02-autocorrelation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Open      High       Low     Close      Volume  \\\n",
      "DateTime                                                                  \n",
      "2022-01-25 13:00:00  36519.12  36579.99  36259.10  36498.00  2008.17575   \n",
      "2022-01-25 14:00:00  36497.39  37149.42  36252.00  36299.99  5493.26524   \n",
      "2022-01-25 15:00:00  36299.99  36780.00  35967.33  36530.00  3943.20294   \n",
      "2022-01-25 16:00:00  36530.00  37086.64  36492.48  36837.70  2613.07831   \n",
      "2022-01-25 17:00:00  36837.70  37481.75  36800.00  37273.33  3299.07838   \n",
      "\n",
      "                      log_ret  fracdiff  \n",
      "DateTime                                 \n",
      "2022-01-25 13:00:00 -0.000579  0.103441  \n",
      "2022-01-25 14:00:00 -0.005440  0.097746  \n",
      "2022-01-25 15:00:00  0.006316  0.107048  \n",
      "2022-01-25 16:00:00  0.008388  0.112089  \n",
      "2022-01-25 17:00:00  0.011756  0.118196  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "data_path = os.path.join(\"eDo\", \"data\", \"01.csv\")\n",
    "df = pd.read_csv(data_path, parse_dates=['DateTime'], index_col='DateTime')\n",
    "\n",
    "df = df.dropna()\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size: 12373, Test size: 3094\n"
     ]
    }
   ],
   "source": [
    "train_size = int(len(df) * 0.8)\n",
    "train_df = df.iloc[:train_size].copy()\n",
    "test_df  = df.iloc[train_size:].copy()\n",
    "\n",
    "print(f\"Train size: {len(train_df)}, Test size: {len(test_df)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(4, 1, figsize=(12, 8))\n",
    "\n",
    "plot_acf(train_df['log_ret'], ax=axes[0], lags=20, title=\"ACF of log returns\")\n",
    "plot_pacf(train_df['log_ret'], ax=axes[1], lags=20, title=\"PACF of log returns\")\n",
    "plot_acf(train_df['fracdiff'], ax=axes[2], lags=20, title=\"ACF of fracdiff\")\n",
    "plot_pacf(train_df['fracdiff'], ax=axes[3], lags=20, title=\"PACF of fracdiff\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,0,1)(0,0,0)[0]             : AIC=-90413.563, Time=0.42 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=-90417.536, Time=0.40 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=-90415.536, Time=0.39 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0]             : AIC=-90415.536, Time=0.38 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=-90415.600, Time=1.16 sec\n",
      "\n",
      "Best model:  ARIMA(0,0,0)(0,0,0)[0]          \n",
      "Total fit time: 2.750 seconds\n",
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                12373\n",
      "Model:                        SARIMAX   Log Likelihood               45209.768\n",
      "Date:                Sat, 04 Jan 2025   AIC                         -90417.536\n",
      "Time:                        20:27:00   BIC                         -90410.112\n",
      "Sample:                    01-25-2022   HQIC                        -90415.049\n",
      "                         - 06-25-2023                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "sigma2      3.924e-05   1.76e-07    222.822      0.000    3.89e-05    3.96e-05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            101797.31\n",
      "Prob(Q):                              0.98   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.46   Skew:                            -0.11\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        17.05\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "from pmdarima import auto_arima\n",
    "\n",
    "stepwise_model_logret = auto_arima(\n",
    "    train_df['log_ret'],\n",
    "    start_p=1, start_q=1,\n",
    "    max_p=5, max_q=5,\n",
    "    d=0,           # because we assume log_ret is already differenced\n",
    "    seasonal=False,\n",
    "    trace=True,\n",
    "    error_action='ignore',\n",
    "    suppress_warnings=True,\n",
    "    stepwise=True\n",
    ")\n",
    "\n",
    "print(stepwise_model_logret.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA(1,0,1)(0,0,0)[0]             : AIC=-89680.804, Time=0.77 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=-21933.312, Time=0.34 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=inf, Time=0.53 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0]             : AIC=-37443.070, Time=0.58 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0]             : AIC=-90200.187, Time=1.42 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0]             : AIC=inf, Time=0.49 sec\n",
      " ARIMA(3,0,1)(0,0,0)[0]             : AIC=-88815.111, Time=1.11 sec\n",
      " ARIMA(2,0,2)(0,0,0)[0]             : AIC=-89757.001, Time=0.85 sec\n",
