TimeSeriesErgasia - ex1-y1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Assignment\n",
    "\n",
    "* Student: **Perakis Georgios**\n",
    "* Code: **DS3616014**\n",
    "* Contact: **gperakis[at]aueb.gr**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data you will have to analyze are in the eclass in the **excel data-assignment.xls**\n",
    "file. The **dependent variables** for which you will construct the models you are asked\n",
    "for, are the **returns** of four investment vechicles **(Y1, Y2, Y3, Y4)** for the period **1/1991 - 12/2005**.\n",
    "\n",
    "The **independent variables** you will use in the models refer to **monthly** values/returns for the variables:\n",
    "* x1 = RUS-Rf\n",
    "* x2 = RUS (-1) -Rf (-1) lagged Russel index\n",
    "* x3 = MXUS-Rf\n",
    "* x4 = MEM- Rf\n",
    "* x5 = SMB\n",
    "* x6 = HML\n",
    "* x7 = MOM\n",
    "* x8 = SBGC-Rf\n",
    "* x9 = SBWG-Rf\n",
    "* x10 = LHY-Rf\n",
    "* x11 = DEFSPR\n",
    "* x12 = FRSI-Rf\n",
    "* x13 = GSCI-Rf\n",
    "* x14 = VIX\n",
    "\n",
    "for the period **1/1991 - 12/2005**.\n",
    "\n",
    "Analyze dependent variables based on data for the period 1/1991 - 12/2004 **[You will not use the data for the period 1/2005 - 12/2005]**:\n",
    "\n",
    "1. Construct an appropriate **time series** model (AR, MA, ARMA).\n",
    "2. Develop an appropriate **regression** model\n",
    "  * In case of **autocorrelation** problem of **regression residuals**, correct the autocorrelation problem (using time series AR, MA, ARMA models).\n",
    "  * In case of **heteroscedasticity** problem of **regression residuals**, correct the\n",
    "    heteroskedasticity problem (using time-varying ARCH, GARCH models).\n",
    "3. Write the models you have found at questions (1) - (2). Assess the **goodness of fit** of these models based on the **AIC and BIC** information criteria.\n",
    "4. Based on the estimated models of questions (1) - (2), **construct forecasts** of the analyzed series for the period **1/2005 - 12/2005**, and **evaluate the forecasts** you have found by using two evaluation criteria:\n",
    "  * the mean square prediction error\n",
    "  * the Hit ratio (indicates the percentage of predictions that correctly evaluate the sign of the actual value of the dependent variable.)\n",
    "  \n",
    "Each student will have to analyze **two only dependent variables**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loads the library urca, which contains the command ur.df() \n",
    "# and applies Unit-Root Testing based on Dickey-Fuller test \n",
    "library(urca)\n",
    "library(tseries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data into R can be carried out in various ways. Below, the command read.table is used:\n",
    "data_all = read.table('/home/george/Desktop/AUEB/Time Series Analysis/time_series_in_r/Data-Assignment-Feb-2018.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th><th scope=col>V12</th><th scope=col>V13</th><th scope=col>V14</th><th scope=col>V15</th><th scope=col>V16</th><th scope=col>V17</th><th scope=col>V18</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 0.02058</td><td> 0.02058</td><td> 0.00468</td><td>0.01468 </td><td> 0.04568</td><td> 0.01745</td><td> 0.02313</td><td> 0.07273</td><td> 0.0389 </td><td>-0.0181 </td><td>-0.0946 </td><td> 0.00575</td><td> 0.01937</td><td> 0.01868</td><td> 0.0001 </td><td> 0.00004</td><td>-0.10389</td><td>-0.0547 </td></tr>\n",
       "\t<tr><td> 0.03503</td><td> 0.02583</td><td> 0.03903</td><td>0.03623 </td><td> 0.06346</td><td> 0.04568</td><td> 0.09344</td><td> 0.13201</td><td> 0.0397 </td><td>-0.0056 </td><td>-0.1147 </td><td> 0.00144</td><td>-0.00486</td><td> 0.10639</td><td>-0.0014 </td><td>-0.01039</td><td> 0.00866</td><td> 0.0032 </td></tr>\n",
