time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import numpy as np\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import statsmodels.api as sm\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Read Historical Daily Yen Futures Data\n",
    "In this notebook, you will load historical 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": 76,
   "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": 76,
     "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": 77,
   "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>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": 77,
     "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": "markdown",
   "metadata": {},
   "source": [
    " Start by plotting the \"Settle\" price. Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 78,
     "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 just the \"Settle\" column from the dataframe:\n",
    "# YOUR CODE HERE!\n",
    "yen_futures.Settle.plot()"
   ]
  },
  {
   "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": 79,
   "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",
    "# YOUR CODE HERE!\n",
    "ts_noise, ts_trend = sm.tsa.filters.hpfilter(yen_futures['Settle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": 80,
     "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",
    "# YOUR CODE HERE!\n",
    "df = None\n",
    "\n",
    "df = yen_futures[[\"Settle\"]].copy()\n",
    "\n",
    "df[\"Noise\"] = ts_noise\n",
    "df[\"Trend\"] = ts_trend\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 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",
    "# YOUR CODE HERE!\n",
    "fig = plt.figure(figsize = (20,10))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "df[[\"Settle\",\"Trend\"]][\"2015\":].plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Noise\n",
    "# YOUR CODE HERE!\n",
    "ts_noise.plot(figsize=(20,10))"
   ]
  },
  {
   "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",
    "No, all of the p-values are above .05.\n",
    "\n",
    "3. Plot the 5-day forecast of the forecasted returns (the results forecast from ARMA model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series using \"Settle\" 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 = (yen_futures[[\"Settle\"]].pct_change()*100)\n",
    "returns = returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\laure\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:218: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "\n",
    "# Estimate and ARMA model using statsmodels (use order=(2, 1))\n",
    "# YOUR CODE HERE!\n",
    "model = ARMA(returns, order = (2,1))\n",
    "\n",
    "# Fit the model and assign it to a variable called results\n",