      " ARIMA(1,0,2)(0,0,0)[0]             : AIC=-89879.995, Time=0.93 sec\n",
      " ARIMA(3,0,0)(0,0,0)[0]             : AIC=inf, Time=0.60 sec\n",
      " ARIMA(3,0,2)(0,0,0)[0]             : AIC=-89945.325, Time=0.99 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=-89936.645, Time=8.33 sec\n",
      "\n",
      "Best model:  ARIMA(2,0,1)(0,0,0)[0]          \n",
      "Total fit time: 16.941 seconds\n",
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                12373\n",
      "Model:               SARIMAX(2, 0, 1)   Log Likelihood               45104.093\n",
      "Date:                Sat, 04 Jan 2025   AIC                         -90200.187\n",
      "Time:                        20:27:17   BIC                         -90170.493\n",
      "Sample:                    01-25-2022   HQIC                        -90190.241\n",
      "                         - 06-25-2023                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          1.3092      0.006    227.782      0.000       1.298       1.320\n",
      "ar.L2         -0.3095      0.006    -53.936      0.000      -0.321      -0.298\n",
      "ma.L1         -0.8810      0.004   -239.555      0.000      -0.888      -0.874\n",
      "sigma2      3.984e-05    1.8e-07    221.029      0.000    3.95e-05    4.02e-05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   3.08   Jarque-Bera (JB):             98054.58\n",
      "Prob(Q):                              0.08   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.46   Skew:                            -0.00\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        16.79\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "stepwise_model_fracdiff = auto_arima(\n",
    "    train_df['fracdiff'],\n",
    "    start_p=1, start_q=1,\n",
    "    max_p=5, max_q=5,\n",
    "    d=0,           # because its already fractionally differenced\n",
    "    seasonal=False,\n",
    "    trace=True,\n",
    "    error_action='ignore',\n",
    "    suppress_warnings=True,\n",
    "    stepwise=True\n",
    ")\n",
    "\n",
    "print(stepwise_model_fracdiff.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:465: ValueWarning: No frequency information was provided, so inferred frequency h will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:465: ValueWarning: No frequency information was provided, so inferred frequency h will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:465: ValueWarning: No frequency information was provided, so inferred frequency h will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:               fracdiff   No. Observations:                12373\n",
      "Model:                 ARIMA(2, 0, 1)   Log Likelihood               45094.766\n",
      "Date:                Sat, 04 Jan 2025   AIC                         -90179.533\n",
      "Time:                        20:27:19   BIC                         -90142.416\n",
      "Sample:                    01-25-2022   HQIC                        -90167.101\n",
      "                         - 06-25-2023                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0994      0.000    293.655      0.000       0.099       0.100\n",
      "ar.L1          1.0915      0.013     80.873      0.000       1.065       1.118\n",
      "ar.L2         -0.1532      0.011    -14.397      0.000      -0.174      -0.132\n",
      "ma.L1         -0.6384      0.013    -49.491      0.000      -0.664      -0.613\n",
      "sigma2      3.997e-05   1.84e-07    216.802      0.000    3.96e-05    4.03e-05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                  15.67   Jarque-Bera (JB):             95064.38\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.45   Skew:                            -0.15\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        16.58\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:465: ValueWarning: No frequency information was provided, so inferred frequency h will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:465: ValueWarning: No frequency information was provided, so inferred frequency h will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:465: ValueWarning: No frequency information was provided, so inferred frequency h will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                log_ret   No. Observations:                12373\n",
      "Model:                          ARIMA   Log Likelihood               45209.790\n",
      "Date:                Sat, 04 Jan 2025   AIC                         -90415.580\n",
      "Time:                        20:27:22   BIC                         -90400.734\n",
      "Sample:                    01-25-2022   HQIC                        -90410.607\n",
