py4fi_L4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare EURO STOXX 50 to VSTOXX\n",
    "\n",
    "EURO STOXX 50:\n",
    "Historical daily closing values of the EURO STOXX 50 index, composed of European\n",
    "blue-chip stocks\n",
    "\n",
    "VSTOXX:\n",
    "Historical daily closing data for the VSTOXX volatility index, calculated on the\n",
    "basis of *volatilities implied* by options on the EURO STOXX 50 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching eurostoxx 50 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "import urllib.request\n",
    "\n",
    "es_url = 'http://www.stoxx.com/download/historical_values/hbrbcpe.txt'\n",
    "\n",
    "raw_es_filename = 'data/raw_es50.txt'\n",
    "with urllib.request.urlopen(es_url) as response:\n",
    "    with open(raw_es_filename,'wb') as file:\n",
    "        shutil.copyfileobj(response, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching vstoxx 50 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs_url = 'http://www.stoxx.com/download/historical_values/h_vstoxx.txt'\n",
    "\n",
    "raw_vs_filename = 'data/raw_vs50.txt'\n",
    "\n",
    "with urllib.request.urlopen(vs_url) as response:\n",
    "    with open(raw_vs_filename,'wb') as file:\n",
    "        shutil.copyfileobj(response, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning is necessary\n",
    "\n",
    "- useless blanks\n",
    "- couple of additional header lines to remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Price Indices - EURO Currency\\n',\n",
       " 'Date    ;Blue-Chip;Blue-Chip;Broad    ; Broad   ;Ex UK    ;Ex Euro Zone;Blue-Chip; Broad\\n',\n",
       " '        ;  Europe ;Euro-Zone;Europe   ;Euro-Zone;         ;            ; Nordic  ; Nordic\\n',\n",
       " '        ;  SX5P   ;  SX5E   ;SXXP     ;SXXE     ; SXXF    ;    SXXA    ;    DK5F ; DKXF\\n',\n",
       " '31.12.1986;775.00 ;  900.82 ;   82.76 ;   98.58 ;   98.06 ;   69.06 ;  645.26  ;  65.56\\n',\n",
       " '01.01.1987;775.00 ;  900.82 ;   82.76 ;   98.58 ;   98.06 ;   69.06 ;  645.26  ;  65.56\\n']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines = open(raw_es_filename, 'r').readlines()\n",
    "lines[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From 27/12/2001 onwards, semicolon at the end of line to remove as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 300.97;   317.10;   267.23;  5268.36 ;  363.19\n",
      "\n",
      " 306.60;   322.55;   272.18;  5360.52 ;  370.94\n",
      "\n",
      " 306.77;   322.69;   272.95;  5360.52 ;  370.94\n",
      "\n",
      " 312.43;   327.57;   277.68;  5479.59;   378.69;\n",
      "\n",
      " 314.52;   329.94;   278.87;  5585.35;   386.99;\n",
      "\n",
      " 311.43;   326.77;   272.38;  5522.25;   380.09;\n",
      "\n",
      " 317.54;   332.62;   277.08;  5722.57;   396.12;\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for line in lines[3883:3890]:\n",
    "    print(line[41:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PriceIndices-EUROCurrency\\n',\n",
       " 'Date;Blue-Chip;Blue-Chip;Broad;Broad;ExUK;ExEuroZone;Blue-Chip;Broad\\n',\n",
       " ';Europe;Euro-Zone;Europe;Euro-Zone;;;Nordic;Nordic\\n',\n",
       " ';SX5P;SX5E;SXXP;SXXE;SXXF;SXXA;DK5F;DKXF\\n',\n",
       " '31.12.1986;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n',\n",
       " '01.01.1987;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines = [line.replace(' ', '') for line in lines]\n",
    "lines[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to generate a new text file:\n",
    "- delete unneeded header lines\n",
    "- write an appropriate header line\n",
    "- add a helper column to remove the trailing semicolons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "es_filename = 'data/res50.txt'\n",
    "with open(es_filename,'w') as file:\n",
    "    # writes the corrected third line of the original file\n",
    "    # as first line of new file\n",
    "    file.writelines('date' + lines[3][:-1] + ';DEL' + lines[3][-1])\n",
    "    # writes the remaining lines of the original file\n",
    "    file.writelines(lines[4:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['date;SX5P;SX5E;SXXP;SXXE;SXXF;SXXA;DK5F;DKXF;DEL\\n',\n",
      " '31.12.1986;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n',\n",
      " '01.01.1987;775.00;900.82;82.76;98.58;98.06;69.06;645.26;65.56\\n',\n",