       "\t<tr><td> 0.04458</td><td> 0.02168</td><td> 0.02878</td><td>0.06348 </td><td> 0.02225</td><td> 0.06346</td><td>-0.06591</td><td> 0.03187</td><td> 0.0395 </td><td>-0.0124 </td><td>-0.0407 </td><td> 0.00253</td><td>-0.04193</td><td> 0.02621</td><td>-0.0024 </td><td> 0.03228</td><td> 0.03201</td><td>-0.0435 </td></tr>\n",
       "\t<tr><td> 0.00865</td><td> 0.01325</td><td> 0.02585</td><td>0.04855 </td><td>-0.00581</td><td> 0.02225</td><td> 0.00371</td><td>-0.00174</td><td> 0.0037 </td><td> 0.0170 </td><td>-0.0224 </td><td> 0.00790</td><td> 0.01053</td><td> 0.01619</td><td>-0.0008 </td><td> 0.01107</td><td> 0.01764</td><td> 0.0136 </td></tr>\n",
       "\t<tr><td> 0.01507</td><td> 0.00227</td><td> 0.02377</td><td>0.00887 </td><td> 0.03258</td><td>-0.00581</td><td> 0.00577</td><td> 0.06795</td><td>-0.0037 </td><td>-0.0052 </td><td> 0.0282 </td><td>-0.00023</td><td>-0.00601</td><td>-0.00780</td><td>-0.0011 </td><td> 0.00235</td><td>-0.00524</td><td>-0.0231 </td></tr>\n",
       "\t<tr><td>-0.00064</td><td>-0.00924</td><td>-0.00234</td><td>0.01246 </td><td>-0.05360</td><td> 0.03258</td><td>-0.07982</td><td>-0.04322</td><td> 0.0006 </td><td> 0.0102 </td><td> 0.0010 </td><td>-0.00499</td><td>-0.01526</td><td> 0.01826</td><td>-0.0011 </td><td> 0.01085</td><td>-0.02869</td><td> 0.0362 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       " V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14 & V15 & V16 & V17 & V18\\\\\n",
       "\\hline\n",
       "\t  0.02058 &  0.02058 &  0.00468 & 0.01468  &  0.04568 &  0.01745 &  0.02313 &  0.07273 &  0.0389  & -0.0181  & -0.0946  &  0.00575 &  0.01937 &  0.01868 &  0.0001  &  0.00004 & -0.10389 & -0.0547 \\\\\n",
       "\t  0.03503 &  0.02583 &  0.03903 & 0.03623  &  0.06346 &  0.04568 &  0.09344 &  0.13201 &  0.0397  & -0.0056  & -0.1147  &  0.00144 & -0.00486 &  0.10639 & -0.0014  & -0.01039 &  0.00866 &  0.0032 \\\\\n",
       "\t  0.04458 &  0.02168 &  0.02878 & 0.06348  &  0.02225 &  0.06346 & -0.06591 &  0.03187 &  0.0395  & -0.0124  & -0.0407  &  0.00253 & -0.04193 &  0.02621 & -0.0024  &  0.03228 &  0.03201 & -0.0435 \\\\\n",
       "\t  0.00865 &  0.01325 &  0.02585 & 0.04855  & -0.00581 &  0.02225 &  0.00371 & -0.00174 &  0.0037  &  0.0170  & -0.0224  &  0.00790 &  0.01053 &  0.01619 & -0.0008  &  0.01107 &  0.01764 &  0.0136 \\\\\n",
       "\t  0.01507 &  0.00227 &  0.02377 & 0.00887  &  0.03258 & -0.00581 &  0.00577 &  0.06795 & -0.0037  & -0.0052  &  0.0282  & -0.00023 & -0.00601 & -0.00780 & -0.0011  &  0.00235 & -0.00524 & -0.0231 \\\\\n",
       "\t -0.00064 & -0.00924 & -0.00234 & 0.01246  & -0.05360 &  0.03258 & -0.07982 & -0.04322 &  0.0006  &  0.0102  &  0.0010  & -0.00499 & -0.01526 &  0.01826 & -0.0011  &  0.01085 & -0.02869 &  0.0362 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 | V12 | V13 | V14 | V15 | V16 | V17 | V18 | \n",
       "|---|---|---|---|---|---|\n",
       "|  0.02058 |  0.02058 |  0.00468 | 0.01468  |  0.04568 |  0.01745 |  0.02313 |  0.07273 |  0.0389  | -0.0181  | -0.0946  |  0.00575 |  0.01937 |  0.01868 |  0.0001  |  0.00004 | -0.10389 | -0.0547  | \n",
       "|  0.03503 |  0.02583 |  0.03903 | 0.03623  |  0.06346 |  0.04568 |  0.09344 |  0.13201 |  0.0397  | -0.0056  | -0.1147  |  0.00144 | -0.00486 |  0.10639 | -0.0014  | -0.01039 |  0.00866 |  0.0032  | \n",
       "|  0.04458 |  0.02168 |  0.02878 | 0.06348  |  0.02225 |  0.06346 | -0.06591 |  0.03187 |  0.0395  | -0.0124  | -0.0407  |  0.00253 | -0.04193 |  0.02621 | -0.0024  |  0.03228 |  0.03201 | -0.0435  | \n",
       "|  0.00865 |  0.01325 |  0.02585 | 0.04855  | -0.00581 |  0.02225 |  0.00371 | -0.00174 |  0.0037  |  0.0170  | -0.0224  |  0.00790 |  0.01053 |  0.01619 | -0.0008  |  0.01107 |  0.01764 |  0.0136  | \n",