    "# YOUR CODE HERE!\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Wed, 18 Nov 2020</td> <th>  AIC                </th> <td>15798.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>16:48:14</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> 0.422</td> <td>   -0.009</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Settle</th> <td>   -0.3061</td> <td>    1.278</td> <td>   -0.240</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>    0.2945</td> <td>    1.277</td> <td>    0.231</td> <td> 0.818</td> <td>   -2.209</td> <td>    2.798</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>          -3.3367</td> <td>          +0.0000j</td> <td>           3.3367</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>        -157.2576</td> <td>          +0.0000j</td> <td>         157.2576</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -3.3957</td> <td>          +0.0000j</td> <td>           3.3957</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:                Wed, 18 Nov 2020   AIC                          15798.142\n",
       "Time:                        16:48:14   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      0.422      -0.009       0.022\n",
       "ar.L1.Settle    -0.3061      1.278     -0.240      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     0.2945      1.277      0.231      0.818      -2.209       2.798\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -3.3367           +0.0000j            3.3367            0.5000\n",
       "AR.2         -157.2576           +0.0000j          157.2576            0.5000\n",
       "MA.1           -3.3957           +0.0000j            3.3957            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "# YOUR CODE HERE!\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'ARMA Five Day Returns Forecast'}>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tjZtmrpmtM7MmMzvlWZWpFBSIq+CNbQfYtPuDVIcjItIl2kwAZpYN/BiYBUwAbjGzCXHNZgHjwtcdwE9ixj0EzEww67XAjcDL7Y66G9xwfgU5WaY7g0Wk10rmCGA6sMndN7t7HfAoMCeuzRzgYQ8sB4rNrAzA3V8G9sXP1N3fdveNHQu/6zQXiHvijWoViBORXimZBFAOxHaGV4fD2tsm7cyfFmHP4Tp+rwJxItILJZMALMGw+Ntkk2lzWszsDjOrNLPK2trazphl0i4/s5Qh/fvoOQEi0islkwCqgUjM5wog/vrIZNqcFne/192j7h4tLS3tjFkmLSc7i5umVvCHjbUqECcivU4yCWAFMM7MRptZHrAAWBLXZglwa3g10AzgoLv3iqesz4tGaGxyHn9DBeJEpHdpMwG4ewNwF/AC8DawyN3XmdmdZnZn2GwpsBnYBNwHfK55ejN7BHgNGG9m1Wb22XD4DWZWDVwIPGtmL3TienWa0YP7Mn1UCYsrVSBORHoXS6edWjQa9crKym5f7mMrq/ny4jdZ9LcXMn10SbcvX0SkI8xspbufcr+V7gROwuxzhtGvTw6LdDJYRHoRJYAkFOblcO15ZTy7ZgcfHK9PdTgiIp1CCSBJc6MRjtU38uyaXnFuW0RECSBZ50eKGTekHwvVDSQivYQSQJKCAnERVm07wLu7VCBORNKfEkA73DClPCwQp6MAEUl/SgDtMLhfHz5+9hCeeKNGBeJEJO0pAbTT/GkR9h6p46W3VSBORNKbEkA7XTaulKEDVCBORNKfEkA75WRncdOUCv6wcTe7VCBORNKYEsBpmBeN0OTw+Bt6WpiIpC8lgNMwanBfpo8uYXFltQrEiUjaUgI4TfOjEbbsOcKft5zytEsRkbSgBHCaZp0sEKduIBFJT0oApykoEDecpW+pQJyIpCclgA6YF63gWH0jz6hAnIikISWADpgcKebMof1YuEL3BIhI+lEC6IDmAnGrqw7wjgrEiUiaUQLooBvODwvE6ShARNKMEkAHDerXhyvPHsqTq2qoa1CBOBFJH0klADObaWYbzWyTmd2dYLyZ2T3h+DVmNiVm3INmttvM1sZNU2JmL5rZu+HPgR1fndRoLhD3+w27Uh2KiEjS2kwAZpYN/BiYBUwAbjGzCXHNZgHjwtcdwE9ixj0EzEww67uBl9x9HPBS+DktXXZmKcMG5OueABFJK8kcAUwHNrn7ZnevAx4F5sS1mQM87IHlQLGZlQG4+8tAottl5wA/D9//HLj+NOLvEbKzjJumlvPHjbvZeVAF4kQkPSSTAMqB2DOc1eGw9raJN9TddwCEP4ckamRmd5hZpZlV1tbWJhFuasydqgJxIpJekkkAlmBYfAW0ZNqcFne/192j7h4tLS3tjFl2iVGD+3LB6BIWVVapQJyIpIVkEkA1EIn5XAFsP4028XY1dxOFP9P+EVvzp0XYuvcor6tAnIikgWQSwApgnJmNNrM8YAGwJK7NEuDW8GqgGcDB5u6dViwBbgvf3wY83Y64e6RZk8ro3ydHD40XkbTQZgJw9wbgLuAF4G1gkbuvM7M7zezOsNlSYDOwCbgP+Fzz9Gb2CPAaMN7Mqs3ss+Go7wJXmdm7wFXh57RWkJfNtZODAnGHVCBORHo4S6f+6mg06pWVlakOo1Wrqw5w/Y9f5Ts3nMNfXjAi1eGIiGBmK909Gj9cdwJ3svMqihg/tD8L1Q0kIj2cEkAnMzPmRit4s+oAG3eqQJyI9FxKAF3gxikV5GabTgaLSI+mBNAFSvrmcdUEFYgTkZ5NCaCLzI1G2HekjpfeVoE4EemZlAC6yGXjggJxOhksIj2VEkAXyc4ybp5awcvv1LLj4LFUhyMicgolgC40N1oRFIhbqQJxItLzKAF0oZGD+jJjTAmLKqtpakqfG+5EJDMoAXSx+dMibNunAnEi0vMoAXSxmRODAnGLdTJYRHoYJYAuVpCXzXWTh7N0rQrEiUjPogTQDeZFIxyvb+I3b7b1iAQRke6jBNANzq0o4qxh/Vm0Qt1AItJzKAF0g6BAXIQ3qw+yYeehVIcjIgIoAXSbG84vDwrErdA9ASLSMygBdJOSvnlcPWEYT66q5kRDY6rDERFRAuhOc6MV7D9az0tv7051KCIiSgDd6dJxpZQV5bNQJ4NFpAdQAuhGJwvEvVvL9gMqECciqZVUAjCzmWa20cw2mdndCcabmd0Tjl9jZlPamtbMzjOz18zsLTP7jZkN6JxV6tnmTo3gKhAnIj1AmwnAzLKBHwOzgAnALWY2Ia7ZLGBc+LoD+EkS094P3O3u5wBPAv/Y4bVJAyMGFXLhmEEsXqkCcSKSWskcAUwHNrn7ZnevAx4F5sS1mQM87IHlQLGZlbUx7Xjg5fD9i8BNHVyXtNFcIG75lr2pDkVEMlgyCaAciD1rWR0OS6ZNa9OuBa4L388FIokWbmZ3mFmlmVXW1tYmEW7PN3PSMPrn57C4Ut1AIpI6ySQASzAsvu+ipTatTfsZ4PNmthLoD9QlWri73+vuUXePlpaWJhFuz5efm82cycNZ+tYODh5TgTgRSY1kEkA1H/12XgHEVzVrqU2L07r7Bne/2t2nAo8A77Uv9PQ2LxrhRIMKxIlI6iSTAFYA48xstJnlAQuAJXFtlgC3hlcDzQAOuvuO1qY1syHhzyzgn4CfdsoapYlzysMCcXpOgIikSJsJwN0bgLuAF4C3gUXuvs7M7jSzO8NmS4HNwCbgPuBzrU0bTnOLmb0DbCA4KvhZp61VGjAz5k+LsKb6IG/vUIE4Eel+5p4+lyJGo1GvrKxMdRidZv+ROi74zkt8csYIvnXtxFSHIyK9lJmtdPdo/HDdCZxCA/vmcdXEoTy5qkYF4kSk2ykBpNi8aIQDR+v53XoViBOR7qUEkGKXjB3M8KJ8FupksIh0MyWAFGsuELdMBeJEpJspAfQAc6NBgbjHVCBORLqREkAPECkp5KIzBrF4ZZUKxIlIt1EC6CHmT4tQte8YyzerQJyIdA8lgB7imolBgTjdGSwi3UUJoIfIz83m+snlPLd2pwrEiUi3UALoQZoLxC1ZXZPqUEQkAygB9CCTygdwdtkAFuk5ASLSDZQAehAzY360grdqDrJ+uwrEiUjXUgLoYeZMLicvO0sng0WkyykB9DAD++Zx9cShPLVaBeJEpGspAfRAzQXiXly/K9WhiEgvpgTQA108djDlxQUsXKFuIBHpOkoAPVB2lnHT1Ape2bSHGhWIE5EuogTQQ82dWhEUiNMloSLSRZQAeqhISSEXj1WBOBHpOkoAPdi8aITq/cd4TQXiRKQLJJUAzGymmW00s01mdneC8WZm94Tj15jZlLamNbPJZrbczFabWaWZTe+cVeo9rpk4jAH5OToZLCJdos0EYGbZwI+BWcAE4BYzmxDXbBYwLnzdAfwkiWm/B/yLu08Gvhl+lhj5udlcf345z6/bycGjKhAnIp0rmSOA6cAmd9/s7nXAo8CcuDZzgIc9sBwoNrOyNqZ1YED4vgjY3sF16ZXmRSPUNTTx9JsqECcinSuZBFAOxPZBVIfDkmnT2rRfBL5vZlXAvwFfS7RwM7sj7CKqrK2tTSLc3mVSeRETygaoNISIdLpkEoAlGBZ/WUpLbVqb9u+AL7l7BPgS8ECihbv7ve4edfdoaWlpEuH2PvOnRVhbc4h12w+mOhQR6UWSSQDVQCTmcwWndte01Ka1aW8DngjfLyboLpIE5kweTl5OFot1T4CIdKJkEsAKYJyZjTazPGABsCSuzRLg1vBqoBnAQXff0ca024HLw/cfA97t4Lr0WsWFeVwzcRhPrqrheL0KxIlI52gzAbh7A3AX8ALwNrDI3deZ2Z1mdmfYbCmwGdgE3Ad8rrVpw2n+Bvh3M3sT+A7B1UPSgnnRCg4eU4E4Eek85p4+d5lGo1GvrKxMdRgp0dTkXPq9PzCmtC+/+OwFqQ5HRNKIma1092j8cN0JnCaysoybwwJx1fuPpjocEekFlADSyNxoBQCPrdTJYBHpOCWANFIxsJCLzxjM4spqFYgTkQ5TAkgz86ZFqDlwjD+9pwJxItIxSgBp5uoJQykqyGWh7gwWkQ5SAkgz+bnZXD95OC+s28mBo3WpDkdE0pgSQBqaNy0sELda9fNE5PQpAaShicOLmDhcBeJEpGOUANLU/GkR1m0/xNoaFYgTkdOjBJCm5pxXHhaI01GAiJweJYA0VVSYy8yJw3hq9XYViBOR06IEkMbmRSMcPFbPb1UgTkROgxJAGrvojEGUFxewSA+NF5HToASQxrKyjLnRCl59bw9V+1QgTkTaRwkgzd08VQXiROT0KAGkuYqBhVwydjCPraymUQXiRKQdlAB6gXnR5gJxe1IdioikESWAXuCq5gJxOhksIu2gBNAL5Odmc8P55fx23S4ViBORpCkB9BLzohHqGpt4alVNqkMRkTSRVAIws5lmttHMNpnZ3QnGm5ndE45fY2ZT2prWzBaa2erw9b6Zre6UNcpQE4YPYFL5ABZV6mogEUlOmwnAzLKBHwOzgAnALWY2Ia7ZLGBc+LoD+Elb07r7fHef7O6TgceBJzpjhTLZ/GiE9TtUIE5EkpPMEcB0YJO7b3b3OuBRYE5cmznAwx5