      " '02.01.1987;770.89;891.78;82.57;97.80;97.43;69.37;647.62;65.81\\n',\n",
      " '05.01.1987;771.89;898.33;82.82;98.60;98.19;69.16;649.94;65.82\\n']\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "with open(es_filename,'r') as file:\n",
    "    pprint.pprint(file.readlines()[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5P</th>\n",
       "      <th>SX5E</th>\n",
       "      <th>SXXP</th>\n",
       "      <th>SXXE</th>\n",
       "      <th>SXXF</th>\n",
       "      <th>SXXA</th>\n",
       "      <th>DK5F</th>\n",
       "      <th>DKXF</th>\n",
       "      <th>DEL</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-28</th>\n",
       "      <td>2847.0</td>\n",
       "      <td>2991.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>9072.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-29</th>\n",
       "      <td>2849.0</td>\n",
       "      <td>2992.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>9112.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>2843.0</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>9116.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-03</th>\n",
       "      <td>2845.0</td>\n",
       "      <td>2998.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>9131.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>2871.0</td>\n",
       "      <td>3030.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>9212.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SX5P    SX5E   SXXP   SXXE   SXXF   SXXA    DK5F   DKXF  DEL\n",
       "date                                                                      \n",
       "2016-09-28  2847.0  2991.0  343.0  324.0  408.0  350.0  9072.0  581.0  NaN\n",
       "2016-09-29  2849.0  2992.0  343.0  324.0  408.0  351.0  9112.0  583.0  NaN\n",
       "2016-09-30  2843.0  3002.0  343.0  325.0  408.0  350.0  9116.0  583.0  NaN\n",
       "2016-10-03  2845.0  2998.0  343.0  325.0  408.0  351.0  9131.0  584.0  NaN\n",
       "2016-10-04  2871.0  3030.0  346.0  328.0  411.0  354.0  9212.0  589.0  NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es = pd.read_csv(es_filename, index_col=0, parse_dates=True, sep=';', dayfirst=True)\n",
    "np.round(es.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The helper column has fulfilled its purpose and can now be deleted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 7673 entries, 1986-12-31 to 2016-10-04\n",
      "Data columns (total 8 columns):\n",
      "SX5P    7673 non-null float64\n",
      "SX5E    7673 non-null float64\n",
      "SXXP    7673 non-null float64\n",
      "SXXE    7673 non-null float64\n",
      "SXXF    7673 non-null float64\n",
      "SXXA    7673 non-null float64\n",
      "DK5F    7673 non-null float64\n",
      "DKXF    7673 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 539.5 KB\n"
     ]
    }
   ],
   "source": [
    "del es['DEL']\n",
    "es.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equipped with the knowledge about the structure of the EURO STOXX 50 data set, we\n",
    "can also use the advanced capabilities of the read_csv function to make the import\n",
    "more compact and efficient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5P</th>\n",
       "      <th>SX5E</th>\n",
       "      <th>SXXP</th>\n",
       "      <th>SXXE</th>\n",
       "      <th>SXXF</th>\n",
       "      <th>SXXA</th>\n",
       "      <th>DK5F</th>\n",
       "      <th>DKXF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-28</th>\n",
       "      <td>2846.55</td>\n",
       "      <td>2991.11</td>\n",
       "      <td>342.57</td>\n",
       "      <td>324.24</td>\n",
       "      <td>407.97</td>\n",
       "      <td>350.45</td>\n",
       "      <td>9072.09</td>\n",
       "      <td>581.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-29</th>\n",
       "      <td>2848.93</td>\n",
       "      <td>2991.58</td>\n",
       "      <td>342.72</td>\n",
       "      <td>324.08</td>\n",
       "      <td>407.65</td>\n",
       "      <td>350.90</td>\n",
       "      <td>9112.09</td>\n",
       "      <td>582.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>2843.17</td>\n",
       "      <td>3002.24</td>\n",
       "      <td>342.92</td>\n",
       "      <td>325.31</td>\n",
       "      <td>408.27</td>\n",
       "      <td>350.09</td>\n",
       "      <td>9115.81</td>\n",
