time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Time Series Analysis & Modelling with CAD-PHY Exchange rate data.\n",
    "In this notebook, you will load historical Canadian Dollar-Yen exchange rate futures data and apply time series analysis and modeling to determine whether there is any predictable behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-01-05</th>\n",
       "      <td>184.65</td>\n",
       "      <td>184.65</td>\n",
       "      <td>184.65</td>\n",
       "      <td>184.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-06</th>\n",
       "      <td>185.06</td>\n",
       "      <td>185.06</td>\n",
       "      <td>185.06</td>\n",
       "      <td>185.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-07</th>\n",
       "      <td>186.88</td>\n",
       "      <td>186.88</td>\n",
       "      <td>186.88</td>\n",
       "      <td>186.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-08</th>\n",
       "      <td>186.58</td>\n",
       "      <td>186.58</td>\n",
       "      <td>186.58</td>\n",
       "      <td>186.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-11</th>\n",
       "      <td>187.64</td>\n",
       "      <td>187.64</td>\n",
       "      <td>187.64</td>\n",
       "      <td>187.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price    Open    High     Low\n",
       "Date                                      \n",
       "1982-01-05  184.65  184.65  184.65  184.65\n",
       "1982-01-06  185.06  185.06  185.06  185.06\n",
       "1982-01-07  186.88  186.88  186.88  186.88\n",
       "1982-01-08  186.58  186.58  186.58  186.58\n",
       "1982-01-11  187.64  187.64  187.64  187.64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Currency pair exchange rates for CAD/JPY\n",
    "cad_jpy_df = pd.read_csv(\n",
    "    Path(\"cad_jpy.csv\"), index_col=\"Date\", infer_datetime_format=True, parse_dates=True\n",
    ")\n",
    "cad_jpy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>126.37</td>\n",
       "      <td>126.31</td>\n",
       "      <td>126.37</td>\n",
       "      <td>126.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>125.30</td>\n",
       "      <td>125.24</td>\n",
       "      <td>125.30</td>\n",
       "      <td>125.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>123.46</td>\n",
       "      <td>123.41</td>\n",
       "      <td>123.46</td>\n",
       "      <td>123.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>124.54</td>\n",
       "      <td>124.48</td>\n",
       "      <td>124.54</td>\n",
       "      <td>124.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>124.27</td>\n",
       "      <td>124.21</td>\n",
       "      <td>124.27</td>\n",
       "      <td>124.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price    Open    High     Low\n",
       "Date                                      \n",
       "1990-01-02  126.37  126.31  126.37  126.31\n",
       "1990-01-03  125.30  125.24  125.30  125.24\n",
       "1990-01-04  123.46  123.41  123.46  123.41\n",
       "1990-01-05  124.54  124.48  124.54  124.48\n",
       "1990-01-08  124.27  124.21  124.27  124.21"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim the dataset to begin on January 1st, 1990\n",
    "cad_jpy_df = cad_jpy_df.loc[\"1990-01-01\":, :]\n",
    "cad_jpy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initial Time-Series Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Start by plotting the \"Settle\" price. Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot just the \"Price\" column from the dataframe:\n",
    "title = 'CAD/JPY Exchange Rates'\n",
    "cad_jpy_df['Price'].plot(figsize = (12,8), title = title, legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Do you see any patterns, long-term and/or short? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** I can see a downward trend overall but in short terms it is oscillating and damping out the max i-e the next max is lower than the previous max but the min is getting some support after the year 2000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decomposition Using a Hodrick-Prescott Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Using a Hodrick-Prescott Filter, decompose the exchange rate price into trend and noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Apply the Hodrick-Prescott Filter by decomposing the exchange rate price into two separate series:\n",
    "noise, trend = sm.tsa.filters.hpfilter(cad_jpy_df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1990-01-02    0.519095\n",
       "1990-01-03   -0.379684\n",
       "1990-01-04   -2.048788\n",
       "1990-01-05   -0.798304\n",
       "1990-01-08   -0.897037\n",
       "                ...   \n",
       "2020-05-29   -0.503961\n",
       "2020-06-01    0.218939\n",
       "2020-06-02    1.088117\n",
       "2020-06-03    1.125501\n",
       "2020-06-04    0.872339\n",
       "Name: Price_cycle, Length: 7929, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trend.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>Price</th>\n",
       "      <th>noise</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>126.37</td>\n",
       "      <td>0.519095</td>\n",
       "      <td>125.850905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>125.30</td>\n",
       "      <td>-0.379684</td>\n",
       "      <td>125.679684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>123.46</td>\n",
       "      <td>-2.048788</td>\n",
       "      <td>125.508788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>124.54</td>\n",
       "      <td>-0.798304</td>\n",
       "      <td>125.338304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>124.27</td>\n",
       "      <td>-0.897037</td>\n",
       "      <td>125.167037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price     noise       trend\n",
       "Date                                    \n",
       "1990-01-02  126.37  0.519095  125.850905\n",
       "1990-01-03  125.30 -0.379684  125.679684\n",
       "1990-01-04  123.46 -2.048788  125.508788\n",