       "|  0.01507 |  0.00227 |  0.02377 | 0.00887  |  0.03258 | -0.00581 |  0.00577 |  0.06795 | -0.0037  | -0.0052  |  0.0282  | -0.00023 | -0.00601 | -0.00780 | -0.0011  |  0.00235 | -0.00524 | -0.0231  | \n",
       "| -0.00064 | -0.00924 | -0.00234 | 0.01246  | -0.05360 |  0.03258 | -0.07982 | -0.04322 |  0.0006  |  0.0102  |  0.0010  | -0.00499 | -0.01526 |  0.01826 | -0.0011  |  0.01085 | -0.02869 |  0.0362  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  V1       V2       V3       V4      V5       V6       V7       V8      \n",
       "1  0.02058  0.02058  0.00468 0.01468  0.04568  0.01745  0.02313  0.07273\n",
       "2  0.03503  0.02583  0.03903 0.03623  0.06346  0.04568  0.09344  0.13201\n",
       "3  0.04458  0.02168  0.02878 0.06348  0.02225  0.06346 -0.06591  0.03187\n",
       "4  0.00865  0.01325  0.02585 0.04855 -0.00581  0.02225  0.00371 -0.00174\n",
       "5  0.01507  0.00227  0.02377 0.00887  0.03258 -0.00581  0.00577  0.06795\n",
       "6 -0.00064 -0.00924 -0.00234 0.01246 -0.05360  0.03258 -0.07982 -0.04322\n",
       "  V9      V10     V11     V12      V13      V14      V15     V16      V17     \n",
       "1  0.0389 -0.0181 -0.0946  0.00575  0.01937  0.01868  0.0001  0.00004 -0.10389\n",
       "2  0.0397 -0.0056 -0.1147  0.00144 -0.00486  0.10639 -0.0014 -0.01039  0.00866\n",
       "3  0.0395 -0.0124 -0.0407  0.00253 -0.04193  0.02621 -0.0024  0.03228  0.03201\n",
       "4  0.0037  0.0170 -0.0224  0.00790  0.01053  0.01619 -0.0008  0.01107  0.01764\n",
       "5 -0.0037 -0.0052  0.0282 -0.00023 -0.00601 -0.00780 -0.0011  0.00235 -0.00524\n",
       "6  0.0006  0.0102  0.0010 -0.00499 -0.01526  0.01826 -0.0011  0.01085 -0.02869\n",
       "  V18    \n",
       "1 -0.0547\n",
       "2  0.0032\n",
       "3 -0.0435\n",
       "4  0.0136\n",
       "5 -0.0231\n",
       "6  0.0362"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(data_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y1 <- data_all$V1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Let create a time series object using the function “ts” from a vector - single time-series or a matrix - multivariate time-series. The data consist of the Y1 monthly returns from 1/1991 - 12/2004, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "j1 = ts(y1, frequency=12, start = c(1991, 1), end = c(2004, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.02058</li>\n",
       "\t<li>0.03503</li>\n",
       "\t<li>0.04458</li>\n",
       "\t<li>0.00865</li>\n",
       "\t<li>0.01507</li>\n",
       "\t<li>-0.00064</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.02058\n",
       "\\item 0.03503\n",
       "\\item 0.04458\n",
       "\\item 0.00865\n",
       "\\item 0.01507\n",
       "\\item -0.00064\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.02058\n",
       "2. 0.03503\n",
       "3. 0.04458\n",
       "4. 0.00865\n",
       "5. 0.01507\n",
       "6. -0.00064\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  0.02058  0.03503  0.04458  0.00865  0.01507 -0.00064"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Jan      Feb      Mar      Apr      May      Jun      Jul      Aug\n",
       "1991  0.02058  0.03503  0.04458  0.00865  0.01507 -0.00064  0.02146  0.01603\n",
       "1992  0.03512  0.01805  0.00244 -0.00016  0.01523 -0.00714  0.01809 -0.00539\n",
       "1993  0.02073  0.01090  0.02813  0.01244  0.02339  0.02362  0.01831  0.02673\n",
       "1994  0.02248 -0.00837 -0.01808 -0.00920  0.00323 -0.00083  0.00986  0.02070\n",
       "1995 -0.00720  0.00815  0.01160  0.01610  0.01214  0.02008  0.02735  0.01746\n",
       "1996  0.02469  0.00813  0.01031  0.03531  0.02619 -0.00250 -0.02523  0.01839\n",
       "1997  0.02741  0.00595 -0.02085 -0.00547  0.03968  0.02269  0.03433 -0.00096\n",
