Volatility_Forecasting_with_Univariate_Vol.ipynb

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      "name": "Volatility_Forecasting_with_Univariate_Vol.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FinancialEngineerLab/finefinance/blob/master/Volatility_Forecasting_with_Univariate_Vol.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mkz2GSwU4rMo"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Volatility Forecasting \n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Author : Hyunjin Shin\n",
        "\n",
        "# Analytical\n",
        "# Simulaton\n",
        "# Bootstrap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2tkqygS5JqQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "outputId": "054f5367-6c3e-44ab-cb99-ecce4a3d1bfa"
      },
      "source": [
        "!pip install arch\n",
        "!pip install Quandl\n",
        "!pip install -U finance_datareader"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: arch in /usr/local/lib/python3.6/dist-packages (4.14)\n",
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            "Requirement already up-to-date: finance_datareader in /usr/local/lib/python3.6/dist-packages (0.9.6)\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.19.2 in /usr/local/lib/python3.6/dist-packages (from finance_datareader) (1.0.4)\n",
            "Requirement already satisfied, skipping upgrade: lxml in /usr/local/lib/python3.6/dist-packages (from finance_datareader) (4.2.6)\n",
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            "Requirement already satisfied, skipping upgrade: requests-file in /usr/local/lib/python3.6/dist-packages (from finance_datareader) (1.5.1)\n",
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            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->finance_datareader) (2020.4.5.1)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzGdx87yAbuD"
      },
      "source": [
        "### Load Library ###"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYCzc1zY4iPY"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sys\n",
        "import statsmodels\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tsa.stattools import coint, adfuller\n",
        "import matplotlib.pyplot as plt\n",
        "import FinanceDataReader as fdr\n",
        "import warnings\n",
        "import seaborn as sns\n",
        "import datetime as dt\n",
        "import scipy.stats as ss\n",
        "import quandl\n",
        "import io\n",
        "from arch import arch_model\n",
        "from pylab import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0o59AX1iAZ59"
      },
      "source": [
        "### Data Preprocessing ###"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3R9bzxb5ONd"
      },
      "source": [
        "### Data ###\n",
        "\n",
        "now = datetime.datetime.today().strftime('%Y-%m-%d')\n",
        "\n",
        "def dataframe(ticker, start_date = '2008-01-01',end_date= now):\n",
        "    # Series Generation\n",
        "    df_list_1 = fdr.DataReader(ticker,  \"2008-01-01\",\"2008-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_2 = fdr.DataReader(ticker,  \"2009-01-01\",\"2009-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_3 = fdr.DataReader(ticker,  \"2010-01-01\",\"2010-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_4 = fdr.DataReader(ticker,  \"2011-01-01\",\"2011-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_5 = fdr.DataReader(ticker,  \"2012-01-01\",\"2012-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_6 = fdr.DataReader(ticker,  \"2013-01-01\",\"2013-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_7 = fdr.DataReader(ticker,  \"2014-01-01\",\"2014-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_8 = fdr.DataReader(ticker, \"2015-01-01\",\"2015-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_9 =fdr.DataReader(ticker,  \"2016-01-01\",\"2016-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_10 =fdr.DataReader(ticker, \"2017-01-01\",\"2017-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_11 =fdr.DataReader(ticker, \"2018-01-01\",\"2018-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_12 =fdr.DataReader(ticker, \"2019-01-01\",\"2019-12-31\")[['Open','High','Low','Close']]\n",
        "    df_list_13 =fdr.DataReader(ticker, \"2020-01-01\")[['Open','High','Low','Close']]\n",
        "                \n",
        "    ### data frame preprocessing ###\n",
        "    # axis transformation\n",
        "    # axis = 1이면 cbind, axis = 0이면 rbind\n",
        "    df_total = pd.concat([df_list_1, df_list_2, df_list_3, df_list_4,df_list_5, df_list_6, df_list_7, df_list_8,\n",
        "                          df_list_9, df_list_10, df_list_11, df_list_12, df_list_13])\n",
        "    df_total = df_total[(df_total.index >= start_date) & (df_total.index < end_date)]\n",
        "    # NA cleaning\n",
        "    df_raw0 = df_total[['Open','High','Low']]\n",
        "    df_raw = df_total['Close']\n",
        "    df_raw = df_raw.fillna(method = 'ffill')\n",
        "    df_raw0 = df_raw0.fillna(method = 'ffill')\n",
        "    df = pd.concat([df_raw0,df_raw], axis =1)\n",
        "\n",
        "    df.columns = [\"Open\",\"High\",\"Low\",\"Close\"]\n",
        "    df['returns'] = df.Close.pct_change().dropna()\n",
        "\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6sexoCMy5OLn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "d3ecb985-2050-4347-c749-87ed6618d9cb"
