ARMA-2000-2006 12M.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/friday/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('12monthsnew data.xls',sheetname='data to use',index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_forecast = df.loc['20060101':'20061231',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.loc['20000105':'20051231',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "252"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('Results/in-sample.xls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GbqiqTUmuaN5+HfBu4CTgD5MA7K+q1d0rW5IkSeqstuYYV9V6YP2EtutaLr8NeFtnS5Mk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\n/+qOAACAqgaFcFB6XGDlawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe752438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(df['Return'].dropna(),lags=22,ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(df['Return'].dropna(),lags=22,ax=ax2)\n",
    "fig.savefig('Results/acf22-0005.png',dpi=2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_max=7\n",
    "q_max=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_list = []\n",
    "q_list = []\n",
    "AIC_list = []\n",
    "BIC_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minAIC = 0\n",
    "minBIC = 0\n",
    "pminAIC = 0\n",
    "pminBIC = 0\n",
    "qminAIC = 0\n",
    "qminBIC = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:473: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#warnings.filterwarnings('ignore')\n",
    "for p in range (0,p_max):\n",
    "    for q in range (0,q_max):\n",
    "        try:\n",
    "            model = sm.tsa.ARMA(df['Return'],order=(p,q))\n",
    "            results = model.fit(maxiter=1000)\n",
    "            p_list.append(p)\n",
    "            q_list.append(q)\n",
    "            AIC_list.append(results.aic)\n",
    "            BIC_list.append(results.bic)\n",
    "            if minAIC>results.aic:\n",
    "                minAIC = results.aic\n",
    "                pminAIC = p\n",
    "                qminAIC = q\n",
    "            if minBIC>results.bic:\n",
    "                minBIC = results.bic\n",
    "                pminBIC = p\n",
    "                qminBIC = q\n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = {'p':p_list,'q':q_list,'AIC':AIC_list,'BIC':BIC_list}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = pd.DataFrame(data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models['AIC'] = models['AIC']/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models['BIC'] = models['BIC']/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models.to_excel('Results/models0005.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'According to AIC, the minimum AIC value is -5.016747150772372 at p=4 and q=4.\\nAccording to BIC, the minimum BIC value is -5.0042082556 at p=0 and q=0.\\n'"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Str_conclusion = 'According to AIC, the minimum AIC value is '+str(minAIC/len(df))+' at p='+str(pminAIC)+' and q='+str(qminAIC)+'.\\n'+'According to BIC, the minimum BIC value is '+str(minBIC/len(df))+' at p='+str(pminBIC)+' and q='+str(qminBIC)+'.\\n'\n",
    "with open(\"Results/ARMA_chosen_model(00-05).txt\", \"w\") as text_file:\n",
    "        print(Str_conclusion, file=text_file)\n",
    "Str_conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'                              ARMA Model Results                              \\n==============================================================================\\nDep. Variable:                 Return   No. Observations:                 1767\\nModel:                     ARMA(4, 4)   Log Likelihood                4567.012\\nMethod:                       css-mle   S.D. of innovations              0.018\\nDate:                Wed, 27 Dec 2017   AIC                          -9114.025\\nTime:                        17:05:25   BIC                          -9059.254\\nSample:                    01-05-2000   HQIC                         -9093.788\\n                         - 12-29-2006                                         \\n================================================================================\\n                   coef    std err          z      P>|z|      [0.025      0.975]\\n--------------------------------------------------------------------------------\\nconst           -0.0001      0.000     -0.274      0.784      -0.001       0.001\\nar.L1.Return    -0.0598      0.014     -4.332      0.000      -0.087      -0.033\\nar.L2.Return     0.6470      0.013     47.975      0.000       0.621       0.673\\nar.L3.Return    -0.1252      0.015     -8.096      0.000      -0.156      -0.095\\nar.L4.Return    -0.9440      0.015    -62.481      0.000      -0.974      -0.914\\nma.L1.Return     0.0836      0.009      9.589      0.000       0.066       0.101\\nma.L2.Return    -0.6757      0.008    -80.151      0.000      -0.692      -0.659\\nma.L3.Return     0.1112      0.010     11.078      0.000       0.092       0.131\\nma.L4.Return     0.9826      0.010    101.611      0.000       0.964       1.002\\n                                    Roots                                    \\n=============================================================================\\n                 Real           Imaginary           Modulus         Frequency\\n-----------------------------------------------------------------------------\\nAR.1            0.7958           -0.6116j            1.0037           -0.1043\\nAR.2            0.7958           +0.6116j            1.0037            0.1043\\nAR.3           -0.8621           -0.5552j            1.0254           -0.4089\\nAR.4           -0.8621           +0.5552j            1.0254            0.4089\\nMA.1            0.7946           -0.6071j            1.0000           -0.1038\\nMA.2            0.7946           +0.6071j            1.0000            0.1038\\nMA.3           -0.8512           -0.5415j            1.0088           -0.4098\\nMA.4           -0.8512           +0.5415j            1.0088            0.4098\\n-----------------------------------------------------------------------------'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.tsa.ARMA(df['Return'],order=(pminAIC,qminAIC))\n",
    "res = model.fit(maxiter=1000)\n",
    "\n",
    "Str_conclusion = str(res.summary())\n",
    "with open(\"Results/ARMA_summary.txt\", \"w\") as text_file:\n",
    "        print(Str_conclusion, file=text_file)\n",
    "Str_conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.stats.diagnostic import acorr_ljungbox as LB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = [pd.Series(range(1,11)),pd.Series(LB(res.resid,lags=10)[0])]\n",
    "Lbt = pd.concat(result,axis=1)\n",