       "      <td>583.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-03</th>\n",
       "      <td>2845.43</td>\n",
       "      <td>2998.50</td>\n",
       "      <td>343.23</td>\n",
       "      <td>325.08</td>\n",
       "      <td>408.44</td>\n",
       "      <td>350.92</td>\n",
       "      <td>9131.24</td>\n",
       "      <td>584.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>2871.06</td>\n",
       "      <td>3029.50</td>\n",
       "      <td>346.10</td>\n",
       "      <td>327.73</td>\n",
       "      <td>411.41</td>\n",
       "      <td>353.92</td>\n",
       "      <td>9212.05</td>\n",
       "      <td>588.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5P     SX5E    SXXP    SXXE    SXXF    SXXA     DK5F    DKXF\n",
       "2016-09-28  2846.55  2991.11  342.57  324.24  407.97  350.45  9072.09  581.27\n",
       "2016-09-29  2848.93  2991.58  342.72  324.08  407.65  350.90  9112.09  582.60\n",
       "2016-09-30  2843.17  3002.24  342.92  325.31  408.27  350.09  9115.81  583.26\n",
       "2016-10-03  2845.43  2998.50  343.23  325.08  408.44  350.92  9131.24  584.32\n",
       "2016-10-04  2871.06  3029.50  346.10  327.73  411.41  353.92  9212.05  588.71"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['SX5P', 'SX5E', 'SXXP', 'SXXE', 'SXXF',\n",
    "'SXXA', 'DK5F', 'DKXF']\n",
    "es = pd.read_csv(es_url, index_col=0, parse_dates=True,\n",
    "                 sep=';', dayfirst=True, header=None,\n",
    "                 skiprows=4, names=cols)\n",
    "es.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, the VSTOXX data set is already in a form such that it can be imported a bit\n",
    "more easily into a DataFrame object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4357 entries, 1999-01-04 to 2016-02-12\n",
      "Data columns (total 9 columns):\n",
      "V2TX    4357 non-null float64\n",
      "V6I1    3906 non-null float64\n",
      "V6I2    4357 non-null float64\n",
      "V6I3    4296 non-null float64\n",
      "V6I4    4357 non-null float64\n",
      "V6I5    4357 non-null float64\n",
      "V6I6    4340 non-null float64\n",
      "V6I7    4357 non-null float64\n",
      "V6I8    4343 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 340.4 KB\n"
     ]
    }
   ],
   "source": [
    "vs = pd.read_csv('data/raw_vs50.txt', index_col=0, header=2,\n",
    "parse_dates=True, sep=',', dayfirst=True)\n",
    "vs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V2TX</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>V6I2</th>\n",
       "      <th>V6I3</th>\n",
       "      <th>V6I4</th>\n",
       "      <th>V6I5</th>\n",
       "      <th>V6I6</th>\n",
       "      <th>V6I7</th>\n",
       "      <th>V6I8</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",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-04</th>\n",
       "      <td>18.2033</td>\n",
       "      <td>21.2458</td>\n",
       "      <td>17.5555</td>\n",
       "      <td>31.2179</td>\n",
       "      <td>33.3124</td>\n",
       "      <td>33.7327</td>\n",
       "      <td>33.2232</td>\n",
       "      <td>31.8535</td>\n",
       "      <td>23.8209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-05</th>\n",
       "      <td>29.6912</td>\n",
       "      <td>36.6400</td>\n",
       "      <td>28.4274</td>\n",
       "      <td>32.6922</td>\n",
       "      <td>33.7326</td>\n",
       "      <td>33.1724</td>\n",
       "      <td>32.8457</td>\n",
       "      <td>32.2904</td>\n",
       "      <td>25.0532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-06</th>\n",
       "      <td>25.1670</td>\n",
       "      <td>25.4107</td>\n",
       "      <td>25.1351</td>\n",
       "      <td>32.2186</td>\n",
       "      <td>32.6459</td>\n",
       "      <td>31.9673</td>\n",
       "      <td>32.9260</td>\n",
       "      <td>33.2871</td>\n",
       "      <td>26.0107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-07</th>\n",
       "      <td>32.5205</td>\n",
       "      <td>35.4410</td>\n",
       "      <td>32.2004</td>\n",
       "      <td>36.1265</td>\n",
       "      <td>34.5150</td>\n",
       "      <td>33.1095</td>\n",
       "      <td>33.2843</td>\n",
       "      <td>33.7269</td>\n",
       "      <td>26.2205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-08</th>\n",
       "      <td>33.2296</td>\n",
       "      <td>35.8846</td>\n",
       "      <td>33.0020</td>\n",
