09_dx_calibration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"45%\" align=\"right\" border=\"4\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implied Volatilities and Model Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This setion of the documentation illustrates how to calculate **implied volatilities** and how to calibrate a model to **VSTOXX volatility index call option quotes**. The example implements the calibration for a total of **one month worth of data**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dx import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pylab import plt\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VSTOXX Futures & Options Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by loading **VSTOXX data** from a pandas `HDFStore` into `DataFrame` objects (source: Eurex, cf. http://www.eurexchange.com/advanced-services/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5 = pd.HDFStore('./data/vstoxx_march_2014.h5', 'r')\n",
    "vstoxx_index = h5['vstoxx_index'] \n",
    "vstoxx_futures = h5['vstoxx_futures'] \n",
    "vstoxx_options = h5['vstoxx_options']\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**VSTOXX index** for the first quarter of 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115520978>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WwkkM+p31XBpphAMwiSp0ZmIFf/Pjd5DJVpd6X43kZzg1PePEwIkqpx4QvRZPa7wSbYSj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d43c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "vstoxx_index['V2TX'].plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **VSTOXX futures** data (8 futures maturities/quotes per day)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 504 entries, 0 to 503\n",
      "Data columns (total 5 columns):\n",
      "DATE         504 non-null datetime64[ns]\n",
      "EXP_YEAR     504 non-null int64\n",
      "EXP_MONTH    504 non-null int64\n",
      "PRICE        504 non-null float64\n",
      "MATURITY     504 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int64(2)\n",
      "memory usage: 23.6 KB\n"
     ]
    }
   ],
   "source": [
    "vstoxx_futures.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>DATE</th>\n",
       "      <th>EXP_YEAR</th>\n",
       "      <th>EXP_MONTH</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>MATURITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>20.40</td>\n",
       "      <td>2014-07-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2014-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>20.95</td>\n",
       "      <td>2014-09-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>21.05</td>\n",
       "      <td>2014-10-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>21.25</td>\n",
       "      <td>2014-11-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  EXP_YEAR  EXP_MONTH  PRICE   MATURITY\n",
       "499 2014-03-31      2014          7  20.40 2014-07-18\n",
       "500 2014-03-31      2014          8  20.70 2014-08-15\n",
       "501 2014-03-31      2014          9  20.95 2014-09-19\n",
       "502 2014-03-31      2014         10  21.05 2014-10-17\n",
       "503 2014-03-31      2014         11  21.25 2014-11-21"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstoxx_futures.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **VSTOXX options** data. This data set is quite large due to the large number of European put and call options on the VSTOXX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 46960 entries, 0 to 46959\n",
      "Data columns (total 7 columns):\n",
      "DATE         46960 non-null datetime64[ns]\n",
      "EXP_YEAR     46960 non-null int64\n",
      "EXP_MONTH    46960 non-null int64\n",
      "TYPE         46960 non-null object\n",
      "STRIKE       46960 non-null float64\n",
      "PRICE        46960 non-null float64\n",
      "MATURITY     46960 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(2), object(1)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "vstoxx_options.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>DATE</th>\n",
       "      <th>EXP_YEAR</th>\n",
       "      <th>EXP_MONTH</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>STRIKE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>MATURITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46955</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>P</td>\n",
       "      <td>85.0</td>\n",
       "      <td>63.65</td>\n",
       "      <td>2014-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46956</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>P</td>\n",
       "      <td>90.0</td>\n",
       "      <td>68.65</td>\n",
       "      <td>2014-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46957</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>P</td>\n",
       "      <td>95.0</td>\n",
       "      <td>73.65</td>\n",
       "      <td>2014-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46958</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>P</td>\n",
       "      <td>100.0</td>\n",
       "      <td>78.65</td>\n",
       "      <td>2014-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46959</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>P</td>\n",
       "      <td>105.0</td>\n",
       "      <td>83.65</td>\n",
