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",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Read Historical Daily Yen Futures Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-08-02</th>\n",
       "      <td>3398.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3398.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-03</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-04</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-05</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-06</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle  Volume  \\\n",
       "Date                                                                 \n",
       "1976-08-02  3398.0  3401.0  3398.0  3401.0     NaN  3401.0     2.0   \n",
       "1976-08-03  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-04  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-05  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-06  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1976-08-02                         1.0  \n",
       "1976-08-03                         1.0  \n",
       "1976-08-04                         1.0  \n",
       "1976-08-05                         1.0  \n",
       "1976-08-06                         1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Futures contract on the Yen-dollar exchange rate:\n",
    "# This is the continuous chain of the futures contracts that are 1 month to expiration\n",
    "yen_futures = pd.read_csv(\n",
    "    Path(\"yen.csv\"), index_col=\"Date\", infer_datetime_format=True, parse_dates=True\n",
    ")\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6954.0</td>\n",
       "      <td>6954.0</td>\n",
       "      <td>6835.0</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>48336.0</td>\n",
       "      <td>51473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6877.0</td>\n",
       "      <td>6910.0</td>\n",
       "      <td>6865.0</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>38206.0</td>\n",
       "      <td>53860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6937.0</td>\n",
       "      <td>7030.0</td>\n",
       "      <td>6924.0</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>49649.0</td>\n",
       "      <td>55699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6952.0</td>\n",
       "      <td>6985.0</td>\n",
       "      <td>6942.0</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>29944.0</td>\n",
       "      <td>53111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6936.0</td>\n",
       "      <td>6972.0</td>\n",
       "      <td>6936.0</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>19763.0</td>\n",
       "      <td>52072.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle   Volume  \\\n",
       "Date                                                                  \n",
       "1990-01-02  6954.0  6954.0  6835.0  6847.0     NaN  6847.0  48336.0   \n",
       "1990-01-03  6877.0  6910.0  6865.0  6887.0     NaN  6887.0  38206.0   \n",
       "1990-01-04  6937.0  7030.0  6924.0  7008.0     NaN  7008.0  49649.0   \n",
       "1990-01-05  6952.0  6985.0  6942.0  6950.0     NaN  6950.0  29944.0   \n",
       "1990-01-08  6936.0  6972.0  6936.0  6959.0     NaN  6959.0  19763.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1990-01-02                     51473.0  \n",
       "1990-01-03                     53860.0  \n",
       "1990-01-04                     55699.0  \n",
       "1990-01-05                     53111.0  \n",
       "1990-01-08                     52072.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim the dataset to begin on January 1st, 1990\n",
    "yen_futures = yen_futures.loc[\"1990-01-01\":, :]\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: Initial Time-Series Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 just the \"Settle\" column from the dataframe:\n",
    "yen_futures.Settle.plot(figsize = (12,8), title = 'Yen Futures Settle Prices')\n",
    "\n",
    "# Set margins to 0 to eliminate whitespace on x-axis (required importing pyplot from matplotlib):\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "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 Settle price into a 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 \"Settle\" price into two separate series:\n",
    "settle_noise, settle_trend = sm.tsa.filters.hpfilter(yen_futures['Settle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Settle</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>6847.0</td>\n",