      "                         - 06-25-2023                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -1.932e-05   5.64e-05     -0.343      0.732      -0.000    9.13e-05\n",
      "sigma2       3.93e-05   1.77e-07    222.415      0.000     3.9e-05    3.96e-05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            101797.31\n",
      "Prob(Q):                              0.98   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.46   Skew:                            -0.11\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        17.05\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:612: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "p, d, q = (2, 0, 1)\n",
    "fracdiff_model = ARIMA(train_df['fracdiff'], order=(p, d, q)).fit()\n",
    "print(fracdiff_model.summary())\n",
    "\n",
    "p, d, q = (0, 0, 0)\n",
    "log_ret_model = ARIMA(train_df['log_ret'], order=(p, d, q)).fit()\n",
    "print(log_ret_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "import statsmodels.api as sm\n",
    "\n",
    "residuals_fracdiff = fracdiff_model.resid\n",
    "residuals_log_ret = log_ret_model.resid.dropna()\n",
    "\n",
    "fig, axes = plt.subplots(4, 2, figsize=(20, 16))\n",
    "\n",
    "# ACF and PACF for Fracdiff residuals\n",
    "plot_acf(residuals_fracdiff, ax=axes[0, 0], lags=40, title=\"ACF (Fracdiff)\")\n",
    "plot_pacf(residuals_fracdiff, ax=axes[1, 0], lags=40, title=\"PACF (Fracdiff)\")\n",
    "\n",
    "# ACF and PACF for log returns residuals\n",
    "plot_acf(residuals_log_ret, ax=axes[0, 1], lags=40, title=\"ACF (log returns)\")\n",
    "plot_pacf(residuals_log_ret, ax=axes[1, 1], lags=40, title=\"PACF (log returns)\")\n",
    "\n",
    "# Q-Q Plots\n",
    "sm.qqplot(residuals_fracdiff, line='s', ax=axes[2, 0])\n",
    "axes[2, 0].set_title(\"Q-Q Plot (Fracdiff)\")\n",
    "sm.qqplot(residuals_log_ret, line='s', ax=axes[2, 1])\n",
    "axes[2, 1].set_title(\"Q-Q Plot (log returns)\")\n",
    "\n",
    "# Residual Histograms\n",
    "axes[3, 0].hist(residuals_fracdiff, bins=90, density=True, alpha=0.7, color='blue', edgecolor='black')\n",
    "axes[3, 0].set_title(\"Histogram (Fracdiff)\")\n",
    "axes[3, 0].set_xlabel(\"Residuals\")\n",
    "axes[3, 0].set_ylabel(\"Density\")\n",
    "\n",
    "axes[3, 1].hist(residuals_log_ret, bins=90, density=True, alpha=0.7, color='blue', edgecolor='black')\n",
    "axes[3, 1].set_title(\"Histogram (log returns)\")\n",
    "axes[3, 1].set_xlabel(\"Residuals\")\n",
    "axes[3, 1].set_ylabel(\"Density\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Residual Time Series Plots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(20, 6))\n",
    "\n",
    "# Residuals over time for Fracdiff\n",
    "axes[0].plot(residuals_fracdiff, label='Residuals', color='red')\n",
    "axes[0].axhline(y=0, color='black', linestyle='--', linewidth=0.8)\n",
    "axes[0].set_title(\"Residuals Over Time (Fracdiff)\")\n",
    "axes[0].set_xlabel(\"Time\")\n",
    "axes[0].set_ylabel(\"Residuals\")\n",
    "axes[0].legend()\n",
    "\n",
    "# Residuals over time for log returns\n",
    "axes[1].plot(residuals_log_ret, label='Residuals', color='red')\n",
    "axes[1].axhline(y=0, color='black', linestyle='--', linewidth=0.8)\n",
    "axes[1].set_title(\"Residuals Over Time (log returns)\")\n",
    "axes[1].set_xlabel(\"Time\")\n",
    "axes[1].set_ylabel(\"Residuals\")\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GARCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "plot_acf(train_df['log_ret']**2, lags=40, \n",
    "         title=\"ACF of Squared Log Returns\", ax=axes[0])\n",
    "\n",
    "plot_acf(train_df['fracdiff']**2, lags=40, \n",
    "         title=\"ACF of Squared Fracdiff\", ax=axes[1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means the volatility has tendency to cluster and is present for some time after an event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARCH test: (5684.996101152077, 0.0, 1051.5278652883815, 0.0)\n",
      "ARCH test: (911.1998816453887, 2.4740270407517e-189, 98.28272255681819, 8.019400539957793e-197)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.diagnostic import het_arch\n",
    "\n",
    "arch_test = het_arch(train_df['fracdiff'])\n",
    "# arch_test returns (LM stat, LM p-value, F-stat, F p-value)\n",
    "print(\"ARCH test:\", arch_test)\n",
    "\n",
    "arch_test = het_arch(train_df['log_ret'])\n",
    "print(\"ARCH test:\", arch_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA + GARCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best GARCH model order: p=1, q=5 with AIC=20283.122070845075\n",
      "                       Zero Mean - GARCH Model Results                        \n",
      "==============================================================================\n",