YDhSbWVky05qZAfOARzq4LhnvurBAnMpEi0gykkkA5UDsHqU6HJZMm2SmvRTY5e7vJlq4md1hZpVmVllbW5tEuJmrqDCXWZOG8dSqGhWIE5E2JZMALMGw+DuOWmqTzLS30Mq3f3e/192j7h4tLS1tNVAJTgYfOt7AC+t2pjoUEenhkkkA1UAk5nMFEP8swpbatDqtmeUANwILkw9ZWnPhmEFUDCxQN5CItCmZBLACGGdmo80sD1gALIlrswS4NbwaaAZw0N13JDHtlcAGd9elK50kK8uYOzXCq5v2qkCciLSqzQTg7g3AXcALwNvAIndfZ2Z3mtmdYbOlwGZgE3Af8LnWpo2Z/QJ08rfT3RytwAwWq0CciLTC3NOngFg0GvXKyspUh5EWPvXA67y3+zDLvvoxsrMSnYoRkUxhZivdPRo/XHcC91Lzp0XYfvA4r25SgTgRSUwJoJe6asJQigtzWaiTwSLSAiWAXqpPTjbXTy7nxXW72H9EBeJE5FRKAL3YyQJxq1UgTkROlZPqAKTrTBg+gHPKi1i4oorbLxpFUHVDOlPVvqP87NX3eXXTHoYW5TOipIDIwEJGlBQSKSlkxKBCBuTnpjpMkYSUAHq5edMi/PNTa1lbc4hzKopSHU6vsWrbfu5ftoXn1u4gy4wLzxjE/iN1vFl1gIPH6j/Strgw96NJoaSQSEkBI0oKGV5cQG62DsQlNZQAernrzhvO/3hmPYsqq5QAOqixyXlx/U7uW7aFlVv30z8/h7+5bAy3XzSKsqKCk+0OHqunat9RqvYdZdu+o1TtP8q2fcdYv+MQv12/k/rGDy+9zjIYXvzhUcOIQUGSiAwMEkRJ3zwduUmXUQLo5YoKwgJxq2v4xifOJj83O9UhpZ0jJxpYXFnFg6++z7Z9R4mUFPCtaycwNxqhX59T/4WKCnIpKi9iUvmpCbexydl16DjbmpNDTKJ4acNu9hw+8ZH2ffOyg4QQHjmMKPnwSKJiYIF+n9IhSgAZYF40wlOrt/P82p1cf358MVZpyY6Dx/j5n7by69e3cuh4A1NGFPO1WWdx9cRhp31zXXaWMby4gOHFBcwYM+iU8UfrGqjad+xkUmhOElv3HmHZu7Ucr2/6SPuhA/qcTAixRxEjSgop7deHLN0EKK1QAsgAM8YMIlISFIhTAmjb2pqDPPDKFn7z5naa3Jk1qYzPXDKaqSMHdvmyC/NyGD+sP+OH9T9lnLtTe/hEeNRw7GSC2LbvKMvf28uTh2qIvbE/LyfrZFfSySQR8z7R0YtkFv0FZIDmAnE/ePEdtu09yohBhakOqcdpanL+sHE39y3bzPLN++ibl82tF47i0xePIlLSM7aXmTGkfz5D+uczdeSp4080NFKz/1h43iE8itgbJIjK9/fzwYmGj7Qf1DePipPdSkGiiAwMkkNZUT45Ojnd6ykBZIibp1bwH797h8dWVvH3V49PdTg9xvH6Rh5/o5oHXtnC5tojlBXl8/XZZzF/2giKCtLr8s0+OdmMKe3HmNJ+p4xzdw4eq//IUUNzV9ObVQdY+tYOGps+PHzIyTLKBxacTAgj4q5eKirI1cnpFjQ1OccbGjlW18ix+kaO1zdyrK6JY/XB52N1DeHPppjxjSfHH69r5Gjs53D8v954DtFRJZ0aqxJAhhheXMCl40pZvLKaL1x5ZsYXiKv94AS/eO19fvn6NvYdqeOc8iL+z4LJzD6nrFdelmlmFBfmUVyYx7kVxaeMb2hsYsfB4x899xAeTbywbif74u4m75+fk7BraURJIeXFBeTl9Mxt2NDYFLOjDd4fDXfI8Tvq4zE74WN1cTvpRDvtcNiJhqa2A4mTZUH3X35uNoV52RTkZpOfl01BbhaD+uZRMDC7S074KwFkkPnRCJ//9Ru8smkPl5+ZmU9Xe2fXB9y/bDNPrdpOfVMTHz9rKH9z6Wimjy7J6G+0OdlZJ3fkFyUYf/hEw4eXtcb8fGfXB7y0YTd1MTs9MygbkP+R8w2xPwf3O/XSVnenrrGJ43VNHK1vaPHbc/NO+Whdyzvij+y0Y34er2/8yCW4ycrLziI/N4uCcMdckJdDQfi5uDCX/Nzm4af+PDku5nNhgvG52ZaSvz8lgAxy5YQhDCzMZdGKqoxKAO7OK5v2cN+yLbz8Ti35uVnMm1bBZy4enbC7RE7Vr08OZ5cN4OyyAaeMa2pydn9w4pTksG3fUV5+t5Zdhz56aWtBbjbDivKpb2z6yA686TQq0+fnZp3cwebnfbhz7dcnh9J+fU7ujPPDHW5h/I45bnxBuINu/pyfk9Wrz4UoAWSQPjnZXH9+Ob9cvpV9R+oo6ZuX6pC61ImGRp5evZ0Hlm1h464PKO3fhy9ffSafvGAkA3v5unenrCxjWFE+w4rymT761D7q4/WNVO//8LzDtn1H2XnoOH2ys8JujsTfoGN3ysHwrPDbd/C5T06WLnPtICWADDN/WoSfvfo+T62q4TOXjE51OF1i/5E6fvX6Vn7+2lZqPzjBWcP68/2bz+W6ycPpk6Mbp7pbfm42Y4f0Z+yQUy9tldRSAsgwZw0bwLkVRSyqrOLTF/euAnGbaw/z4KtbeGxlNcfrm7j8zFL+et5oLhk7uFetp0hnUQLIQPOiEf7pqbW8VXMw4RUh6cTdeX3LPu5ftpmXNuwmNyuLG84v57OXjubMofrGKdIaJYAMdO15w/nvz6xn4YqqtE0A9Y1NLH1rB/cv28JbNQcp6ZvHf/3YOD41YySl/fukOjyRtJDU6W0zm2lmG81sk5ndnWC8mdk94fg1ZjYlmWnN7L+G49aZ2fc6vjqSjKKCXGafU8aS1ds5VteY6nDa5eCxev7ff77HZd/7A194dDVH6hr4zg3n8Ke7P8bfX3Wmdv4i7dDmEYCZZQM/Bq4CqoEVZrbE3dfHNJsFjAtfFwA/AS5obVoz+y/AHOBcdz9hZkM6c8WkdXOjFTy5qobn1+3ghvMrUh1Om6r2HeXBV7ewaEUVR+oauXDMIP7nDZO44swhuhJE5DQl0wU0Hdjk7psBzOxRgh13bAKYAzzs7g4sN7NiMysDRrUy7d8B33X3EwDuvrtzVkmSMWP0IEaUFLJoRXWPTgArt+7ngVc28/zanWSZce15w/nsJaMTlloWkfZJJgGUA1Uxn6sJvuW31aa8jWnPBC41s/8JHAe+7O4r4hduZncAdwCMGDEiiXAlGUGBuAr+/cV32Lr3CCMH9U11SCc1NjkvrNvJ/cs288a2AwzIz+GOy87g9otGMawoP9XhifQaySSARMfX8ffstdSmtWlzgIHADGAasMjMxoRHER82dr8XuBcgGo2exr2C0pKboxX84Hfv8NjKav6hBxSIO3yigUUrqvjZn7ZQte8YI0oK+Xb44JW+Kl0s0umS+a+qBiIxnyuA7Um2yWtl2mrgiXCH/2czawIGA7VJRy8dUlZUwGXjSnlsZTVfTGGBuB0Hj/HQq+/z6z9v44PjDURHDuQbsydw1YShGV+0TqQrJZMAVgDjzGw0UAMsAP4yrs0S4K6wj/8C4KC77zCz2lamfQr4GPBHMzuTIFns6eD6SDvNnxbhc796g2Xv1nLF+O49D7+25iD3LdvMs2t2BA9eOaeMv75kNOeP6PoHr4hIEgnA3RvM7C7gBSAbeNDd15nZneH4nwJLgdnAJuAo8OnWpg1n/SDwoJmtBeqA2+K7f6TrXXn2UEr65rGosqpbEkBTk/P7Dbu5/5XgwSv9+uRw20WjuP2invPgFZFMkVTHqrsvJdjJxw77acx7Bz6f7LTh8Drgr9oTrHS+vJwsrp9czi+Wv9+lBeKO1QUPXnnwlS1s3nOE4UX5fGP22cyfHmFAfno9eEWkt9CZNWH+tAgPvrqFJ1fV8NlOLhC3+4Pj/OK1rfxy+Vb2H63n3Ioi7rnlfGZNGtYrH7wikk6UAITxw/pzXkURi1ZU8ZlOKhC3YechHli2hadXBw9euersofz1pWOYNmqgCrOJ9BBKAALAvGkRvvHkWtZUH+S8SPFpzcPdefndPdy/bDPL3t1DQW42C6ZH+PTFoxk9uOfcZyAiASUAAWIKxFVWtTsBnGho5OlV27n/lc28s+swQ/r34R+vGc8nLxhBcaEevCLSUykBCAAD8nOZPamM36zezj9/YgIFeW0/OGXfkTp+uXwrD7+2lT2Hgwev/Pvc87j2vOE99qHgIvIhJQA5aW40whOranhu7Q5unNJyfaD3ag/zwCtbeHxlNScamrhifCl/c+kYLjpjkPr3RdKIEoCcNGNMCSMHFbKosuqUBODuLN/84YNX8nKyuPH8cj57yWjG6cErImlJCUBOMgsKxP3bbz8sEFff2MSza3Zw/yubWVtziJK+eXzh4+P41IUjGdxPtfdF0pkSgHzETVMr+MGL7/CzV99nWFE+D736PjsPHeeM0r78643ncMP55eTn6sHqIr2BEoB8RFlRAZedWcpDf3ofgIvHDuJfbzyHy88s1YNXRHoZJQA5xZevHs/owX25eWoFE4frwSsivZUSgJxiUnmRnrglkgF0sbaISIZSAhARyVBKACIiGUoJQEQkQykBiIhkKCUAEZEMpQQgIpKhlABERDKUBc9zTw9mVgtsPc3JBwN7OjGczqK42kdxtY/iap+eGhd0LLaR7l4aPzCtEkBHmFmlu0dTHUc8xdU+iqt9FFf79NS4oGtiUxeQiEiGUgIQEclQmZQA7k11AC1QXO2juNpHcbVPT40LuiC2jDkHICIiH5VJRwAiIhJDCUBEJEP1ugRgZjPNbKOZbTKzuxOMNzO7Jxy/xsym9JC4rjCzg2a2Onx9sxtietDMdpvZ2hbGp2pbtRVXt2+rcLkRM/uDmb1tZuvM7AsJ2nT7NksyrlT8feWb2Z/N7M0wrn9J0CYV2yuZuFLyNxYuO9vMVpnZMwnGde72cvde8wKygfeAMUAe8CYwIa7NbOA5wIAZwOs9JK4rgGe6eXtdBkwB1rYwvtu3VZJxdfu2CpdbBkwJ3/cH3ukhf1/JxJWKvy8D+oXvc4HXgRk9YHslE1dK/sbCZf898OtEy+/s7dXbjgCmA5vcfbO71wGPAnPi2swBHvbAcqDYzMp6QFzdzt1fBva10iQV2yqZuFLC3Xe4+xvh+w+At4HyuGbdvs2SjKvbhdvgcPgxN3zFX3WSiu2VTFwpYWYVwCeA+1to0qnbq7clgHKgKuZzNaf+IyTTJhVxAVwYHpY+Z2YTuzimZKRiWyUrpdvKzEYB5xN8e4yV0m3WSlyQgm0WdmesBnYDL7p7j9heScQFqfkb+9/AV4CmFsZ36vbqbQnAEgyLz+zJtOlsySzzDYJ6HecBPwSe6uKYkpGKbZWMlG4rM+sHPA580d0PxY9OMEm3bLM24krJNnP3RnefDFQA081sUlyTlGyvJOLq9u1lZn8B7Hb3la01SzDstLdXb0sA1UAk5nMFsP002nR7XO5+qPmw1N2XArlmNriL42pLKrZVm1K5rcwsl2An+yt3fyJBk5Rss7biSvXfl7sfAP4IzIwbldK/sZbiStH2uhi4zszeJ+gm/piZ/TKuTadur96WAFYA48xstJnlAQuAJXFtlgC3hmfTZwAH3X1HquMys2FmZuH76QS/m71dHFdbUrGt2pSqbRUu8wHgbXf/QQvNun2bJRNXKraZmZWaWXH4vgC4EtgQ1ywV26vNuFKxvdz9a+5e4e6jCPYRv3f3v4pr1qnbK+f0w+153L3BzO4CXiC48uZBd19nZneG438KLCU4k74JOAp8uofEdTPwd2bWABwDFnh42r+rmNkjBFc7DDazauBbBCfEUratkoyr27dV6GLgU8BbYf8xwNeBETGxpWKbJRNXKrZZGfBzM8sm2IEucvdnUv3/mGRcqfobO0VXbi+VghARyVC9rQtIRESSpAQgIpKhlABERDKUEoCISIZSAhARyVBKACIiGUoJQEQkQ/1/bhXlN0ebz6cAAAAASUVORK5CYII=\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",
    "# YOUR CODE HERE!\n",
    "pd.DataFrame(results.forecast(steps=5)[0]).plot(title=\"ARMA Five Day Returns Forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "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",