       "      <td>36.0813</td>\n",
       "      <td>36.3964</td>\n",
       "      <td>33.4658</td>\n",
       "      <td>33.4837</td>\n",
       "      <td>33.9227</td>\n",
       "      <td>26.3672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               V2TX     V6I1     V6I2     V6I3     V6I4     V6I5     V6I6  \\\n",
       "Date                                                                        \n",
       "1999-01-04  18.2033  21.2458  17.5555  31.2179  33.3124  33.7327  33.2232   \n",
       "1999-01-05  29.6912  36.6400  28.4274  32.6922  33.7326  33.1724  32.8457   \n",
       "1999-01-06  25.1670  25.4107  25.1351  32.2186  32.6459  31.9673  32.9260   \n",
       "1999-01-07  32.5205  35.4410  32.2004  36.1265  34.5150  33.1095  33.2843   \n",
       "1999-01-08  33.2296  35.8846  33.0020  36.0813  36.3964  33.4658  33.4837   \n",
       "\n",
       "               V6I7     V6I8  \n",
       "Date                          \n",
       "1999-01-04  31.8535  23.8209  \n",
       "1999-01-05  32.2904  25.0532  \n",
       "1999-01-06  33.2871  26.0107  \n",
       "1999-01-07  33.7269  26.2205  \n",
       "1999-01-08  33.9227  26.3672  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V2TX</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>V6I2</th>\n",
       "      <th>V6I3</th>\n",
       "      <th>V6I4</th>\n",
       "      <th>V6I5</th>\n",
       "      <th>V6I6</th>\n",
       "      <th>V6I7</th>\n",
       "      <th>V6I8</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>33.3917</td>\n",
       "      <td>34.7251</td>\n",
       "      <td>33.2189</td>\n",
       "      <td>31.8308</td>\n",
       "      <td>31.4783</td>\n",
       "      <td>30.6941</td>\n",
       "      <td>30.3705</td>\n",
       "      <td>29.8478</td>\n",
       "      <td>29.2559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-09</th>\n",
       "      <td>33.9664</td>\n",
       "      <td>35.7884</td>\n",
       "      <td>33.7800</td>\n",
       "      <td>32.6220</td>\n",
       "      <td>32.2459</td>\n",
       "      <td>31.5572</td>\n",
       "      <td>31.1659</td>\n",
       "      <td>30.5339</td>\n",
       "      <td>29.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-10</th>\n",
       "      <td>33.4528</td>\n",
       "      <td>34.6020</td>\n",
       "      <td>33.3637</td>\n",
       "      <td>32.1438</td>\n",
       "      <td>32.0018</td>\n",
       "      <td>31.2099</td>\n",
       "      <td>30.9937</td>\n",
       "      <td>30.8174</td>\n",
       "      <td>30.2533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-11</th>\n",
       "      <td>38.3051</td>\n",
       "      <td>41.2095</td>\n",
       "      <td>38.1347</td>\n",
       "      <td>35.8676</td>\n",
       "      <td>35.1073</td>\n",
       "      <td>33.7847</td>\n",
       "      <td>33.4126</td>\n",
       "      <td>32.2234</td>\n",
       "      <td>31.2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-12</th>\n",
       "      <td>35.6846</td>\n",
       "      <td>35.3849</td>\n",
       "      <td>35.6965</td>\n",
       "      <td>34.1404</td>\n",
       "      <td>33.8645</td>\n",
       "      <td>32.9590</td>\n",
       "      <td>32.8208</td>\n",
       "      <td>32.3189</td>\n",
       "      <td>31.8194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               V2TX     V6I1     V6I2     V6I3     V6I4     V6I5     V6I6  \\\n",
       "Date                                                                        \n",
       "2016-02-08  33.3917  34.7251  33.2189  31.8308  31.4783  30.6941  30.3705   \n",
       "2016-02-09  33.9664  35.7884  33.7800  32.6220  32.2459  31.5572  31.1659   \n",
       "2016-02-10  33.4528  34.6020  33.3637  32.1438  32.0018  31.2099  30.9937   \n",
       "2016-02-11  38.3051  41.2095  38.1347  35.8676  35.1073  33.7847  33.4126   \n",
       "2016-02-12  35.6846  35.3849  35.6965  34.1404  33.8645  32.9590  32.8208   \n",
       "\n",
       "               V6I7     V6I8  \n",
       "Date                          \n",
       "2016-02-08  29.8478  29.2559  \n",
       "2016-02-09  30.5339  29.9000  \n",
       "2016-02-10  30.8174  30.2533  \n",
       "2016-02-11  32.2234  31.2124  \n",
       "2016-02-12  32.3189  31.8194  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "help(pd.read_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To implement the regression analysis, we only need one column from each data set:\n",