      "Dep. Variable:                   None   R-squared:                       0.000\n",
      "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                      GARCH   Log-Likelihood:               -10134.6\n",
      "Distribution:                  Normal   AIC:                           20283.1\n",
      "Method:            Maximum Likelihood   BIC:                           20335.1\n",
      "                                        No. Observations:                12373\n",
      "Date:                Sat, Jan 04 2025   Df Residuals:                    12373\n",
      "Time:                        20:27:24   Df Model:                            0\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega          0.0155  5.673e-03      2.734  6.253e-03 [4.392e-03,2.663e-02]\n",
      "alpha[1]       0.1800  3.647e-02      4.934  8.069e-07     [  0.108,  0.251]\n",
      "beta[1]        0.4464      0.275      1.624      0.104  [-9.218e-02,  0.985]\n",
      "beta[2]        0.0000      0.598      0.000      1.000     [ -1.173,  1.173]\n",
      "beta[3]        0.0511      0.701  7.295e-02      0.942     [ -1.323,  1.425]\n",
      "beta[4]        0.1022      0.542      0.189      0.850     [ -0.959,  1.164]\n",
      "beta[5]        0.1964      0.236      0.833      0.405     [ -0.266,  0.658]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "from arch import arch_model\n",
    "\n",
    "best_aic = np.inf\n",
    "best_order = None\n",
    "best_model = None\n",
    "\n",
    "p_range = range(1, 6)\n",
    "q_range = range(1, 6)\n",
    "\n",
    "arima_resid  = fracdiff_model.resid.dropna()*100\n",
    "\n",
    "for p, q in itertools.product(p_range, q_range):\n",
    "\n",
    "        model = arch_model(arima_resid, mean='Zero', vol='GARCH', p=p, q=q)\n",
    "        res = model.fit(disp='off')\n",
    "        if res.aic < best_aic:\n",
    "            best_aic = res.aic\n",
    "            best_order = (p, q)\n",
    "            best_model = res\n",
    "\n",
    "print(f\"Best GARCH model order: p={best_order[0]}, q={best_order[1]} with AIC={best_aic}\")\n",
    "print(best_model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_test = 10\n",
    "\n",
    "# ARIMA forecast object\n",
    "forecast_obj = fracdiff_model.get_forecast(steps=n_test)\n",
    "\n",
    "# ARIMA point forecast (mean)\n",
    "arima_mean_pred = forecast_obj.predicted_mean\n",
    "\n",
    "# Confidence intervals for ARIMA forecast\n",
    "arima_conf_int = forecast_obj.conf_int(alpha=0.05)  \n",
    "# alpha=0.05 for 95% CI (the default), returns a DataFrame with columns ['lower fracdiff', 'upper fracdiff']\n",
    "\n",
    "# You can extract them for plotting (assuming the column name is 'lower fracdiff' / 'upper fracdiff'):\n",
    "lower_ci = arima_conf_int.iloc[:, 0]\n",
    "upper_ci = arima_conf_int.iloc[:, 1]\n",
    "\n",
    "# GARCH forecast\n",
    "garch_vol_forecast = best_model.forecast(horizon=n_test, reindex=False)\n",
    "out_of_sample_vol = garch_vol_forecast.variance.values[-1,:] ** 0.5  # sqrt of variance\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10,6))\n",
    "\n",
    "# --- Left axis for ARIMA forecast, actual data, etc. --- #\n",
    "ax1.set_title(\"ARIMA Forecast + GARCH Volatility (Secondary Axis)\")\n",
    "\n",
    "# Plot test data\n",
    "ax1.plot(\n",
    "    np.arange(train_size, train_size + n_test),\n",
    "    test_df['fracdiff'].values[:n_test],\n",
    "    label='Test (fracdiff)',\n",
    "    color='orange'\n",
    ")\n",
    "\n",
    "# Plot the ARIMA mean forecast\n",
    "ax1.plot(\n",
    "    np.arange(train_size, train_size + n_test),\n",
    "    arima_mean_pred,\n",
    "    label='ARIMA Forecast (mean)',\n",
    "    color='red'\n",
    ")\n",
    "\n",
    "# Plot the ARIMA confidence interval\n",
    "ax1.fill_between(\n",
    "    np.arange(train_size, train_size + n_test),\n",
    "    lower_ci,\n",
    "    upper_ci,\n",
    "    color='red',\n",
    "    alpha=0.2,\n",
    "    label='95% CI'\n",
    ")\n",
    "\n",
    "ax1.set_xlabel(\"Time Index\")\n",
    "ax1.set_ylabel(\"fracdiff level\")  # or an appropriate label\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# --- Right axis for GARCH volatility --- #\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Plot out-of-sample GARCH volatility forecast\n",
    "ax2.plot(\n",
    "    np.arange(train_size, train_size + n_test),\n",
    "    out_of_sample_vol,\n",
    "    label=\"GARCH Vol Forecast\",\n",
    "    color='blue'\n",
    ")\n",
    "\n",
    "ax2.set_ylabel(\"Volatility\")\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO\n",
    "- finish rolling window arima+garch"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}