    " No, it is not a good fit. Only one of the p-values is under .05.\n",
    " \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?\n",
    " It forecasts the the yen will increase.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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",
    "# YOUR CODE HERE!\n",
    "model = ARIMA(df.Settle.values, order = (5,1,1))\n",
    "\n",
    "# Fit the model\n",
    "# YOUR CODE HERE!\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>D.y</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>Wed, 18 Nov 2020</td> <th>  AIC                </th>  <td>83905.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>16:49:27</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>    0.3159</td> <td>    0.700</td> <td>    0.451</td> <td> 0.652</td> <td>   -1.056</td> <td>    1.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.y</th> <td>    0.2819</td> <td>    0.699</td> <td>    0.403</td> <td> 0.687</td> <td>   -1.089</td> <td>    1.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.y</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.y</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.y</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.y</th> <td>   -0.0012</td> <td>    0.018</td> <td>   -0.065</td> <td> 0.949</td> <td>   -0.036</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.y</th> <td>   -0.2969</td> <td>    0.699</td> <td>   -0.425</td> <td> 0.671</td> <td>   -1.668</td> <td>    1.074</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>           1.8914</td> <td>          -1.3789j</td> <td>           2.3407</td> <td>          -0.1003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           1.8914</td> <td>          +1.3789j</td> <td>           2.3407</td> <td>           0.1003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>          -2.2684</td> <td>          -3.0217j</td> <td>           3.7784</td> <td>          -0.3525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th> <td>          -2.2684</td> <td>          +3.0217j</td> <td>           3.7784</td> <td>           0.3525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th> <td>         -11.0153</td> <td>          -0.0000j</td> <td>          11.0153</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           3.3679</td> <td>          +0.0000j</td> <td>           3.3679</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.y   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:                Wed, 18 Nov 2020   AIC                          83905.238\n",
       "Time:                        16:49:27   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          0.3159      0.700      0.451      0.652      -1.056       1.688\n",
       "ar.L1.D.y      0.2819      0.699      0.403      0.687      -1.089       1.653\n",
       "ar.L2.D.y      0.0007      0.016      0.043      0.966      -0.030       0.032\n",
       "ar.L3.D.y     -0.0127      0.012     -1.032      0.302      -0.037       0.011\n",