    "- SX5E from Eurostoxx 50\n",
    "- V2TX from Vstoxx\n",
    "\n",
    "Since VSTOXX data is only available\n",
    "from the beginning of January 1999, we only take data from that date on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "start_date = datetime.datetime(1999, 1, 1)\n",
    "end_date = datetime.datetime(2016, 2, 12)\n",
    "filtered_es = pd.DataFrame({'EUROSTOXX' : es['SX5E'][(es.index > start_date) & (es.index < end_date)]})\n",
    "filtered_vs = pd.DataFrame({'VSTOXX' : vs['V2TX'][(vs.index > start_date) & (vs.index < end_date)]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We therefore generate a new DataFrame object within which we combine the two columns\n",
    "of interest, namely those for the major indexes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method join in module pandas.core.frame:\n",
      "\n",
      "join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False) method of pandas.core.frame.DataFrame instance\n",
      "    Join columns with other DataFrame either on index or on a key\n",
      "    column. Efficiently Join multiple DataFrame objects by index at once by\n",
      "    passing a list.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    other : DataFrame, Series with name field set, or list of DataFrame\n",
      "        Index should be similar to one of the columns in this one. If a\n",
      "        Series is passed, its name attribute must be set, and that will be\n",
      "        used as the column name in the resulting joined DataFrame\n",
      "    on : column name, tuple/list of column names, or array-like\n",
      "        Column(s) in the caller to join on the index in other,\n",
      "        otherwise joins index-on-index. If multiples\n",
      "        columns given, the passed DataFrame must have a MultiIndex. Can\n",
      "        pass an array as the join key if not already contained in the\n",
      "        calling DataFrame. Like an Excel VLOOKUP operation\n",
      "    how : {'left', 'right', 'outer', 'inner'}, default: 'left'\n",
      "        How to handle the operation of the two objects.\n",
      "    \n",
      "        * left: use calling frame's index (or column if on is specified)\n",
      "        * right: use other frame's index\n",
      "        * outer: form union of calling frame's index (or column if on is\n",
      "          specified) with other frame's index, and sort it\n",
      "          lexicographically\n",
      "        * inner: form intersection of calling frame's index (or column if\n",
      "          on is specified) with other frame's index, preserving the order\n",
      "          of the calling's one\n",
      "    lsuffix : string\n",
      "        Suffix to use from left frame's overlapping columns\n",
      "    rsuffix : string\n",
      "        Suffix to use from right frame's overlapping columns\n",
      "    sort : boolean, default False\n",
      "        Order result DataFrame lexicographically by the join key. If False,\n",
      "        the order of the join key depends on the join type (how keyword)\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    on, lsuffix, and rsuffix options are not supported when passing a list\n",
      "    of DataFrame objects\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> caller = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],\n",
      "    ...                        'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\n",
      "    \n",
      "    >>> caller\n",
      "        A key\n",
      "    0  A0  K0\n",
      "    1  A1  K1\n",
      "    2  A2  K2\n",
      "    3  A3  K3\n",
      "    4  A4  K4\n",
      "    5  A5  K5\n",
      "    \n",
      "    >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],\n",
      "    ...                       'B': ['B0', 'B1', 'B2']})\n",
      "    \n",
      "    >>> other\n",
      "        B key\n",
      "    0  B0  K0\n",
      "    1  B1  K1\n",
      "    2  B2  K2\n",
      "    \n",
      "    Join DataFrames using their indexes.\n",
      "    \n",
      "    >>> caller.join(other, lsuffix='_caller', rsuffix='_other')\n",
      "    \n",
      "    >>>     A key_caller    B key_other\n",
      "        0  A0         K0   B0        K0\n",
      "        1  A1         K1   B1        K1\n",