       "      <td>-61.503967</td>\n",
       "      <td>6908.503967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6887.0</td>\n",
       "      <td>-21.799756</td>\n",
       "      <td>6908.799756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>7008.0</td>\n",
       "      <td>98.942896</td>\n",
       "      <td>6909.057104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6950.0</td>\n",
       "      <td>40.776052</td>\n",
       "      <td>6909.223948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6959.0</td>\n",
       "      <td>49.689938</td>\n",
       "      <td>6909.310062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Settle      Noise        Trend\n",
       "Date                                      \n",
       "1990-01-02  6847.0 -61.503967  6908.503967\n",
       "1990-01-03  6887.0 -21.799756  6908.799756\n",
       "1990-01-04  7008.0  98.942896  6909.057104\n",
       "1990-01-05  6950.0  40.776052  6909.223948\n",
       "1990-01-08  6959.0  49.689938  6909.310062"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of just the settle price, and add columns for \"noise\" and \"trend\" series from above:\n",
    "df_settle_price = pd.DataFrame(yen_futures['Settle'])\n",
    "df_settle_price['Noise'] = settle_noise\n",
    "df_settle_price['Trend'] = settle_trend\n",
    "df_settle_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 Settle Price vs. the Trend for 2015 to the present\n",
    "settle_price_2015_to_present = yen_futures.loc['2015-01-01':]\n",
    "trend_2015_to_present = settle_trend.loc['2015-01-01':]\n",
    "settle_vs_trend = pd.concat([settle_price_2015_to_present, trend_2015_to_present], axis=\"columns\", join=\"inner\")\n",
    "settle_vs_trend_slice = settle_vs_trend[['Settle', 'Settle_trend']]\n",
    "settle_vs_trend_slice.plot(figsize = (12,8), title = 'Settle vs. Trend')\n",
    "\n",
    "# Set margins to 0 to eliminate whitespace on x-axis (required importing pyplot from matplotlib):\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 Settle Noise\n",
    "settle_noise.plot(figsize = (12,8), title = 'Noise')\n",
    "\n",
    "# Set margins to 0 to eliminate whitespace on x-axis (required importing pyplot from matplotlib):\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Returns using an ARMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using futures Settle *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": 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>Settle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>-0.410601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>-0.369458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>-0.564304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-14</th>\n",
       "      <td>0.151335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>-0.469509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Settle\n",
       "Date                \n",
       "2019-10-09 -0.410601\n",
       "2019-10-10 -0.369458\n",
       "2019-10-11 -0.564304\n",
       "2019-10-14  0.151335\n",
       "2019-10-15 -0.469509"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series using \"Settle\" price percentage returns, drop any nan's, and check the results:\n",
    "returns = (yen_futures[[\"Settle\"]].pct_change() * 100)\n",
    "returns = returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:581: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "\n",
    "# Estimate an ARMA model using statsmodels (order=(2, 1))\n",
    "arma_model = ARMA(returns.Settle, order=(2,1))\n",
    "\n",
    "# Fit the model and assign it to a variable called results\n",
    "arma_results = arma_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Settle</td>      <th>  No. Observations:  </th>   <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(2, 1)</td>    <th>  Log Likelihood     </th> <td>-7894.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.692</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 03 Dec 2020</td> <th>  AIC                </th> <td>15798.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>20:17:05</td>     <th>  BIC                </th> <td>15832.765</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>15810.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </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>    0.0063</td> <td>    0.008</td> <td>    0.804</td> <td> 0.421</td> <td>   -0.009</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Settle</th> <td>   -0.3059</td> <td>    1.278</td> <td>   -0.239</td> <td> 0.811</td> <td>   -2.810</td> <td>    2.