       "1990-01-05  124.54 -0.798304  125.338304\n",
       "1990-01-08  124.27 -0.897037  125.167037"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of just the exchange rate price, and add columns for \"noise\" and \"trend\" series from above:\n",
    "df = pd.concat([cad_jpy_df['Price'], noise, trend], axis = 1)\n",
    "df.rename(columns = {'Price_cycle': 'noise', 'Price_trend': 'trend'}, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 7929 entries, 1990-01-02 to 2020-06-04\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Price   7929 non-null   float64\n",
      " 1   noise   7929 non-null   float64\n",
      " 2   trend   7929 non-null   float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 247.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Exchange Rate Price vs. the Trend for 2015 to the present\n",
    "df.iloc[:,[0,2]].loc[\"2015-01-01\":, :].plot(figsize = (12,8), title = 'Price vs. Trend', legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** the overall trend is downward as seen in the previous trend analysis as well, but here we can see since 2015 onward the minimum is also getting lower than the previous one year by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['noise'].plot(figsize = (12,8), title = 'Noise')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Returns using an ARMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using exchange rate *Returns*, estimate an ARMA model\n",
    "\n",
    "1. ARMA: Create an ARMA model and fit it to the returns data. Note: Set the AR and MA (\"p\" and \"q\") parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the ARMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the forecasted returns (the results forecast from ARMA model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
    "                        FutureWarning)\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
    "                        FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>0.076697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>1.251756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>1.425508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>0.373134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>0.012392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price\n",
       "Date                \n",
       "2020-05-29  0.076697\n",
       "2020-06-01  1.251756\n",
       "2020-06-02  1.425508\n",
       "2020-06-03  0.373134\n",
       "2020-06-04  0.012392"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series using \"Price\" percentage returns, drop any nan\"s, and check the results:\n",
    "# (Make sure to multiply the pct_change() results by 100)\n",
    "# In this case, you may have to replace inf, -inf values with np.nan\"s\n",
    "returns = (cad_jpy_df[[\"Price\"]].pct_change() * 100)\n",
    "returns = returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>   <td>7928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(2, 0, 1)</td>  <th>  Log Likelihood     </th> <td>-9752.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 01 May 2022</td> <th>  AIC                </th> <td>19514.620</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:50:11</td>     <th>  BIC                </th> <td>19549.511</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>19526.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 7928</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>   -0.0022</td> <td>    0.009</td> <td>   -0.237</td> <td> 0.813</td> <td>   -0.021</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.0152</td> <td>    3.499</td> <td>   -0.004</td> <td> 0.997</td> <td>   -6.873</td> <td>    6.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>   -0.0018</td> <td>    0.107</td> <td>   -0.017</td> <td> 0.987</td> <td>   -0.212</td> <td>    0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.0153</td> <td>    3.499</td> <td>   -0.004</td> <td> 0.997</td> <td>   -6.874</td> <td>    6.844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.6855</td> <td>    0.006</td> <td>  122.678</td> <td> 0.000</td> <td>    0.675</td> <td>    0.696</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>11578.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>1.00</td> <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.85</td> <th>  Skew:              </th>   <td>-0.38</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>8.87</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 7928\n",
       "Model:                 ARIMA(2, 0, 1)   Log Likelihood               -9752.310\n",
       "Date:                Sun, 01 May 2022   AIC                          19514.620\n",
       "Time:                        16:50:11   BIC                          19549.511\n",
       "Sample:                             0   HQIC                         19526.568\n",
       "                               - 7928                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0022      0.009     -0.237      0.813      -0.021       0.016\n",
       "ar.L1         -0.0152      3.499     -0.004      0.997      -6.873       6.843\n",
       "ar.L2         -0.0018      0.107     -0.017      0.987      -0.212       0.209\n",
       "ma.L1         -0.0153      3.499     -0.004      0.997      -6.874       6.844\n",
       "sigma2         0.6855      0.006    122.678      0.000       0.675       0.696\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             11578.41\n",
       "Prob(Q):                              1.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.85   Skew:                            -0.38\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         8.87\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimate and ARMA model using statsmodels (use order=(2, 1))\n",
    "model = ARIMA(returns.values, order=(2,0,1))\n",
    "\n",
    "# Fit the model and assign it to a variable called results\n",
    "results = model.fit()\n",
    "\n",
    "# Output model summary results:\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Returns Forecast\n",