      "        2  A2         K2   B2        K2\n",
      "        3  A3         K3  NaN       NaN\n",
      "        4  A4         K4  NaN       NaN\n",
      "        5  A5         K5  NaN       NaN\n",
      "    \n",
      "    \n",
      "    If we want to join using the key columns, we need to set key to be\n",
      "    the index in both caller and other. The joined DataFrame will have\n",
      "    key as its index.\n",
      "    \n",
      "    >>> caller.set_index('key').join(other.set_index('key'))\n",
      "    \n",
      "    >>>      A    B\n",
      "        key\n",
      "        K0   A0   B0\n",
      "        K1   A1   B1\n",
      "        K2   A2   B2\n",
      "        K3   A3  NaN\n",
      "        K4   A4  NaN\n",
      "        K5   A5  NaN\n",
      "    \n",
      "    Another option to join using the key columns is to use the on\n",
      "    parameter. DataFrame.join always uses other's index but we can use any\n",
      "    column in the caller. This method preserves the original caller's\n",
      "    index in the result.\n",
      "    \n",
      "    >>> caller.join(other.set_index('key'), on='key')\n",
      "    \n",
      "    >>>     A key    B\n",
      "        0  A0  K0   B0\n",
      "        1  A1  K1   B1\n",
      "        2  A2  K2   B2\n",
      "        3  A3  K3  NaN\n",
      "        4  A4  K4  NaN\n",
      "        5  A5  K5  NaN\n",
      "    \n",
      "    \n",
      "    See also\n",
      "    --------\n",
      "    DataFrame.merge : For column(s)-on-columns(s) operations\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    joined : DataFrame\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.DataFrame().join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4386 entries, 1999-01-04 to 2016-02-11\n",
      "Data columns (total 2 columns):\n",
      "EUROSTOXX    4386 non-null float64\n",
      "VSTOXX       4355 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 262.8 KB\n"
     ]
    }
   ],
   "source": [
    "data = filtered_es.join(filtered_vs)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "31 vstoxx values are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUROSTOXX</th>\n",
       "      <th>VSTOXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-05-13</th>\n",
       "      <td>3693.62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-24</th>\n",
       "      <td>4781.16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-24</th>\n",
       "      <td>3696.98</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-01</th>\n",
       "      <td>2324.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-24</th>\n",
       "      <td>2725.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>2760.66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-24</th>\n",
       "      <td>2951.16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>2951.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>3839.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-01</th>\n",
       "      <td>4392.41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-24</th>\n",
       "      <td>4388.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-01</th>\n",
       "      <td>3825.02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-24</th>\n",
       "      <td>2377.42</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>2447.62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-01</th>\n",
       "      <td>2375.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-24</th>\n",
       "      <td>2957.03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-24</th>\n",
       "      <td>2861.94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>2792.82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-01</th>\n",
       "      <td>2306.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-24</th>\n",
       "      <td>2648.53</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>2635.93</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>2711.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-24</th>\n",
       "      <td>3072.88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>3109.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>3198.66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-24</th>\n",
       "      <td>3184.66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>3146.43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>3615.59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-25</th>\n",