       "      <td>2014-11-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  EXP_YEAR  EXP_MONTH TYPE  STRIKE  PRICE   MATURITY\n",
       "46955 2014-03-31      2014         11    P    85.0  63.65 2014-11-21\n",
       "46956 2014-03-31      2014         11    P    90.0  68.65 2014-11-21\n",
       "46957 2014-03-31      2014         11    P    95.0  73.65 2014-11-21\n",
       "46958 2014-03-31      2014         11    P   100.0  78.65 2014-11-21\n",
       "46959 2014-03-31      2014         11    P   105.0  83.65 2014-11-21"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstoxx_options.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a helper function we need a function to calculate all relevant **third Fridays** for all relevant maturity months of the data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import calendar\n",
    "\n",
    "def third_friday(date):\n",
    "    day = 21 - (calendar.weekday(date.year, date.month, 1) + 2) % 7\n",
    "    return dt.datetime(date.year, date.month, day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "third_fridays = {}\n",
    "for month in set(vstoxx_futures['EXP_MONTH']):\n",
    "    third_fridays[month] = third_friday(dt.datetime(2014, month, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: datetime.datetime(2014, 1, 17, 0, 0),\n",
       " 2: datetime.datetime(2014, 2, 21, 0, 0),\n",
       " 3: datetime.datetime(2014, 3, 21, 0, 0),\n",
       " 4: datetime.datetime(2014, 4, 18, 0, 0),\n",
       " 5: datetime.datetime(2014, 5, 16, 0, 0),\n",
       " 6: datetime.datetime(2014, 6, 20, 0, 0),\n",
       " 7: datetime.datetime(2014, 7, 18, 0, 0),\n",
       " 8: datetime.datetime(2014, 8, 15, 0, 0),\n",
       " 9: datetime.datetime(2014, 9, 19, 0, 0),\n",
       " 10: datetime.datetime(2014, 10, 17, 0, 0),\n",
       " 11: datetime.datetime(2014, 11, 21, 0, 0)}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third_fridays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implied Volatilities from Market Quotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often calibration efforts are undertaken to replicate the **market implied volatilities** or the so-called **volatility surface** as good as possible. With DX Analytics and the `BSM_european_option` class, you can efficiently calculate (i.e. numerically estimate) implied volatilities. For the example, we use the **VSTOXX futures and call options data** from 31. March 2014. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some definitions, the pre-selection of option data and the pre-definition of the market environment needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "V0 = 17.6639  # VSTOXX level on 31.03.2014\n",
    "futures_data = vstoxx_futures[vstoxx_futures.DATE == '2014/3/31'].copy()\n",
    "options_data = vstoxx_options[(vstoxx_options.DATE == '2014/3/31')\n",
    "                              & (vstoxx_options.TYPE == 'C')].copy()\n",
    "me = market_environment('me', dt.datetime(2014, 3, 31))\n",
    "me.add_constant('initial_value', 17.6639)  # index on 31.03.2014\n",
    "me.add_constant('volatility', 2.0)  # for initialization\n",
    "me.add_curve('discount_curve', constant_short_rate('r', 0.01))  # assumption\n",
    "options_data['IMP_VOL'] = 0.0  # initialization new iv column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following loop now **calculates the implied volatilities** for all those options whose strike lies within the defined tolerance level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 691 ms, sys: 6.05 ms, total: 698 ms\n",
      "Wall time: 695 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "tol = 0.3  # tolerance level for moneyness\n",
    "for option in options_data.index:\n",
    "    # iterating over all option quotes\n",
    "    forward = futures_data[futures_data['MATURITY'] == \\\n",
    "                options_data.loc[option]['MATURITY']]['PRICE'].values\n",
    "                # picking the right futures value\n",
    "    if (forward * (1 - tol) < options_data.loc[option]['STRIKE']\n",
    "                             < forward * (1 + tol)):\n",
    "        # only for options with moneyness within tolerance\n",
    "        call = options_data.loc[option]\n",
    "        me.add_constant('strike', call['STRIKE'])\n",
    "        me.add_constant('maturity', call['MATURITY'])\n",
    "        call_option = BSM_european_option('call', me)\n",
    "        options_data.loc[option, 'IMP_VOL'] = \\\n",
    "            call_option.imp_vol(call['PRICE'], 'call', volatility_est=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A selection of the **results**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>DATE</th>\n",
       "      <th>EXP_YEAR</th>\n",
       "      <th>EXP_MONTH</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>STRIKE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>MATURITY</th>\n",