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.Settle</th> <td>   -0.0019</td> <td>    0.019</td> <td>   -0.099</td> <td> 0.921</td> <td>   -0.040</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.Settle</th> <td>    0.2944</td> <td>    1.278</td> <td>    0.230</td> <td> 0.818</td> <td>   -2.210</td> <td>    2.799</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -3.3379</td> <td>          +0.0000j</td> <td>           3.3379</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>        -157.3101</td> <td>          +0.0000j</td> <td>         157.3101</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -3.3970</td> <td>          +0.0000j</td> <td>           3.3970</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                 Settle   No. Observations:                 7514\n",
       "Model:                     ARMA(2, 1)   Log Likelihood               -7894.071\n",
       "Method:                       css-mle   S.D. of innovations              0.692\n",
       "Date:                Thu, 03 Dec 2020   AIC                          15798.142\n",
       "Time:                        20:17:05   BIC                          15832.765\n",
       "Sample:                             0   HQIC                         15810.030\n",
       "                                                                              \n",
       "================================================================================\n",
       "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const            0.0063      0.008      0.804      0.421      -0.009       0.022\n",
       "ar.L1.Settle    -0.3059      1.278     -0.239      0.811      -2.810       2.198\n",
       "ar.L2.Settle    -0.0019      0.019     -0.099      0.921      -0.040       0.036\n",
       "ma.L1.Settle     0.2944      1.278      0.230      0.818      -2.210       2.799\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -3.3379           +0.0000j            3.3379            0.5000\n",
       "AR.2         -157.3101           +0.0000j          157.3101            0.5000\n",
       "MA.1           -3.3970           +0.0000j            3.3970            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "arma_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the above ARMA model, the autoregressive term has a p-value (.42) that is greater than the significance level of 0.05. We can conclude that the coefficient for the autoregressive term is NOT statistically significant, and should NOT be kept in the model.\n"
     ]
    }
   ],
   "source": [
    "print(\"In the above ARMA model, the autoregressive term has a p-value (.42) that is greater than the significance level of 0.05. We can conclude that the coefficient for the autoregressive term is NOT statistically significant, and should NOT be kept in the model.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7uvf9P5hZGXAesNjMlnViv0I2cfgAPjcpg9+8voOd+4/5UYKISFiySLq7aV5enissLOz06ZYfqWHafau5dEIqv/ryOZ0+fRERP5nZO865dl9v1uuupA4mvX8CCy4Zw5IP9rJuxyfOyBUR6ZUUEJ5/vngUQ/on8JMXimhqipytKhGRrqKA8PSNi+H7s8azvuwwf31/l9/liIj4TgHRzGfOHs6ZGQP42YubOF6nZ0aISO+mgGgmKsq4c04uew7X8Pir2/wuR0TEVwqIFqaMGkTBxCE8snor5Udq/C5HRMQ3CoggbiuYQGOT4/5lvtxLUEQkLCggghg5uB9fuyCL//duGR/uOux3OSIivlBAnMI3Lx3LwL5x3LW4iEi6mFBEpLMoIE5hQJ9YvpufzdptB3ipqNzvckREup0CohXXTBlBdloi9ywppq5Bz4wQkd5FAdGKmOgo7piTw479x1j45g6/yxER6VYKiDZMG5/GxeNSeXDlFg4erfO7HBGRbqOACMGdc3Korm3glyu3+F2KiEi3UUCEYFx6EtdMGcHv135ESUW13+WIiHQLBUSIvjtjHH1jo7lnSbHfpYiIdAsFRIhSEuP55mVjWbmxgte27PO7HBGRLqeAaIevnZ9F5qA+3LW4iEY9M0JEejgFRDskxEZz26wcNu6t4pnCUr/LERHpUgqIdpr9qSHkjRzIz1/aRHVtg9/liIh0mZACwsxmmdkmMysxs9uCDDcze9Abvt7MJjUb9qSZVZjZhy3GGWRmy81si/dz4Ol3p+uZGT+8Ipd91XX86uUSv8sREekybQaEmUUDDwMFQC5wjZnltmhWAGR7r/nAI82G/RaYFWTStwErnXPZwErvc0Q4KzOZf/j0cP7nte2UHTzmdzkiIl0ilC2IKUCJc26bc64OeBqY26LNXGChC1gLJJvZUADn3BrgQJDpzgV+573/HfCZDtTvm1tmjifK4Kcv6pkRItIzhRIQw4HmR2TLvO/a26aldOfcHgDvZ1qwRmY238wKzaywsrIyhHK7x7DkPsy/aDTPv7+bdz466Hc5IiKdLpSAsCDftTzHM5Q2HeKce8w5l+ecy0tNTe2MSXaab1wyhrSkeD0zQkR6pFACogzIbPY5A9jdgTYtlZ/YDeX9rAihlrDSLz6G780cz992HuL59Xv8LkdEpFOFEhDrgGwzG2VmccA8YFGLNouA67yzmaYCh0/sPmrFIuCr3vuvAn9tR91h4/OTMjhjWH9+unQjNfWNfpcjItJp2gwI51wDcBOwDCgGnnHObTCzBWa2wGu2BNgGlACPAzeeGN/MngLeBMabWZmZfd0bdC8ww8y2ADO8zxEnKsq4c04uuw4d54nXtvtdjohIp7FI2neel5fnCgsL/S4jqPkLC3m9ZB8v3zKNtKQEv8sRETnJzN5xzuW1dzxdSd1Jbp+dQ11jEw8s3+x3KSIinUIB0UlGpfTjuvOy+NO6Uor3HPG7HBGR06aA6ETfviyb/n1iddqriPQICohONKBvLDdPz+b1kv2s2hhxZ+2KiHyMAqKTfXnqSEan9uPuJcXUNzb5XY6ISIcpIDpZbHQUd8zOYVvlUf649iO/yxER6TAFRBe4bEIaF4wdzH+t3MLhY/V+lyMi