      },
      "source": [
        "data = dataframe('KS200', start_date = '2017-01-01')\n",
        "data.describe()\n",
        "data.tail(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>returns</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-05-29</th>\n",
              "      <td>266.62</td>\n",
              "      <td>269.61</td>\n",
              "      <td>265.23</td>\n",
              "      <td>268.32</td>\n",
              "      <td>0.000112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>269.29</td>\n",
              "      <td>273.39</td>\n",
              "      <td>268.93</td>\n",
              "      <td>273.19</td>\n",
              "      <td>0.018150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-02</th>\n",
              "      <td>272.65</td>\n",
              "      <td>276.47</td>\n",
              "      <td>272.22</td>\n",
              "      <td>276.08</td>\n",
              "      <td>0.010579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-03</th>\n",
              "      <td>279.14</td>\n",
              "      <td>287.49</td>\n",
              "      <td>278.98</td>\n",
              "      <td>285.91</td>\n",
              "      <td>0.035606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-04</th>\n",
              "      <td>291.17</td>\n",
              "      <td>292.90</td>\n",
              "      <td>284.93</td>\n",
              "      <td>286.45</td>\n",
              "      <td>0.001889</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open    High     Low   Close   returns\n",
              "Date                                                \n",
              "2020-05-29  266.62  269.61  265.23  268.32  0.000112\n",
              "2020-06-01  269.29  273.39  268.93  273.19  0.018150\n",
              "2020-06-02  272.65  276.47  272.22  276.08  0.010579\n",
              "2020-06-03  279.14  287.49  278.98  285.91  0.035606\n",
              "2020-06-04  291.17  292.90  284.93  286.45  0.001889"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wj5OfqQu_qOJ"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "# Basic Forecasting #\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "ARCH Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCR9y3ILChdl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "bea28a07-55d2-4963-b31a-34259c6cef05"
      },
      "source": [
        "returns = data['returns']\n",
        "returns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2017-01-02         NaN\n",
              "2017-01-03    0.010025\n",
              "2017-01-04    0.000570\n",
              "2017-01-05   -0.004333\n",
              "2017-01-06    0.004657\n",
              "                ...   \n",
              "2020-05-29    0.000112\n",
              "2020-06-01    0.018150\n",
              "2020-06-02    0.010579\n",
              "2020-06-03    0.035606\n",
              "2020-06-04    0.001889\n",
              "Name: returns, Length: 838, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8bdSWM15OJJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "656eae13-1091-4bf7-dac9-13a1fea47250"
      },
      "source": [
        "returns = data['returns'].dropna()\n",
        "price = data['Close']\n",
        "# ARCH (1,0,1)\n",
        "am = arch_model(price, vol='Garch', p=1, o=0, q=1, dist='Normal')\n",
        "res = am.fit(update_freq=5)\n",
        "forecasts = res.forecast(horizon=5)\n",
        "\n",
        "print(price.iloc[-2:])\n",
        "print(forecasts.mean.iloc[-2:])\n",
        "print(forecasts.residual_variance.iloc[-2:])\n",
        "print(forecasts.variance.iloc[-2:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration:      5,   Func. Count:     35,   Neg. LLF: 3492.543318327739\n",
            "Iteration:     10,   Func. Count:     69,   Neg. LLF: 3469.8481793851824\n",
            "Iteration:     15,   Func. Count:    100,   Neg. LLF: 3468.346317285207\n",
            "Iteration:     20,   Func. Count:    130,   Neg. LLF: 3468.212900962939\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 3468.21122114012\n",
            "            Iterations: 22\n",
            "            Function evaluations: 142\n",
            "            Gradient evaluations: 22\n",
            "Date\n",
            "2020-06-03    285.91\n",
            "2020-06-04    286.45\n",
            "Name: Close, dtype: float64\n",
            "                   h.1         h.2         h.3         h.4         h.5\n",
            "Date                                                                  \n",
            "2020-06-03         NaN         NaN         NaN         NaN         NaN\n",
            "2020-06-04  277.322827  277.322827  277.322827  277.322827  277.322827\n",
            "                  h.1        h.2        h.3         h.4         h.5\n",
            "Date                                                               \n",
            "2020-06-03        NaN        NaN        NaN         NaN         NaN\n",
            "2020-06-04  86.314174  90.994896  95.643038  100.258824  104.842482\n",
            "                  h.1        h.2        h.3         h.4         h.5\n",
            "Date                                                               \n",
            "2020-06-03        NaN        NaN        NaN         NaN         NaN\n",
            "2020-06-04  86.314174  90.994896  95.643038  100.258824  104.842482\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}