       "ar.L4.D.y     -0.0137      0.015     -0.889      0.374      -0.044       0.016\n",
       "ar.L5.D.y     -0.0012      0.018     -0.065      0.949      -0.036       0.034\n",
       "ma.L1.D.y     -0.2969      0.699     -0.425      0.671      -1.668       1.074\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.8914           -1.3789j            2.3407           -0.1003\n",
       "AR.2            1.8914           +1.3789j            2.3407            0.1003\n",
       "AR.3           -2.2684           -3.0217j            3.7784           -0.3525\n",
       "AR.4           -2.2684           +3.0217j            3.7784            0.3525\n",
       "AR.5          -11.0153           -0.0000j           11.0153           -0.5000\n",
       "MA.1            3.3679           +0.0000j            3.3679            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Yen Five Day Returns Forecast'}>"
      ]
     },
     "execution_count": 94,
     "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 Price Forecast\n",
    "# YOUR CODE HERE!\n",
    "pd.DataFrame(results.forecast(steps=5)[0]).plot(title=\"Yen Five Day Returns Forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "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 an GARCH model\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)? All p-values are under .05 except for alpha2 which shows a pretty good fit.\n",
    "3. Plot the 5-day forecast of the volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arch as arch\n",
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "# YOUR CODE HERE!\n",
    "model = arch_model(returns, mean = \"Zero\", vol = \"GARCH\", p = 2, q = 1)\n",
    "# Fit the model\n",
    "# YOUR CODE HERE!\n",
    "results = model.fit(disp = \"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Zero Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Returns</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>  -7455.84</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   14919.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14947.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>7512</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Wed, Nov 18 2020</td>  <th>  Df Residuals:      </th>    <td>7508</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>15:34:59</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>4.3399e-03</td> <td>2.027e-03</td> <td>    2.141</td> <td>3.226e-02</td>  <td>[3.673e-04,8.312e-03]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th> <td>    0.0383</td> <td>1.288e-02</td> <td>    2.976</td> <td>2.924e-03</td>  <td>[1.308e-02,6.356e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th>   <td>0.0000</td>   <td>1.691e-02</td>   <td>0.000</td>   <td>    1.000</td> <td>[-3.314e-02,3.314e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beta[1]</th>  <td>    0.9533</td> <td>1.389e-02</td> <td>   68.610</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:                Returns   R-squared:                       0.000\n",
       "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:               -7455.84\n",