0iEKiC5gFrh47vDxeh5ctcXvckREOkQB0UVyhvbni3mZLHxzB9v3HfW7HBGRdlNAdKF/uXwccdFR3LOk2O9SRETaTQHRhdKSErjx0rG8VFTOG1v3+V2OiEi7KCC62NcvHMXw5D7c9UIxjU26eE5EIocCooslxEZza8EEivYc4dl3y/wuR0QkZAqIbnDlmUP59Ihk7l+2iaO1DX6XIyISEgVENzAzfnhFLhVVtfz6la1+lyMiEhIFRDeZNGIgV541jMde3cbuQ8f9LkdEpE0KiG5066zxNDm4b9kmv0sREWmTAqIbZQzsyz9dOIrn/raL90sP+V2OiEirFBDd7MZLx5KSGMdPXtAzI0QkvCkgullifAz/evl4Cj86yJIP9vpdjojIKYUUEGY2y8w2mVmJmd0WZLiZ2YPe8PVmNqmtcc3sLDN708w+MLPnzax/53Qp/F2dl8mEIUnc+2IxNfWNfpcjIhJUmwFhZtHAw0ABkAtcY2a5LZoVANneaz7wSAjj/g9wm3PuU8BzwC2n3ZsIER0VuNtr6YHj/PaNHX6XIyISVChbEFOAEufcNudcHfA0MLdFm7nAQhewFkg2s6FtjDseWOO9Xw587jT7ElEuzE5h+oQ0Hl5Vwr7qWr/LERH5hFACYjhQ2uxzmfddKG1aG/dD4Crv/ReAzGAzN7P5ZlZoZoWVlZUhlBs5bp+dw/H6Rh5YvtnvUkREPiGUgLAg37U8/eZUbVob9x+Bb5rZO0ASUBds5s65x5xzec65vNTU1BDKjRxj0xL5ytSRPPX2TjaXV/ldjojIx4QSEGV8/K/7DGB3iG1OOa5zbqNz7nLn3DnAU0CvvAfFd6Znkxgfw12L9cwIEQkvoQTEOiDbzEaZWRwwD1jUos0i4DrvbKapwGHn3J7WxjWzNO9nFHAn8Gin9CjCDOwXx7enZ7NmcyWrN1X4XY6IyEltBoRzrgG4CVgGFAPPOOc2mNkCM1vgNVsCbANKgMeBG1sb1xvnGjPbDGwksFXxm07rVYS57rwssgb35e7FxTQ0NvldjogIABZJV/Pm5eW5wsJCv8voEss27OUbv3+Hn3xmItdOHel3OSLSg5jZO865vPaOpyupw8TluelMHT2IB5Zv5vDxer/LERFRQIQLs8DFcweP1fGrl0v8LkdERAERTiYOH8DnJ2Xwm9d3sHP/Mb/LEZFeTgERZr43czwx0ca9L+q0VxHxlwIizKT3T2DBJWNY8sFe3t5+wO9yRKQXU0CEoX++aDRDByRw1+Iimpoi5ywzEelZFBBhqE9cNN+fNZ71ZYf5y3u7/C5HRHopBUSYmnvWcM7MGMDPXtzEsboGv8sRkV5IARGmoqKMH16Ry94jNTy+Zrvf5YhIL6SACGOTswYx+1NDePSVrew9XON3OSLSyyggwtxts3JobHLc/9Imv0sRkV5GARHmRgzuy/UXZPHsu2V8uOuw3+WISC+igIgA37xsLIP6xvGTF4qIpJsrikhkU0BEgP4JsXx3xjje2n6AZRvK/S5HRHoJBUSEmDc5k3HpidyztJi6Bj0zQkS6ngIiQsRER3HHnFw+2n+MhW/u8LscEekFFBAR5JJxqVwyLpVfrtzCgaN1fpcjIj2cAiLC3Dknh2N1jfxyxWa/SxGRHk4BEWGy05O4Zkomf3hrJyUV1X6XIyI9mAIiAn03fxx9Y6P5zyV6ZoSIdJ2QAsLMZpnZJjMrMbPbggw3M3vQG77ezCa1Na6ZnW1ma83sPTMrNLMpndOlnm9wYjw3XTaWVRsreHVLpd/liEgP1WZAmFk08DBQAOQC15hZbotmBUC295oPPBLCuD8DfuycOxv4N++zhOhrF2SROagPdy8uplHPjBCRLhDKFsQUoMQ5t805Vwc8Dcxt0WYusNAFrAWSzWxoG+M6oL/3fgCw+zT70qvEx0Rze0EOG/dW8Uxhqd/liEgPFEpADAear4HKvO9CadPauDcD95lZKXA/cHuwmZvZfG8XVGFlpXanNFcwcQiTswby85c2UVVT73c5ItLDhBIQFuS7lvs0TtWmtXFvAL7rnMsEvgs8EWzmzrnHnHN5zrm81NTUEMrtPcyMO+fksq+6jl+t3up3OSLSw4QSEGVAZrPPGXxyd9Cp2rQ27leBP3vv/4/A7ihpp7Myk/nsp4fzxGvbKT1wzO9yRKQHCSUg1gHZZjbKzOKAecCiFm0WAdd5ZzNNBQ475/a0Me5u4BLv/WXAltPsS691y6zxRBn89MWNfpciIj1ITFsNnHMNZnYTsAyIBp50zm0wswXe8EeBJcBsoAQ4Blzf2rjepP8Z+KWZxQA1BM5+kg4YOqAP8y8ew4Mrt3D9BQc5Z+RAv0sSkR7AIun5Anl5ea6wsNDvMsLSsboGpt23mmHJffjzDecTFRXs8I+I9EZm9o5zLq+94+lK6h6ib1wMt8wcz3ulh3h+vc4YFpHTp4DoQT43KYOJw/vz06Ubqalv9LscEYlwCogeJCoqcNrr7sM1PPHadr/LEZEIp4DoYaaOHszMM9L51cslVFTV+F2OiEQwBUQPdHtBDnWNTfziJT0zQkQ6TgHRA2Wl9OOr52Xxp8JSinYf8bscEYlQCoge6luXZZPcJ5a7FhcRSacyi0j4UED0UAP6xnJz/jje2LqflcUVfpcjIhFIAdGDfencEYxJ7cd/LimmvrHJ73JEJMIoIHqw2Ogo7piTw7Z9R/nD2o/8LkdEIowCooe7dHwaF45N4b9WbOHQsTq/yxGRCKKA6OHMjDuvyKGqpp4HV5b4XY6IRBAFRC8wYUh/vjg5k4Vv7mBbZbXf5YhIhFBA9BL/MmM8CbHR3LNUz4wQkdAoIHqJ1KR4brx0DMuLynlj6z6/yxGRCKCA6EX+8YJRDE/uw10vFNPYpIvnRKR1CoheJCE2mtsKJlC05wjPvlPmdzkiEuYUEL3MFWcOZdKIZO57aRNHaxv8LkdEwpgCopcJnPaaS2VVLY++stXvckQkjCkgeqFJIwZy1VnDeGzNNnYfOu53OSISpkIKCDObZWabzKzEzG4LMtzM7EFv+Hozm9TWuGb2JzN7z3vtMLP3OqVHEpJbCyYA8LMXddqriATXZkCYWTTwMFAA5ALXmFlui2YFQLb3mg880ta4zrkvOufOds6dDTwL/LkzOiShGZ7ch3+6aBR/eW8375Ue8rscEQlDoWxBTAFKnHPbnHN1wNPA3BZt5gILXcBaINnMhoYyrpkZcDXw1Gn2RdrphmljSUmM564X9MwIEfmkUAJiOFDa7HOZ910obUIZ9yKg3Dm3JdjMzWy+mRWaWWFlZWUI5UqoEuNj+N7l4yj86CBLPtjrdzkiEmZCCQgL8l3LPzdP1SaUca+hla0H59xjzrk851xeampqq4VK+30hL5MJQ5K4Z2kxNfWNfpcjImEklIAoAzKbfc4AdofYptVxzSwG+Czwp9BLls4UHWX88Ipcyg4e57dv7PC7HBEJI6EExDog28xGmVkcMA9Y1KLNIuA672ymqcBh59yeEMbNBzY653RZr48uGJtCfk4aD60qYV91rd/liEiYaDMgnHMNwE3AMqAYeMY5t8HMFpjZAq/ZEmAbUAI8DtzY2rjNJj8PHZwOC7fPzqGmvpEHlm/2uxQRCRMWSWev5OXlucLCQr/L6LH+fdEGFr65g6XfuZjxQ5L8LkdEOomZveOcy2vveLqSWk66OT+bpIRY7lqs015FRAEhzST3jePb07N5dcs+Vm/WKcUivZ0CQj7m2qkjGZXSj7sXF1Pf2OR3OSLiIwWEfExcTBS3F0ygpKKap9/e6Xc5IuKjGL8LkPAzIzed80YP5hfLN3PV2cMZ0CfW75LEBxVVNawqrmBFcTk7DxxjcL94UpLiSUmMIyUxntTEeFKSAu9TEuMZnBhHfEy032VLJ1JAyCcEnhmRwxX//RoPv1zCD2bn+F2SdAPnHJvLq1lRXM7yovKTN3EcntyH3GH9OXi0jg/KDrGvuo7qUzxsqn9CjBciXoB4YXLiu5PhkhRPQqzCJNwpICSoM4YN4AvnZPCb17fz5XNHMHJwP79Lki5Q39jEuu0HWF5czorickoPBJ4PclbGAP51xjjyc9OZMCSJwD01/+54XSP7qmu9V13gZ9XfP1dW11K89wj7qmo5UhM8TBLjY/4eIC22RgIh8vfP/eK1qvKDroOQU6o4UsO0+1dzybhUHvnKOX6XI53k8PF6XtlcyYqicl7eVEFVTQNxMVFcODaF/Jx0puekkd4/odPmV9vQyP4TIVJdy76qQICcDJeTwVLLwWP1QafRJzb6kwGSGNdsy8TbOkmKJyk+5hOB1tt19DoIxbKcUlr/BG64ZAw/X76Zt7bt59zRg/0uSTqo9MAxVhaXs7y4nLe2HaChyTG4XxyzzhhCfm46F2Wn0Deua1YH8THRDEvuw7DkPm22rW9sOhkmlSe3SpqFS3UtO/cf492PDnLgWB3B/r6Nj4n62O6s1rZOBvSJVZi0QlsQ0qrjdY1c9vPVpCTG89dvXkBUlP4zRYKmJscHuw6fPJ6wcW8VAGPTEsnPSWdGbhpnZw4kOoKXZ0NjEweO1bGv6uMBcmKrpLLZ7q8DR+tobPrkui422j6+BdLieElqs8/JfWIj9t+/tiCkS/SJi+bWWRO4+U/v8dzfdvG5czL8LklOoaa+kTe27mN5UQUri8upqKolymBy1iDunJPD9Jx0RqX0nGNJMdFRpCUlkJbU9u6wpibHwWN1H9saqWyxdVJZXUvxnir2H62lvvGTYRIdZQzu1zxEvAAJsoUyqF9cRIfvCQoIadNVZw3jN69v575lmyj41JAu2xUh7bevupZVGytYUVTOq1v2cby+kX5x0Uwbn0Z+bhrTxqUxsF+c32X6LirKGJwYz+DEeMbT+n3GnHMcPl7vhUiLrZNmn7dWVFNZXUtdwycvKI0yGNQvrtWtkxNncw3qF0dsdHhekqb/6dKmKO+ZEZ9/9E0eW7ONm/PH+V1Sr+WcY2tlNcuLAtcnvLvzIM7BsAEJfCEvg/ycdM4dPUjXI5wGMyO5bxzJfeMYm9Z6W+ccVbUNnzxWUlVLZbPPH+08yr6qOo6f4qFcA/vGNjtecuprTVIS44mL6b4wUUBISPKyBjHnU0P59SvbmDd5BEMGdN5ZLtK6hsYmCj86yIqiwKmoO/YfA2Di8P58Z3o2+TnpnDGsvw62+sDM6J8QS/+EWEaH8MDLo7UNzXZxBd86Od1rTVKbBczpXmuigJCQ3VYwgeVF5dy3bBM/v/osv8vp0apq6lmzeR8ristZtbGCw8friYuO4rwxg/n6RaPJz0lj6IC2zwqS8NIvPoZ+8TEhXVfUGdeaJMUHwqSjFBASssxBfbn+wix+/co2vnZ+Fp/KGOB3ST3K7kPHWVlczktF5azdtp/6RsfAvrFMz0ljRk46F41LJVEXjPUafeKiyRzUl8xBfdts29a1Jqs7WINOc5V2qaqpZ9p9qxmTlsif5k/Vbo3T4Jxjw+4jLPd2HW3YfQSAUSn9mJGbTn5OOpNGJBMTpgcwJXLoNFfpFkkJsfzL5eO447kPWbZhL7MmDvW7pIhS29DIm1v3s6K4nBVFFew9UkOUwTkjB3J7wQTyc9MZk5rod5kigAJCOuCLeZn87o0d3LN0I5dOSNMZM204cLSOlzcGzjpas7mSo3WN9I2L5uLsVPJz07l0fCqDEzu+n1ikqyggpN1ioqO4c04u1z35Ngvf+Ih/vni03yWFnW2V1Se3Ego/OkCTg/T+8cz99PCTt1PX3Uwl3IUUEGY2C/glEA38j3Pu3hbDzRs+GzgGfM05925b45rZt4CbgAZgsXPu+6fdI+kWF49LZdr4VB5ctYXPnZPBoF5+MVZjk+PdnYFTUZcXl7Ot8igAOUP7c9OlY8nPTWfisAERe6sG6Z3aDAgziwYeBmYAZcA6M1vknCtq1qwAyPZe5wKPAOe2Nq6ZXQrMBc50ztWaWRuXpEi4uWN2DrN++Sr/tWIz/zF3ot/ldLujtQ28uqWS5UUVvLypggNH64iNNqaOHsxXz8tiek4aGQPbPgNFJFyFsgUxBShxzm0DMLOnCazYmwfEXGChC5wStdbMks1sKJDVyrg3APc652oBnHMVndMl6S7Z6Ul8acoI/vjWTq47byRj01q/hUFPsPdwDSs3lrOiqJzXt+6nrqGJ/gkxXDYhjfzcdC4el0r/BD2BT3qGUAJiOFDa7HMZga2EttoMb2PcccBFZnY3UAN8zzm3ruXMzWw+MB9gxIgRIZQr3enm/Gz+8t4u7l5czG+un+J3OZ3OOUfxnqrA8YTictaXHQZgxKC+XDt1JPk56eRlDQzbe+mInI5QAiLYTtOWF0+cqk1r48YAA4GpwGTgGTMb7VpcmOGcewx4DALXQYRQr3SjwYnxfOuysfznko2s2VzJxeNCuN9AmKtraOKt7fu9W1tUsOvQcczg05nJfH/WeGbkpDM2LVHXgEiPF0pAlAGZzT5nALtDbBPXyrhlwJ+9QHjbzJqAFKAy5OolLHz1/Cz+sHYndy8u5vwxgyPywq5Dx+pYvamS5cXlvLKpkuraBhJio7goO5XvTM/m0glppJ7GLQtEIlEoAbEOyDazUcAuYB7wpRZtFgE3eccYzgUOO+f2mFllK+P+BbgMWG1m4wiEyb7T7I/4ID4mmtsLJnDDH9/lmcIyvnRuZOwK/Gj/0ZNXMa/bcZDGJkdqUjxXnjWU/Jx0LhibolNRpVdrMyCccw1mdhOwjMCpqk865zaY2QJv+KPAEgKnuJYQOM31+tbG9Sb9JPCkmX0I1AFfbbl7SSLHrIlDmJI1iF8s38SVZw0lKQwP1DY1Of5Wesi7PqGcLRXVAIxPT2LBJaOZkTuEM4frVFSRE3QvJuk068sOcdVDr3PDtDHcOmuC3+UAgTtivlayjxVF5azcWM6+6jqio4xzRw0iPydwv6MRg3UqqvRsuheT+O7MjGQ+O2k4T7y2nS9NGRHSXSi7QkVVDauKA7e2eHXLPmobmkiKj2HahDTycwJPWRvQN/y2cETCjQJCOtUtM8ez5IM9/PTFjTz0pUndMk/nHJvLA7e2WF5UznulhwAYntyHa6aMYEZuOpOzBnXrk7hEegIFhHSqoQP68I2Lx/DLlVu4/oIDnDNyUJfMp76xiXXbD7Dcuz6h9MBxAM7KTOZ7l48jPzed8elJOhVV5DQoIKTTfeOS0Ty9bif/8UIxz91wfqcd9D18vJ5XNleyoqiclzdVUFXTQHxMFBeOTeHGaWOZPiGNtP56FKpIZ1FASKfrGxfDLTMn8L3/e5/n1+9m7tnDOzyt0gPHTl7F/Na2AzQ0OQb3i6Ng4hDyc9K5MDuFvnH6ZyzSFfQ/S7rEZz89nN++sZ2fLt3I5blD6BMX2vUETU2O9bsOe1cxl7NxbxUAY9MS+aeLRjMjN42zMwcSrVNRRbqcAkK6RFSU8cM5uXzxsbU88do2bros+5Rta+obeWPrPpYXVbCyuJyKqlqiDCZnDeLOOTlMz0lnVErbD3kXkc6lgJAuc+7owcw6Ywi/Wr2Vq/MyP3Z8YF91Las2VrCiKHAq6vH6RhLjY7hkXCr5uYFTUQf28mdMiPhNASFd6raCCazcWM79L21i/sWjWV4UuD7h3Z0HcQ6GDUjgC3kZ5Oekc+7oQXp8qUgYUUBIl8pK6cfXzs/i8Ve380xhGQCfGj6Am6ePIz83jdyh/XUqqkiYUkBIl/vW9Gxq6psYNySJ/Jw0hg7o43dJIhICBYR0uf4JsfzkM73vkaQikU73HhARkaAUECIiEpQCQkREglJAiIhIUAoIEREJSgEhIiJBKSBERCQoBYSIiARlzjm/awiZmVUBm/yuowulAPv8LqIL9eT+9eS+gfoX6cY755LaO1KkXUm9yTmX53cRXcXMCtW/yNST+wbqX6Qzs8KOjKddTCIiEpQCQkREgoq0gHjM7wK6mPoXuXpy30D9i3Qd6l9EHaQWEZHuE2lbECIi0k0UECIiElRYBoSZzTKzTWZWYma3BRluZvagN3y9mU3yo86OCKFv08zssJm9573+zY86O8rMnjSzCjP78BTDI3nZtdW3SF92mWb2spkVm9kGM/tOkDaRvPxC6V9ELkMzSzCzt83sfa9vPw7Spv3LzjkXVi8gGtgKjAbigPeB3BZtZgNLAQOmAm/5XXcn9m0a8ILftZ5GHy8GJgEfnmJ4RC67EPsW6ctuKDDJe58EbO4p//fa0b+IXIbe8kj03scCbwFTT3fZheMWxBSgxDm3zTlXBzwNzG3RZi6w0AWsBZLNbGh3F9oBofQtojnn1gAHWmkSqcsulL5FNOfcHufcu977KqAYGN6iWSQvv1D6F5G85VHtfYz1Xi3PQGr3sgvHgBgOlDb7XMYnF2IobcJRqHWf520qLjWzM7qntG4TqcsuVD1i2ZlZFvBpAn+JNtcjll8r/YMIXYZmFm1m7wEVwHLn3Gkvu3C81YYF+a5lEobSJhyFUve7wEjnXLWZzQb+AmR3dWHdKFKXXSh6xLIzs0TgWeBm59yRloODjBJRy6+N/kXsMnTONQJnm1ky8JyZTXTONT9e1u5lF45bEGVAZrPPGcDuDrQJR23W7Zw7cmJT0Tm3BIg1s5TuK7HLReqya1NPWHZmFktg5flH59yfgzSJ6OXXVv96wjJ0zh0CVgOzWgxq97ILx4BYB2Sb2SgziwPmAYtatFkEXOcdlZ8KHHbO7enuQjugzb6Z2RAzM+/9FALLaH+3V9p1InXZtSnSl51X+xNAsXPuF6doFrHLL5T+ReoyNLNUb8sBM+sD5AMbWzRr97ILu11MzrkGM7sJWEbgrJ8nnXMbzGyBN/xRYAmBI/IlwDHger/qbY8Q+/Z54AYzawCOA/OcdwpCJDCzpwicCZJiZmXAjwgcMIvoZQch9S2ilx1wAXAt8IG3LxvgB8AIiPzlR2j9i9RlOBT4nZlFEwi1Z5xzL5zuelO32hARkaDCcReTiIiEAQWEiIgEpYAQEZGgFBAiIhKUAkJERIJSQIiISFAKCBERCer/A+s1f7DU/rdOAAAAAElFTkSuQmCC\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",