       "      <td>3655.41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>3284.47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>3267.52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EUROSTOXX  VSTOXX\n",
       "1999-05-13    3693.62     NaN\n",
       "1999-12-24    4781.16     NaN\n",
       "2001-12-24    3696.98     NaN\n",
       "2003-05-01    2324.23     NaN\n",
       "2003-12-24    2725.07     NaN\n",
       "2003-12-31    2760.66     NaN\n",
       "2004-12-24    2951.16     NaN\n",
       "2004-12-31    2951.01     NaN\n",
       "2006-05-01    3839.90     NaN\n",
       "2007-05-01    4392.41     NaN\n",
       "2007-12-24    4388.38     NaN\n",
       "2008-05-01    3825.02     NaN\n",
       "2008-12-24    2377.42     NaN\n",
       "2008-12-31    2447.62     NaN\n",
       "2009-05-01    2375.34     NaN\n",
       "2009-12-24    2957.03     NaN\n",
       "2010-12-24    2861.94     NaN\n",
       "2010-12-31    2792.82     NaN\n",
       "2012-05-01    2306.69     NaN\n",
       "2012-12-24    2648.53     NaN\n",
       "2012-12-31    2635.93     NaN\n",
       "2013-05-01    2711.74     NaN\n",
       "2013-12-24    3072.88     NaN\n",
       "2013-12-31    3109.00     NaN\n",
       "2014-05-01    3198.66     NaN\n",
       "2014-12-24    3184.66     NaN\n",
       "2014-12-31    3146.43     NaN\n",
       "2015-05-01    3615.59     NaN\n",
       "2015-05-25    3655.41     NaN\n",
       "2015-12-24    3284.47     NaN\n",
       "2015-12-31    3267.52     NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_indices = data[data.isnull().any(axis=1)].index.values\n",
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(missing_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the vstoxx is updated at 31st Dec of 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4386 entries, 1999-01-04 to 2016-02-11\n",
      "Data columns (total 2 columns):\n",
      "EUROSTOXX    4386 non-null float64\n",
      "VSTOXX       4386 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 262.8 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>EUROSTOXX</th>\n",
       "      <th>VSTOXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>3314.28</td>\n",
       "      <td>21.6067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>3287.98</td>\n",
       "      <td>22.1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>3267.52</td>\n",
       "      <td>22.1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>3164.76</td>\n",
       "      <td>26.9980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EUROSTOXX   VSTOXX\n",
       "2015-12-29    3314.28  21.6067\n",
       "2015-12-30    3287.98  22.1745\n",
       "2015-12-31    3267.52  22.1745\n",
       "2016-01-04    3164.76  26.9980"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.index > datetime.datetime(2015,12,28)) & (data.index < datetime.datetime(2016,1,5))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002B024FAE630>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000002B0250A39B0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b024e47160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "data.plot(subplots=True, grid=True, style='b', figsize=(8, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is more appropriate to work on log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rets = np.log(data / data.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>EUROSTOXX</th>\n",
       "      <th>VSTOXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-05</th>\n",
       "      <td>0.017228</td>\n",
       "      <td>0.489248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-06</th>\n",
       "      <td>0.022138</td>\n",
       "      <td>-0.165317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-07</th>\n",
       "      <td>-0.015723</td>\n",
       "      <td>0.256337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-08</th>\n",
       "      <td>-0.003120</td>\n",
       "      <td>0.021570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EUROSTOXX    VSTOXX\n",
       "1999-01-04        NaN       NaN\n",
       "1999-01-05   0.017228  0.489248\n",
       "1999-01-06   0.022138 -0.165317\n",
       "1999-01-07  -0.015723  0.256337\n",
