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ritvikmath
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GARCH Stock Forecasting"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Read Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas_datareader.data as web\n",
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"from datetime import datetime, timedelta\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from arch import arch_model\n",
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"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## DIS Volatility"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"start = datetime(2015, 1, 1)\n",
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"end = datetime(2020, 6, 10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dis = web.DataReader('DIS', 'yahoo', start=start, end=end)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"returns = 100 * dis.Close.pct_change().dropna()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,4))\n",
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"plt.plot(returns)\n",
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"plt.ylabel('Pct Return', fontsize=16)\n",
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"plt.title('DIS Returns', fontsize=20)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PACF"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_pacf(returns**2)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Fit GARCH(3,3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = arch_model(returns, p=3, q=3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_fit = model.fit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"model_fit.summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Try GARCH(3,0) = ARCH(3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = arch_model(returns, p=3, q=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_fit = model.fit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_fit.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rolling_predictions = []\n",
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"test_size = 365\n",
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"\n",
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"for i in range(test_size):\n",
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" train = returns[:-(test_size-i)]\n",
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" model = arch_model(train, p=3, q=0)\n",
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" model_fit = model.fit(disp='off')\n",
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" pred = model_fit.forecast(horizon=1)\n",
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" rolling_predictions.append(np.sqrt(pred.variance.values[-1,:][0]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rolling_predictions = pd.Series(rolling_predictions, index=returns.index[-365:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,4))\n",
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"true, = plt.plot(returns[-365:])\n",
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"preds, = plt.plot(rolling_predictions)\n",
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"plt.title('Volatility Prediction - Rolling Forecast', fontsize=20)\n",
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"plt.legend(['True Returns', 'Predicted Volatility'], fontsize=16)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# S&P 500"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"start = datetime(2000, 1, 1)\n",
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"end = datetime(2020, 6, 10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"spy = web.DataReader('SPY', 'yahoo', start=start, end=end)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"returns = 100 * spy.Close.pct_change().dropna()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,4))\n",
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"plt.plot(returns)\n",
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"plt.ylabel('Pct Return', fontsize=16)\n",
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"plt.title('SPY Returns', fontsize=20)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PACF"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_pacf(returns**2)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Fit GARCH(2,2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = arch_model(returns, p=2, q=2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_fit = model.fit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"model_fit.summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Rolling Forecast"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rolling_predictions = []\n",
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"test_size = 365*5\n",
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"\n",
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"for i in range(test_size):\n",
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" train = returns[:-(test_size-i)]\n",
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" model = arch_model(train, p=2, q=2)\n",
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" model_fit = model.fit(disp='off')\n",
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" pred = model_fit.forecast(horizon=1)\n",
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" rolling_predictions.append(np.sqrt(pred.variance.values[-1,:][0]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rolling_predictions = pd.Series(rolling_predictions, index=returns.index[-365*5:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,4))\n",
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"true, = plt.plot(returns[-365*5:])\n",
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"preds, = plt.plot(rolling_predictions)\n",
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"plt.title('Volatility Prediction - Rolling Forecast', fontsize=20)\n",
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"plt.legend(['True Returns', 'Predicted Volatility'], fontsize=16)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# How to use the model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train = returns\n",
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"model = arch_model(train, p=2, q=2)\n",
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"model_fit = model.fit(disp='off')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pred = model_fit.forecast(horizon=7)\n",
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"future_dates = [returns.index[-1] + timedelta(days=i) for i in range(1,8)]\n",
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"pred = pd.Series(np.sqrt(pred.variance.values[-1,:]), index=future_dates)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10,4))\n",
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"plt.plot(pred)\n",
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"plt.title('Volatility Prediction - Next 7 Days', fontsize=20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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