    "fc = results.forecast(5, alpha=0.05, )\n",
    "pd.DataFrame(fc).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Based on the p-value, is the model a good fit?\n",
    "\n",
    "**Answer:** the p values are very big and based on that we can ay that the model is not a good fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting the Exchange Rate Price using an ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. Using the *raw* CAD/JPY exchange rate price, estimate an ARIMA model.\n",
    "     1. Set P=5, D=1, and Q=1 in the model (e.g., ARIMA(df, order=(5,1,1))\n",
    "     2. P= # of Auto-Regressive Lags, D= # of Differences (this is usually =1), Q= # of Moving Average Lags\n",
    " 2. Output the ARIMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    " 3. Plot a 5 day forecast for the Exchange Rate Price. What does the model forecast predict will happen to the Japanese Yen in the near term?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "# Estimate and ARIMA Model:\n",
    "# Hint: ARIMA(df, order=(p, d, q))\n",
    "arima = ARIMA(returns.values, order=(5,1,1))\n",
    "# Fit the model\n",
    "results_arima = arima.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>   <td>7928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(5, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-9754.288</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 01 May 2022</td> <th>  AIC                </th> <td>19522.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:54:39</td>     <th>  BIC                </th> <td>19571.423</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>19539.304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 7928</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.0302</td> <td>    0.007</td> <td>   -4.357</td> <td> 0.000</td> <td>   -0.044</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>   -0.0026</td> <td>    0.007</td> <td>   -0.393</td> <td> 0.694</td> <td>   -0.016</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.0102</td> <td>    0.008</td> <td>   -1.282</td> <td> 0.200</td> <td>   -0.026</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>  <td>    0.0015</td> <td>    0.007</td> <td>    0.220</td> <td> 0.825</td> <td>   -0.012</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5</th>  <td>   -0.0188</td> <td>    0.007</td> <td>   -2.724</td> <td> 0.006</td> <td>   -0.032</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -1.0000</td> <td>    0.008</td> <td> -121.043</td> <td> 0.000</td> <td>   -1.016</td> <td>   -0.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.6849</td> <td>    0.009</td> <td>   80.181</td> <td> 0.000</td> <td>    0.668</td> <td>    0.702</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>11612.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.95</td> <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.84</td> <th>  Skew:              </th>   <td>-0.39</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>8.88</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 7928\n",
       "Model:                 ARIMA(5, 1, 1)   Log Likelihood               -9754.288\n",
       "Date:                Sun, 01 May 2022   AIC                          19522.577\n",
       "Time:                        16:54:39   BIC                          19571.423\n",
       "Sample:                             0   HQIC                         19539.304\n",
       "                               - 7928                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.0302      0.007     -4.357      0.000      -0.044      -0.017\n",
       "ar.L2         -0.0026      0.007     -0.393      0.694      -0.016       0.010\n",
       "ar.L3         -0.0102      0.008     -1.282      0.200      -0.026       0.005\n",
       "ar.L4          0.0015      0.007      0.220      0.825      -0.012       0.015\n",
       "ar.L5         -0.0188      0.007     -2.724      0.006      -0.032      -0.005\n",
       "ma.L1         -1.0000      0.008   -121.043      0.000      -1.016      -0.984\n",
       "sigma2         0.6849      0.009     80.181      0.000       0.668       0.702\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             11612.88\n",
       "Prob(Q):                              0.95   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.84   Skew:                            -0.39\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         8.88\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results_arima.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Returns Forecast\n",
    "fc = results_arima.forecast(5, alpha=0.05, )\n",
    "pd.DataFrame(fc).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What does the model forecast will happen to the Japanese Yen in the near term?\n",
    "\n",
    "**Answer:** It seems like it will go up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volatility Forecasting with GARCH\n",
    "\n",
    "Rather than predicting returns, let's forecast near-term **volatility** of Japanese Yen exchange rate returns. Being able to accurately predict volatility will be extremely useful if we want to trade in derivatives or quantify our maximum loss.\n",
    " \n",
    "Using exchange rate *Returns*, estimate a GARCH model. **Hint:** You can reuse the `returns` variable from the ARMA model section.\n",
    "\n",
    "1. GARCH: Create an GARCH model and fit it to the returns data. Note: Set the parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the GARCH summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arch as arch\n",
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "model_garch = arch_model(returns, mean=\"Zero\", vol=\"GARCH\", p=2, q=1)\n",
    "# Fit the model\n",