       "1999-01-08  -0.003120  0.021570"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002B02645ACC0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000002B0264A0BA8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b026437470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.plot(subplots=True, grid=True, style='b', figsize=(8, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>VSTOXX</td>      <th>  R-squared:         </th>  <td>   0.543</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.543</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   5208.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 30 Sep 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:14:41</td>     <th>  Log-Likelihood:    </th>  <td>  7964.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  4385</td>      <th>  AIC:               </th> <td>-1.592e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4383</td>      <th>  BIC:               </th> <td>-1.591e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</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>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>-1.147e-05</td> <td>    0.001</td> <td>   -0.019</td> <td> 0.985</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EUROSTOXX</th> <td>   -2.8463</td> <td>    0.039</td> <td>  -72.164</td> <td> 0.000</td> <td>   -2.924</td> <td>   -2.769</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1315.684</td> <th>  Durbin-Watson:     </th> <td>   2.050</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>24606.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.951</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>14.448</td>  <th>  Cond. No.          </th> <td>    66.4</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 VSTOXX   R-squared:                       0.543\n",
       "Model:                            OLS   Adj. R-squared:                  0.543\n",
       "Method:                 Least Squares   F-statistic:                     5208.\n",
       "Date:                Sun, 30 Sep 2018   Prob (F-statistic):               0.00\n",
       "Time:                        09:14:41   Log-Likelihood:                 7964.0\n",
       "No. Observations:                4385   AIC:                        -1.592e+04\n",
       "Df Residuals:                    4383   BIC:                        -1.591e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const      -1.147e-05      0.001     -0.019      0.985      -0.001       0.001\n",
       "EUROSTOXX     -2.8463      0.039    -72.164      0.000      -2.924      -2.769\n",
       "==============================================================================\n",
       "Omnibus:                     1315.684   Durbin-Watson:                   2.050\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            24606.871\n",
       "Skew:                           0.951   Prob(JB):                         0.00\n",
       "Kurtosis:                      14.448   Cond. No.                         66.4\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "X = rets.EUROSTOXX\n",
    "Y = rets.VSTOXX\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y, X, missing='drop')\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>EUROSTOXX</th>\n",
       "      <th>VSTOXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EUROSTOXX</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.736878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VSTOXX</th>\n",
       "      <td>-0.736878</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           EUROSTOXX    VSTOXX\n",
       "EUROSTOXX   1.000000 -0.736878\n",
       "VSTOXX     -0.736878  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously, there is indeed a highly negative correlation. We can access the results as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1469282088236458e-05"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'VSTOXX returns')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b028d8aac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(rets.EUROSTOXX, rets.VSTOXX, 'r.')\n",
    "ax = plt.axis() # grab axis values\n",
    "x = np.linspace(ax[0], ax[1] + 0.01)\n",
    "plt.plot(x, results.params[0] + results.params[1] * x, 'b', lw=2)\n",
    "plt.grid(True)\n",
    "plt.axis('tight')\n",
    "plt.xlabel('EURO STOXX 50 returns')\n",
    "plt.ylabel('VSTOXX returns')"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}