    "pd.DataFrame(arma_results.forecast(steps=4)[0]).plot(title=\"5 Day Returns Forecast\")\n",
    "\n",
    "# Set margins to 0 to eliminate whitespace on x-axis (required importing pyplot from matplotlib):\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting the Settle Price using an ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. Using the *raw* Yen **Settle 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. Construct a 5 day forecast for the Settle Price. What does the model forecast will happen to the Japanese Yen in the near term?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Settle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>9338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>9303.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>9251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-14</th>\n",
       "      <td>9265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>9221.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Settle\n",
       "Date              \n",
       "2019-10-09  9338.0\n",
       "2019-10-10  9303.5\n",
       "2019-10-11  9251.0\n",
       "2019-10-14  9265.0\n",
       "2019-10-15  9221.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# Create a new series using raw Yen Settle Prices as opposed to the Returns on those Settle prices (which was used for the ARMA model):\n",
    "settle = (yen_futures[[\"Settle\"]])\n",
    "settle = settle.replace(-np.inf, np.nan).dropna()\n",
    "settle.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:581: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n",
      "/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:581: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n"
     ]
    }
   ],
   "source": [
    "# Estimate an ARIMA Model:\n",
    "arima_model = ARIMA(settle.Settle, order=(5,1,1))\n",
    "\n",
    "# Fit the model\n",
    "arima_results = arima_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>D.Settle</td>     <th>  No. Observations:  </th>    <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(5, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-41944.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>64.281</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 03 Dec 2020</td> <th>  AIC                </th>  <td>83905.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>20:17:10</td>     <th>  BIC                </th>  <td>83960.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th>  <td>83924.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </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>    0.3161</td> <td>    0.700</td> <td>    0.452</td> <td> 0.652</td> <td>   -1.056</td> <td>    1.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Settle</th> <td>    0.2821</td> <td>    0.699</td> <td>    0.403</td> <td> 0.687</td> <td>   -1.088</td> <td>    1.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.Settle</th> <td>    0.0007</td> <td>    0.016</td> <td>    0.043</td> <td> 0.966</td> <td>   -0.030</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.D.Settle</th> <td>   -0.0127</td> <td>    0.012</td> <td>   -1.032</td> <td> 0.302</td> <td>   -0.037</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.D.Settle</th> <td>   -0.0137</td> <td>    0.015</td> <td>   -0.889</td> <td> 0.374</td> <td>   -0.044</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.D.Settle</th> <td>   -0.0012</td> <td>    0.018</td> <td>   -0.064</td> <td> 0.949</td> <td>   -0.036</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Settle</th> <td>   -0.2972</td> <td>    0.699</td> <td>   -0.425</td> <td> 0.671</td> <td>   -1.667</td> <td>    1.073</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.8917</td> <td>          -1.3787j</td> <td>           2.3408</td> <td>          -0.1002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           1.8917</td> <td>          +1.3787j</td> <td>           2.3408</td> <td>           0.1002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>          -2.2697</td> <td>          -3.0209j</td> <td>           3.7785</td> <td>          -0.3526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th> <td>          -2.2697</td> <td>          +3.0209j</td> <td>           3.7785</td> <td>           0.3526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th> <td>         -11.0585</td> <td>          -0.0000j</td> <td>          11.0585</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           3.3652</td> <td>          +0.0000j</td> <td>           3.3652</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:               D.Settle   No. Observations:                 7514\n",
