast.variance.values[-1,:][0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/arch/univariate/base.py:755: ConvergenceWarning: The optimizer returned code 8. The message is:\n",
            "Positive directional derivative for linesearch\n",
            "See scipy.optimize.fmin_slsqp for code meaning.\n",
            "\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqZFgOCqg0WV"
      },
      "source": [
        "## Loss Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUipTxORhAoQ"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCSVUsz9hDex",
        "outputId": "21b11021-8844-4d44-cc98-67482982b950"
      },
      "source": [
        "# calculate root mean squared error\n",
        "testScore = mean_squared_error(X_test, forecasts, squared=False)\n",
        "print(\"Test Score: %.2f RMSE\" % (testScore))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Score: 2.93 RMSE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating closing price from forecasted returns with reference to actual closing price in the last index of training set\n",
        "# Todays value = yesterdays value + (pct_returns * yesterdays value) / 100\n",
        "pred_vals = []\n",
        "for i in range(len(forecasts)):\n",
        "  if pred_vals:\n",
        "    pred_val = pred_vals[i-1] + (forecasts[i] * pred_vals[i-1]) / 100\n",
        "  else:\n",
        "    pred_val = df.Close[X_train.shape[0]] + (forecasts[i] * df.Close[X_train.shape[0]]) / 100\n",
        "  pred_vals.append(pred_val)"
      ],
      "metadata": {
        "id": "bNf0KeadmSsm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate root mean squared error of actual predicted values with reference to closing price\n",
        "testScore = mean_squared_error(df.Close[X_train.shape[0]:], pred_vals, squared=False)\n",
        "print(\"Test Score: %.2f RMSE\" % (testScore))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOQRvDXcmUqy",
        "outputId": "98783c8e-9c18-4322-ab10-8c05a8e17fc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Score: 12123349.65 RMSE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSro-LfVflaT"
      },
      "source": [
        "date_range = df['Date'][-X_test.shape[0]:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "i-NLlB5_gIc5",
        "outputId": "ece49072-10ac-4743-ac2e-dea79ced0210"
      },
      "source": [
        "plt.title('Predicting Volatility on test set', fontsize=14)\n",
        "plt.plot(date_range, X_test, label='Actual returns')\n",
        "plt.plot(date_range, forecasts, label='Predicted Volatility')\n",
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('Returns', fontsize=14)\n",
        "plt.legend(fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}