       "Distribution:                  Normal   AIC:                           14919.7\n",
       "Method:            Maximum Likelihood   BIC:                           14947.4\n",
       "                                        No. Observations:                 7512\n",
       "Date:                Wed, Nov 18 2020   Df Residuals:                     7508\n",
       "Time:                        15:34:59   Df Model:                            4\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      4.3399e-03  2.027e-03      2.141  3.226e-02  [3.673e-04,8.312e-03]\n",
       "alpha[1]       0.0383  1.288e-02      2.976  2.924e-03  [1.308e-02,6.356e-02]\n",
       "alpha[2]       0.0000  1.691e-02      0.000      1.000 [-3.314e-02,3.314e-02]\n",
       "beta[1]        0.9533  1.389e-02     68.610      0.000      [  0.926,  0.981]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "# YOUR CODE HERE!\n",
    "results3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-10-15'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the last day of the dataset\n",
    "last_day = df.index.max().strftime('%Y-%m-%d')\n",
    "last_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "# Start the forecast using the last_day calculated above\n",
    "# YOUR CODE HERE!\n",
    "forecasts = results.forecast(start = \"2019-10-15\", horizon = forecast_horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualize the forecast\n",
    "intermediate = np.sqrt(forecasts.variance.dropna() * 252)\n",
    "intermediate.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>h.4</th>\n",
       "      <td>7.557426</td>\n",
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      ],
      "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": 108,
     "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": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\laure\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1235: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the final forecast\n",
    "# YOUR CODE HERE!\n",
    "final.plot(figsize = (20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on your time series analysis, would you buy the yen now?\n",
    "\n",
    "No, I would not buy the yen yet because the ARMA model shows stationary values rather than an increase in value. By the highly positive AIC and BIC we can also see that that this is not a good time to buy.\n",
    "\n",
    "Is the risk of the yen expected to increase or decrease?\n",
    "The risk is going to increase.\n",
    "\n",
    "\n",
    "Based on the model evaluation, would you feel confident in using these models for trading?\n",
    "No, because the p-values showed that the data was not a good fit. I would not want to risk my money with that much of an increase in volatility. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "mimetype": "text/x-python",
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  "npconvert_exporter": "python",
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  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}