       "Model:                 ARIMA(5, 1, 1)   Log Likelihood              -41944.619\n",
       "Method:                       css-mle   S.D. of innovations             64.281\n",
       "Date:                Thu, 03 Dec 2020   AIC                          83905.238\n",
       "Time:                        20:17:10   BIC                          83960.635\n",
       "Sample:                             1   HQIC                         83924.259\n",
       "                                                                              \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const              0.3161      0.700      0.452      0.652      -1.056       1.688\n",
       "ar.L1.D.Settle     0.2821      0.699      0.403      0.687      -1.088       1.653\n",
       "ar.L2.D.Settle     0.0007      0.016      0.043      0.966      -0.030       0.032\n",
       "ar.L3.D.Settle    -0.0127      0.012     -1.032      0.302      -0.037       0.011\n",
       "ar.L4.D.Settle    -0.0137      0.015     -0.889      0.374      -0.044       0.016\n",
       "ar.L5.D.Settle    -0.0012      0.018     -0.064      0.949      -0.036       0.034\n",
       "ma.L1.D.Settle    -0.2972      0.699     -0.425      0.671      -1.667       1.073\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.8917           -1.3787j            2.3408           -0.1002\n",
       "AR.2            1.8917           +1.3787j            2.3408            0.1002\n",
       "AR.3           -2.2697           -3.0209j            3.7785           -0.3526\n",
       "AR.4           -2.2697           +3.0209j            3.7785            0.3526\n",
       "AR.5          -11.0585           -0.0000j           11.0585           -0.5000\n",
       "MA.1            3.3652           +0.0000j            3.3652            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "arima_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the above ARIMA model, the autoregressive term has a p-value (.65) that is greater than the significance level of 0.05. We can conclude that the coefficient for the autoregressive term is NOT statistically significant, and should NOT be kept in the model.\n"
     ]
    }
   ],
   "source": [
    "print(\"In the above ARIMA model, the autoregressive term has a p-value (.65) that is greater than the significance level of 0.05. We can conclude that the coefficient for the autoregressive term is NOT statistically significant, and should NOT be kept in the model.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 5 Day Price Forecast\n",
    "pd.DataFrame(arima_results.forecast(steps=5)[0]).plot(title=\"5 Day Futures Price Forecast\")\n",
    "\n",
    "# Set margins to 0 to eliminate whitespace on x-axis (required importing pyplot from matplotlib):\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "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 futures 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 futures Settle *Returns*, estimate a GARCH model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arch as arch\n",
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "garch_model = arch_model(returns.Settle, mean=\"Zero\", vol=\"GARCH\", p=2, q=1)\n",
    "\n",
    "# Fit the model\n",
    "garch_results = garch_model.fit(disp=\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Zero Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Settle</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>  -7461.93</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   14931.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14959.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Thu, Dec 03 2020</td>  <th>  Df Residuals:      </th>    <td>7510</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>20:17:35</td>      <th>  Df Model:          </th>      <td>4</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>4.2896e-03</td> <td>2.057e-03</td> <td>    2.085</td> <td>3.708e-02</td>  <td>[2.571e-04,8.322e-03]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th> <td>    0.0381</td> <td>1.282e-02</td> <td>    2.970</td> <td>2.974e-03</td>  <td>[1.295e-02,6.321e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th>   <td>0.0000</td>   <td>1.703e-02</td>   <td>0.000</td>   <td>    1.000</td> <td>[-3.338e-02,3.338e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beta[1]</th>  <td>    0.9536</td> <td>1.420e-02</td> <td>   67.135</td>   <td>0.000</td>      <td>[  0.926,  0.981]</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:                 Settle   R-squared:                       0.000\n",