       "1998 -0.01142  0.02826  0.02570  0.00545 -0.02498 -0.00555 -0.01213 -0.09108\n",
       "1999  0.01868 -0.01712  0.02765  0.04121  0.00332  0.03232  0.00124 -0.00425\n",
       "2000  0.00167  0.05677  0.00440 -0.03334 -0.02430  0.03191 -0.01117  0.03285\n",
       "2001  0.02974 -0.02613 -0.01948  0.01626  0.00894 -0.00012 -0.01125 -0.00691\n",
       "2002  0.00303 -0.00848  0.01761  0.00134 -0.00104 -0.02079 -0.03000  0.00393\n",
       "2003  0.00554 -0.00078  0.00049  0.02547  0.03490  0.01280  0.01221  0.01750\n",
       "2004  0.01905  0.01113  0.00433 -0.01560 -0.00398  0.00642 -0.01078 -0.00013\n",
       "          Sep      Oct      Nov      Dec\n",
       "1991  0.01882  0.01418 -0.00103  0.03621\n",
       "1992  0.01712  0.01858  0.02522  0.02268\n",
       "1993  0.01332  0.02923 -0.00112  0.03659\n",
       "1994  0.00491 -0.00460 -0.01748 -0.00555\n",
       "1995  0.01510 -0.01109  0.01429  0.01657\n",
       "1996  0.01650  0.00551  0.01653  0.00901\n",
       "1997  0.03294 -0.01963 -0.01366  0.00434\n",
       "1998  0.00327  0.00859  0.03332  0.02418\n",
       "1999 -0.00242  0.01175  0.04617  0.07215\n",
       "2000 -0.01758 -0.02321 -0.04008  0.01579\n",
       "2001 -0.03028  0.01839  0.01922  0.01566\n",
       "2002 -0.01673  0.00468  0.02018 -0.00309\n",
       "2003  0.01083  0.02373  0.00984  0.01794\n",
       "2004  0.01510  0.00684  0.02658  0.01468"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "j1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are talking about **returns** we **don't** need to take to logs and the differences of the logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The time series plot and the histogram for the Y1 data is given by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sknsWULN4tMmoTwcHz6qfla1lSSk1FYiKFDkZxssvJ8Tg569uQe1FSxKy2FTIaW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7BtgBqDTrW4olJyfv27fvjTfe0Ol0hoM6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\nACpBsAMAAFCJ/wdSxar3V4c73AAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Histogram of Y1”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up the graphics\n",
    "par(mfrow=c(2, 1))      \n",
    "\n",
    "plot(j1, type=\"l\", col='red',\n",
    "     lwd=1, main=\"Time Series plot of Y1\", \n",
    "     ylab=\"Monthly returns\")\n",
    "\n",
    "hist(j1, nclass=20, main=\"Histogram of Y1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The autocorrelation and partial autocorrelation plots are useful to examine if there is dependence between lagged values of the analyzed series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f369SOEiMXiX375Zf78+YWFhWwHBAAd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\n29lEAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “PACF of Y1”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Autocorrelation and partial autocorrelation plots\n",
    "par(mfrow=c(2, 2)) # set up the graphics  \n",
    "\n",
    "acf(j1, 48, main=\"ACF of Y1\")        # autocorrelation function plot \n",
    "pacf(j1, 48, main=\"PACF of Y1\")    # partial autocorrelation function \n",
    "\n",
    "acf(ts(j1, freq=1), 48, main=\"ACF of Y1\")        # autocorrelation function plot \n",
    "pacf(ts(j1, freq=1), 48, main=\"PACF of Y1\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must note that in the autocorrelation plots presented above, the **dashed lines** are the approximate **two standard error confidence bounds** computed by ± 1,96* (1 / T), where T is the number observations.\n",
    "\n",
    "If the autocorrelation is within these bounds, it is not significantly different from zero at (approximately)\n",
    "5% level of significance.\n",
    "\n",
    "So we conclude  that:\n",
    "\n",