    "Lbt.columns=['lags','Test Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Lbt.to_excel('Results/Ljung-Box Test.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.stats.stattools import jarque_bera as JB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = JB(res.resid)\n",
    "JBResult = 'Jarque-Bera: '+ str(result[0])+'\\n p-value:' + str(result[1])+'\\n Skewness:' + str(result[2])+'\\n Kurtosis:' + str(result[3])\n",
    "with open(\"Results/Jarque-Bera.txt\", \"w\") as text_file:\n",
    "        print(JBResult, file=text_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use p=4, q=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first_day = 0\n",
    "forecast_day = len(df)\n",
    "forecast = []\n",
    "df = df.append(df_forecast)\n",
    "last_day = len(df_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,last_day):\n",
    "    in_sample = df.iloc[first_day:forecast_day-1]\n",
    "    model = sm.tsa.ARMA(in_sample['Return'],order=(pminBIC,qminBIC))\n",
    "    res = model.fit(maxiter=1000)\n",
    "    fore = res.forecast(1)[0][0]\n",
    "    forecast.append(fore)\n",
    "    first_day = first_day+1\n",
    "    forecast_day = forecast_day+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "forecast_data = pd.Series(data=forecast,index=df_forecast.index)\n",
    "df_forecast = pd.concat([df_forecast,forecast_data],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_forecast.columns=['Rate','Return','Forecast log']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_forecast['Forecast ARMA'] = np.exp(df_forecast['Forecast log'])*df_forecast['Rate'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_forecast['Forecast ARMA'].iloc[0] = np.exp(df_forecast['Forecast log'].iloc[0])*df['Rate'].iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MAE_ARMA = np.average(np.abs(df_forecast['Rate']-df_forecast['Forecast ARMA']))\n",
    "Sum_Square = (df_forecast['Rate']-df_forecast['Forecast ARMA'])**2\n",
    "RMSE_ARMA = np.sqrt(np.average(Sum_Square))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_forecast['Naive'] = df_forecast['Rate'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_forecast['Naive'].iloc[0] = df['Rate'].iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MAE_naive = np.average(np.abs(df_forecast['Rate']-df_forecast['Naive']))\n",
    "Sum_Square_naive = (df_forecast['Rate']-df_forecast['Naive'])**2\n",
    "RMSE_naive = np.sqrt(np.average(Sum_Square_naive))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe725a20>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+u5O0gWAQ/jYBkkbB9949bP6awlpu/OcmltwxgykZ8bBtEXxwB9y5HlLGdJpbXOfk3CdW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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf2fcba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_forecast[['Rate','Forecast ARMA']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>ARMA-GARCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Results\\\\Forecast.xls'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-160-7b7223fedd08>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_armagarch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Results\\Forecast.xls'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel.py\u001b[0m in \u001b[0;36mread_excel\u001b[1;34m(io, sheetname, header, skiprows, skip_footer, index_col, names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, dtype, true_values, false_values, engine, squeeze, **kwds)\u001b[0m\n\u001b[0;32m    198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m         \u001b[0mio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m     return io._parse_excel(\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, io, **kwds)\u001b[0m\n\u001b[0;32m    255\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 257\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    258\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m             raise ValueError('Must explicitly set engine if not passing in'\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\xlrd\\__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[0;32m    393\u001b[0m         \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_contents\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 395\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    396\u001b[0m             \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpeek\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"PK\\x03\\x04\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# a ZIP file\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Results\\\\Forecast.xls'"
     ]
    }
   ],
   "source": [
    "df_armagarch = pd.read_excel('Results\\Forecast.xls',index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_armagarch['Forecast_ARMAGARCH'] = np.exp(df_armagarch['Forecast'])*df_armagarch['USD12MD156N'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_armagarch['Forecast_ARMAGARCH'].iloc[0] = np.exp(df_armagarch['Forecast'].iloc[0])*df['USD12MD156N'].iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_forecast = df_forecast.merge(pd.DataFrame(df_armagarch['Forecast_ARMAGARCH']),how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_forecast.to_excel('Results/Prediction 2016 (model 2000-2015).xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MAE_ag = np.average(np.abs(df_forecast['Rate']-df_forecast['Forecast_ARMAGARCH']))\n",
    "Sum_Square_ag = (df_forecast['Rate']-df_forecast['Forecast_ARMAGARCH'])**2\n",
    "RMSE_ag = np.sqrt(np.average(Sum_Square_naive))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe34bef0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v/OvPedj2Y7jpGYJjl7D4zpeIJZLs9tyMNR3ndzN+y8377oBZV8Enf37Y8//tuW08ueZ9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FmPA5eUh9AmSKxUwZtdTpPOFRiV50UnHRwqfveZ91yD3+NEkqSVUuOGym3bg7MNG6ZAL5Ex0s/Y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\ngTFBEARBEARBAAD+f8KpG3dd7yQBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe35cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_forecast[['Rate','Forecast_ARMAGARCH']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Forecast Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title = ['Naive','ARMA','ARMA-Garch']\n",
    "obs = [str(len(df_forecast)),str(len(df_forecast)),str(len(df_forecast))]\n",
    "no1 = [RMSE_naive,RMSE_ARMA,RMSE_ag]\n",
    "no2 = [MAE_naive,MAE_ARMA,MAE_ag]\n",
    "result = pd.DataFrame(np.transpose([obs,no1,no2]),index=title,columns=['Number of Observations','RMSE','MAE'])\n",
    "result.to_excel('Results/Forecast_Result.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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