       "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:               -7461.93\n",
       "Distribution:                  Normal   AIC:                           14931.9\n",
       "Method:            Maximum Likelihood   BIC:                           14959.6\n",
       "                                        No. Observations:                 7514\n",
       "Date:                Thu, Dec 03 2020   Df Residuals:                     7510\n",
       "Time:                        20:17:35   Df Model:                            4\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      4.2896e-03  2.057e-03      2.085  3.708e-02  [2.571e-04,8.322e-03]\n",
       "alpha[1]       0.0381  1.282e-02      2.970  2.974e-03  [1.295e-02,6.321e-02]\n",
       "alpha[2]       0.0000  1.703e-02      0.000      1.000 [-3.338e-02,3.338e-02]\n",
       "beta[1]        0.9536  1.420e-02     67.135      0.000      [  0.926,  0.981]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "garch_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-10-15'"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<arch.univariate.base.ARCHModelForecast at 0x122169400>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "forecasts = garch_results.forecast(start=last_day, horizon=forecast_horizon)\n",
    "forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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>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>2019-10-15</th>\n",
       "      <td>7.434048</td>\n",
       "      <td>7.475745</td>\n",
       "      <td>7.516867</td>\n",
       "      <td>7.557426</td>\n",
       "      <td>7.597434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 h.1       h.2       h.3       h.4       h.5\n",
       "Date                                                        \n",
       "2019-10-15  7.434048  7.475745  7.516867  7.557426  7.597434"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2019-10-15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>h.1</th>\n",
       "      <td>7.434048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.2</th>\n",
       "      <td>7.475745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.3</th>\n",
       "      <td>7.516867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.4</th>\n",
       "      <td>7.557426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.5</th>\n",
       "      <td>7.597434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2019-10-15\n",
       "h.1     7.434048\n",
       "h.2     7.475745\n",
       "h.3     7.516867\n",
       "h.4     7.557426\n",
       "h.5     7.597434"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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 Exchange Rate Forecast of Volatility\")\n",
    "\n",
    "# Set margins to 0 to eliminate whitespace on x-axis (required importing pyplot from matplotlib):\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the above time series analysis, I would *not* in fact buy the yen now.  First off, both the ARMA and the ARIMA model have p-values that are greater than .05 (.42 and .65, respectively), and therefore, the coefficient for the autoregressive term is *not* statistically significant and those terms should not be kept in the models.  Additionally, as the upward-trending GARCH Model shows us, the exchange rate risk is expected to increase, and a more conservative investor may not be comfortable with this level of risk.  Although I would not use either of these models, the AIC of the ARMA model (15,798) is significantly lower than that of the ARIMA model (83,905), and therefore it is performing significantly better.\n",
    "\n",
    "When it comes to investing real money, I would not base my decisions solely on the results of these models.  Before using them, I would want to improve them by training the models and making them statistically significant.   If I did that, I would use them as one of the factors I consider, but would want to look at other factors as well before making any investment decisions.\n",
    "\n",
    "However, a more opportunistic investor may take a look at this GARCH plot, expect increased short-term volatility in the markets, and invest in derivatives.  Prices of derivative assets tend to increase as volatility increases, and a prudent investor may take advantage of this."
   ]
  }
 ],
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