    "* From **ACF plot** we observe that we have **autocorrelation for lag=1**. We must check an autoregressive model for lag=1. **(AR(1))**\n",
    "* From **PACF plot** we observe that we also have **partial autocorrelation for lag=1 and lag=15**. We must check a moving average model for lag=1 and lag=15. **(MA(1) and MA(15))**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UNIT ROOT TESTING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we can fit an **autoregressive time series model** to the Y1 data, by selecting the complexity of the\n",
    "model **based on AIC**. Then, we perform an **augmented Dickey-Fuller test** of unit root, based on a model\n",
    "with constant and trend "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "ar(x = j1)\n",
       "\n",
       "Coefficients:\n",
       "     1  \n",
       "0.2481  \n",
       "\n",
       "Order selected 1  sigma^2 estimated as  0.0003725"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Order: 1'"
      ],
      "text/latex": [
       "'Order: 1'"
      ],
      "text/markdown": [
       "'Order: 1'"
      ],
      "text/plain": [
       "[1] \"Order: 1\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unit root testing for the Y1 series\n",
    "# Fits an autoregressive model, and selects the best AR order based on AIC\n",
    "m1 = ar(j1)\n",
    "m1\n",
    "paste('Order:', m1$order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root / Cointegration Test # \n",
       "############################################################### \n",
       "\n",
       "The value of the test statistic is: -8.7817 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression none \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 - 1)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.086629 -0.006201  0.007275  0.018381  0.056157 \n",
       "\n",
       "Coefficients:\n",
       "        Estimate Std. Error t value Pr(>|t|)    \n",
       "z.lag.1 -0.63308    0.07209  -8.782 1.91e-15 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.02017 on 166 degrees of freedom\n",
       "Multiple R-squared:  0.3172,\tAdjusted R-squared:  0.3131 \n",
       "F-statistic: 77.12 on 1 and 166 DF,  p-value: 1.911e-15\n",
       "\n",
       "\n",
       "Value of test-statistic is: -8.7817 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau1 -2.58 -1.95 -1.62\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m11 <- ur.df(j1, type='none', lags=m1$order-1)\n",
    "m11\n",
    "summary(m11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root / Cointegration Test # \n",
       "############################################################### \n",
       "\n",
       "The value of the test statistic is: -9.9769 49.7692 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.094517 -0.011083  0.001633  0.012123  0.054239 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.006449   0.001631   3.953 0.000114 ***\n",
       "z.lag.1     -0.751732   0.075348  -9.977  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.01934 on 165 degrees of freedom\n",
       "Multiple R-squared:  0.3763,\tAdjusted R-squared:  0.3725 \n",
       "F-statistic: 99.54 on 1 and 165 DF,  p-value: < 2.2e-16\n",
       "\n",
       "\n",
       "Value of test-statistic is: -9.9769 49.7692 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.46 -2.88 -2.57\n",
       "phi1  6.52  4.63  3.81\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unit root testing for the Y1 series\n",
    "# Fits an autoregressive model, and selects the best AR order based on AIC\n",
    "m12 <- ur.df(j1, type=\"drift\", lags=m1$order-1)\n",
    "m12\n",
    "summary(m12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Critical Value (CV) for 5% is -2.88\n",
    "* The value of the test statistic for lag=1 with drift is: -7.7644\n",