    "res = model_garch.fit(disp=\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Zero Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Price</td>       <th>  R-squared:         </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>         <td>Zero 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>  -8911.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   17830.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   17858.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>7928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Sun, May 01 2022</td>  <th>  Df Residuals:      </th>    <td>7928</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>17:12:29</td>      <th>  Df Model:          </th>      <td>0</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>9.0733e-03</td> <td>2.545e-03</td> <td>    3.566</td> <td>3.628e-04</td>  <td>[4.086e-03,1.406e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th> <td>    0.0624</td> <td>1.835e-02</td> <td>    3.402</td> <td>6.682e-04</td>  <td>[2.647e-02,9.841e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th>   <td>0.0000</td>   <td>2.010e-02</td>   <td>0.000</td>   <td>    1.000</td> <td>[-3.940e-02,3.940e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beta[1]</th>  <td>    0.9243</td> <td>1.229e-02</td> <td>   75.205</td>   <td>0.000</td>      <td>[  0.900,  0.948]</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Covariance estimator: robust"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       Zero Mean - GARCH Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                  Price   R-squared:                       0.000\n",
       "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:               -8911.02\n",
       "Distribution:                  Normal   AIC:                           17830.0\n",
       "Method:            Maximum Likelihood   BIC:                           17858.0\n",
       "                                        No. Observations:                 7928\n",
       "Date:                Sun, May 01 2022   Df Residuals:                     7928\n",
       "Time:                        17:12:29   Df Model:                            0\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      9.0733e-03  2.545e-03      3.566  3.628e-04  [4.086e-03,1.406e-02]\n",
       "alpha[1]       0.0624  1.835e-02      3.402  6.682e-04  [2.647e-02,9.841e-02]\n",
       "alpha[2]       0.0000  2.010e-02      0.000      1.000 [-3.940e-02,3.940e-02]\n",
       "beta[1]        0.9243  1.229e-02     75.205      0.000      [  0.900,  0.948]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Our p-values for GARCH and volatility forecasts tend to be much lower than our ARMA/ARIMA return and price forecasts. In particular, here we have all p-values of less than 0.05, except for alpha(2), indicating overall a much better model performance. In practice, in financial markets, it's easier to forecast volatility than it is to forecast returns or prices. (After all, if we could very easily predict returns, we'd all be rich!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-06-04'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the last day of the dataset\n",
    "last_day = returns.index.max().strftime('%Y-%m-%d')\n",
    "last_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "\n",
    "# Start the forecast using the last_day calculated above\n",
    "forecasts = res.forecast(start=last_day, horizon=forecast_horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h.1</th>\n",
       "      <th>h.2</th>\n",
       "      <th>h.3</th>\n",
       "      <th>h.4</th>\n",
       "      <th>h.5</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>2020-06-04</th>\n",
       "      <td>12.566029</td>\n",
       "      <td>12.573718</td>\n",
       "      <td>12.581301</td>\n",
       "      <td>12.588778</td>\n",
       "      <td>12.596153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  h.1        h.2        h.3        h.4        h.5\n",
       "Date                                                             \n",
       "2020-06-04  12.566029  12.573718  12.581301  12.588778  12.596153"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualize the forecast\n",
    "intermediate = np.sqrt(forecasts.variance.dropna() * 252)\n",
    "intermediate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2020-06-04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>h.1</th>\n",
       "      <td>12.566029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.2</th>\n",
       "      <td>12.573718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.3</th>\n",
       "      <td>12.581301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.4</th>\n",
       "      <td>12.588778</td>\n",
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       "      <th>h.5</th>\n",
       "      <td>12.596153</td>\n",
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      ],
      "text/plain": [
       "Date  2020-06-04\n",
       "h.1    12.566029\n",
       "h.2    12.573718\n",
       "h.3    12.581301\n",
       "h.4    12.588778\n",
       "h.5    12.596153"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the forecast so that it is easier to plot\n",
    "final = intermediate.dropna().T\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the final forecast\n",
    "final.plot(title = '5 Day Forecast of Volatility')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What does the model forecast will happen to volatility in the near term?\n",
    "\n",
    "**Answer:** Seems like volatility is increasing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Based on your time series analysis, would you buy the yen now?\n",
    "\n",
    "    * Based on time series analysis I would not buy Yen as the overall trend is downward and volatility is also increasing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Is the risk of the yen expected to increase or decrease?\n",
    "    * Yes it is increasing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Based on the model evaluation, would you feel confident in using these models for trading?\n",
    "    * I can use GARCH for predicting volatility but I ARMA and ARIMA needs more fine tunning before we can see its true potential. But with the above parameters I would not feel confident using ARMA or ARIMA models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}