    "* Both Intercept and z.lag.1 are statistically significant\n",
    "\n",
    "Obviously, the null hypothesis of non-stationarity for the Y1 series is rejected. Therefore the returns of Y1 seem to be a stationary process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root / Cointegration Test # \n",
       "############################################################### \n",
       "\n",
       "The value of the test statistic is: -10.1882 34.6052 51.9076 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression trend \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + tt)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.094640 -0.012259  0.001255  0.011391  0.056413 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  1.126e-02  3.198e-03   3.520 0.000558 ***\n",
       "z.lag.1     -7.761e-01  7.618e-02 -10.188  < 2e-16 ***\n",
       "tt          -5.475e-05  3.138e-05  -1.745 0.082899 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.01922 on 164 degrees of freedom\n",
       "Multiple R-squared:  0.3876,\tAdjusted R-squared:  0.3802 \n",
       "F-statistic: 51.91 on 2 and 164 DF,  p-value: < 2.2e-16\n",
       "\n",
       "\n",
       "Value of test-statistic is: -10.1882 34.6052 51.9076 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau3 -3.99 -3.43 -3.13\n",
       "phi2  6.22  4.75  4.07\n",
       "phi3  8.43  6.49  5.47\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m13 <- ur.df(j1, type=\"trend\", lags=m1$order-1)\n",
    "m13\n",
    "summary(m13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus on the stationary time series, i.e. the monthly returns.\n",
    "We can:\n",
    "* **test for normality of Y1**\n",
    "* **create the plot of the histogram together with a density plot**\n",
    "* **also create the normal QQplot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tShapiro-Wilk normality test\n",
       "\n",
       "data:  y1\n",
       "W = 0.96158, p-value = 7.636e-05\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8WaEFKo+HOU7okqnwZ5EtKlhOf4jyks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",
      "text/plain": [
       "Plot with title “Normal QQplot of Y1”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shapiro test of normality\n",
    "shapiro.test(y1)\n",
    "\n",
    "par(mfrow=c(2,1))\n",
    "hist(y1, prob=TRUE, 20) # histogram\n",
    "lines(density(y1))\n",
    "# smooth it - ?density for details\n",
    "qqnorm(y1, main=\"Normal QQplot of Y1\") # normal Q-Q plot\n",
    "qqline(y1) # add a line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusions:\n",
    "* From Shapiro Wilk test we **reject** the Null hypothesis for Normality.'\n",
    "* From the Histogram we see a small deviance from Normality\n",
    "* From the QQplot we see several points off the qqline.\n",
    "\n",
    "So we conclude that we **don't have Normality**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation of ARMA models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We firstly check the moving average of lag=1 model (MA(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = y1, order = c(0, 0, 1))\n",
       "\n",
       "Coefficients:\n",
       "         ma1  intercept\n",
       "      0.2151     0.0084\n",
       "s.e.  0.0669     0.0017\n",
       "\n",
       "sigma^2 estimated as 0.0003589:  log likelihood = 458.5,  aic = -911"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ma1fit=arima(y1, order=c(0,0,1))\n",
    "ma1fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The estimated model (MA(1) can be written in the form:**\n",
    "\n",
    "$$y_{t} - 0.0084 = 0.2151* e_{t-1} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} = 0.0084 + 0.2151* e_{t-1} + e_{t}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = y1, order = c(0, 0, 15), fixed = c(NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
       "    0, 0, 0, 0, NA, NA))\n",
       "\n",
       "Coefficients:\n",
       "         ma1  ma2  ma3  ma4  ma5  ma6  ma7  ma8  ma9  ma10  ma11  ma12  ma13\n",
       "      0.2079    0    0    0    0    0    0    0    0     0     0     0     0\n",
       "s.e.  0.0662    0    0    0    0    0    0    0    0     0     0     0     0\n",
       "      ma14     ma15  intercept\n",
       "         0  -0.1185     0.0083\n",
       "s.e.     0   0.0715     0.0015\n",
       "\n",
       "sigma^2 estimated as 0.0003532:  log likelihood = 459.83,  aic = -911.66"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ma15restricted <- arima(y1,\n",
    "                        order=c(0, 0, 15),\n",
    "                        fixed=c(NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA,NA))\n",
    "ma15restricted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The estimated model (Restricted MA(15) can be written in the form:**\n",
    "\n",
    "$$y_{t} - 0.0083 = 0.2079* e_{t-1} -0.1185 * e_{t-15} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} = 0.0083 + 0.2079* e_{t-1} -0.1185* e_{t-15} + e_{t}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion on MA models:\n",
    "* Both models MA(1) and restricted MA(15) have almost the **same AIC score** and the **same variance**.\n",
    "* Thus we wil keep the MA(1) for now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we will check the autoregressive of lag=1 model (AR(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = y1, order = c(1, 0, 0))\n",
       "\n",
       "Coefficients:\n",
       "         ar1  intercept\n",
       "      0.2377     0.0084\n",
       "s.e.  0.0722     0.0018\n",
       "\n",
       "sigma^2 estimated as 0.0003569:  log likelihood = 458.99,  aic = -911.98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar1fit <- arima(y1, order=c(1,0,0))\n",
    "ar1fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The estimated model (AR(1) can be written in the form:\n",
    "\n",
    "$$y_{t} = μ*(1-φ_{1}) + φ_{1} * y_{t-1} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} = 0.0084*(1- 0.2377) + 0.2377 * y_{t-1} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} = 0.00640332 + 0.2377 * y_{t-1} + e_{t}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets now fit an ARMA(1,1) model. The command is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = y1, order = c(1, 0, 1))\n",
       "\n",
       "Coefficients:\n",
       "         ar1      ma1  intercept\n",
       "      0.2799  -0.0445     0.0084\n",
       "s.e.  0.2543   0.2612     0.0019\n",
       "\n",
       "sigma^2 estimated as 0.0003568:  log likelihood = 459.01,  aic = -910.01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arma11fit <- arima(y1, order=c(1,0,1))\n",
    "arma11fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The estimated ARMA(1, 1) model can be written in the form:\n",
    "\n",
    "\n",
    "$$y_{t} - μ = φ_{1}*(y_{t-1} -μ) + θ_{1} * e_{t-1} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} =μ +  φ_{1}*(y_{t-1} -μ) + θ_{1} * e_{t-1} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} = 0.0084 +  0.2799*(y_{t-1} -0.0084) - 0.0445 * e_{t-1} + e_{t}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "arima(x = y1, order = c(1, 0, 15), fixed = c(NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, \n",
       "    0, 0, 0, 0, 0, NA, NA))\n",
       "\n",
       "Coefficients:\n",
       "         ar1      ma1  ma2  ma3  ma4  ma5  ma6  ma7  ma8  ma9  ma10  ma11  ma12\n",
       "      0.2918  -0.0573    0    0    0    0    0    0    0    0     0     0     0\n",
       "s.e.  0.2144   0.2220    0    0    0    0    0    0    0    0     0     0     0\n",
       "      ma13  ma14     ma15  intercept\n",
       "         0     0  -0.1311     0.0083\n",
       "s.e.     0     0   0.0720     0.0016\n",
       "\n",
       "sigma^2 estimated as 0.00035:  log likelihood = 460.62,  aic = -911.24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arma1_15fit_restricted <- arima(y1, \n",
    "                                order=c(1, 0, 15),\n",
    "                                fixed=c(NA,  NA,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,NA, NA))\n",
    "\n",
    "arma1_15fit_restricted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The estimated ARMA(1, 15) restricted model can be written in the form:\n",
    "\n",
    "\n",
    "$$y_{t} - μ = φ_{1}*(y_{t-1} -μ) + θ_{1} * e_{t-1} + θ_{15} * e_{t-15} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} =μ +  φ_{1}*(y_{t-1} -μ) + θ_{1} * e_{t-1} + θ_{15} * e_{t-15} + e_{t}$$\n",
    "\n",
    "**or** \n",
    "\n",
    "$$y_{t} =0.0083 +  0.2918*(y_{t-1} - 0.0083) - 0.0573 * e_{t-1} -0.1311 * e_{t-15} + e_{t}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarizing we have the following:\n",
    "\n",
    "* MA(1):\n",
    "  * Variance: **0.0003589**\n",
    "  * AIC: **-911**\n",
    "* MA(15) restricted:\n",
    "  * Variance: **0.0003532**\n",
    "  * AIC: **-911.66**\n",
    "* AR(1): \n",
    "  * Variance: **0.0003569**\n",
    "  * AIC: **-911.98**\n",
    "* ARMA(1, 1)\n",
    "  * Variance: **0.0003568**\n",
    "  * AIC: **-911.98**\n",
    "* ARMA(1, 15) restricted:\n",
    "  * Variance: **0.00035**\n",
    "  * AIC: **-911.24**\n",
    "  \n",
    "The model with the lowest variance and lower AIC score is the best. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagnostic plots\n",
    "\n",
    "Now we will provide some **diagnostic plots** for the **residuals** of the above models.\n",
    "Based on the residuals of the restricted ARMA(1,1) model, we will present the:\n",
    "* autocorrelation **(acf)** plots and the partial autocorrelation **(pacf)** plots of the **estimated residuals**\n",
    "* the autocorrelation plots and the partial autocorrelation plots of the **squared** residuals (check **heteroskedasticity**)\n",
    "* normality plots (examine the assumption of **normality of residuals**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AsLC2dnZ6FQKJFIsrKy3NzcEhMTuU4N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\n6Oi33nrL0FV0JTw8nF7Krtt6662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      "text/plain": [
       "Plot with title “Normal QQplot of residuals”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arma11residuals <- arma11fit$residuals\n",
    "residuals <- ts(arma11residuals, frequency=12, start = c(1991, 1), end = c(2004, 12))\n",
    "\n",
    "par(mfrow=c(3,2))\n",
    "\n",
    "# set up the graphics\n",
    "acf(ts(residuals,freq=1), 48, main=\"ACF of residuals\")\n",
    "pacf(ts(residuals,freq=1), 48, main=\"PACF of residuals\")\n",
    "acf(ts(residuals^2,freq=1), 48, main=\"ACF of squared residuals\")\n",
    "pacf(ts(residuals^2,freq=1), 48, main=\"PACF of squared residuals\")\n",
    "qqnorm(residuals,main=\"Normal QQplot of residuals\")\n",
    "qqline(residuals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the residual plots presented above:\n",
    "\n",
    "* From the ACF and PACF of the residuals, it seems that the assumptions with respect to them are satisfied\n",
    "* From the PACF fo the squared residuals it seems that there is an **heteroskedastisity** issue \n",
    "* From the Normal  QQPlot of residuals only 2-3 points are of the line. So we assume normality.\n",
    "\n",
    "Due to the issue of heteroskedasticity, a **GARCH model** should be used with the aforementionded model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}