       "      <th>IMP_VOL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46230</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.55</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46231</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46232</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>1.541568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46233</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>1.321803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46234</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>1.153001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46235</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>1.042549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46236</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.997178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46237</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.969301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46238</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.958777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46239</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.968430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  EXP_YEAR  EXP_MONTH TYPE  STRIKE  PRICE   MATURITY   IMP_VOL\n",
       "46230 2014-03-31      2014          5    C    12.0   7.55 2014-05-16  0.000000\n",
       "46231 2014-03-31      2014          5    C    13.0   6.55 2014-05-16  0.000000\n",
       "46232 2014-03-31      2014          5    C    14.0   5.55 2014-05-16  1.541568\n",
       "46233 2014-03-31      2014          5    C    15.0   4.55 2014-05-16  1.321803\n",
       "46234 2014-03-31      2014          5    C    16.0   3.65 2014-05-16  1.153001\n",
       "46235 2014-03-31      2014          5    C    17.0   2.90 2014-05-16  1.042549\n",
       "46236 2014-03-31      2014          5    C    18.0   2.35 2014-05-16  0.997178\n",
       "46237 2014-03-31      2014          5    C    19.0   1.90 2014-05-16  0.969301\n",
       "46238 2014-03-31      2014          5    C    20.0   1.55 2014-05-16  0.958777\n",
       "46239 2014-03-31      2014          5    C    21.0   1.30 2014-05-16  0.968430"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_data[60:70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the **complete results visualized**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Korn37YCKLDExtXIEamYVZSV43cH9uPTrmfc9ZdO2Mv1HWVIVJT8VmeSoaJKf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114af2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plot_data = options_data[options_data.IMP_VOL > 0]\n",
    "plt.figure(figsize=(10, 6))\n",
    "for maturity in sorted(set(options_data['MATURITY'])):\n",
    "    data = plot_data.isin({'MATURITY': [maturity,]})\n",
    "    data = plot_data[plot_data.MATURITY == maturity]\n",
    "      # select data for this maturity\n",
    "    plt.plot(data['STRIKE'], data['IMP_VOL'],\n",
    "             label=maturity.date(), lw=1.5)\n",
    "    plt.plot(data['STRIKE'], data['IMP_VOL'], 'r.')\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('implied volatility of volatility')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Market Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sub-section now implements the model calibration based on **selected options data**. In particular, we choose, for a given pricing date, the following options data:\n",
    "\n",
    "* for a single maturity only\n",
    "* call options only\n",
    "* for a certain moneyness of the options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relevant Market Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following following returns the **relevant market data per calibration date**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tol = 0.2\n",
    "def get_option_selection(pricing_date, maturity, tol=tol):\n",
    "    ''' Function selects relevant options data. '''\n",
    "    forward = vstoxx_futures[(vstoxx_futures.DATE == pricing_date)\n",
    "                & (vstoxx_futures.MATURITY == maturity)]['PRICE'].values[0]\n",
    "    option_selection = \\\n",
    "        vstoxx_options[(vstoxx_options.DATE == pricing_date)\n",
    "                     & (vstoxx_options.MATURITY == maturity)\n",
    "                     & (vstoxx_options.TYPE == 'C')\n",
    "                     & (vstoxx_options.STRIKE > (1 - tol) * forward)\n",
    "                     & (vstoxx_options.STRIKE < (1 + tol) * forward)]\n",
    "    return option_selection, forward"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Options Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the options and their respective quotes to which to calibrate the model, the function `get_option_models` returns the **DX Analytics option models for all relevant options**. As **risk factor model** we use the `square_root_diffusion` class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_option_models(pricing_date, maturity, option_selection):\n",
    "    ''' Models and returns traded options for given option_selection object. '''\n",
    "    me_vstoxx = market_environment('me_vstoxx', pricing_date)\n",
    "    initial_value = vstoxx_index['V2TX'][pricing_date]\n",
    "    me_vstoxx.add_constant('initial_value', initial_value)\n",
    "    me_vstoxx.add_constant('final_date', maturity)\n",
    "    me_vstoxx.add_constant('currency', 'EUR')\n",
    "    me_vstoxx.add_constant('frequency', 'W')\n",
    "    me_vstoxx.add_constant('paths', 10000)\n",
    "    csr = constant_short_rate('csr', 0.01)\n",
    "      # somewhat arbitrarily chosen here\n",
    "    me_vstoxx.add_curve('discount_curve', csr)\n",
    "    \n",
    "    # parameters to be calibrated later\n",
    "    me_vstoxx.add_constant('kappa', 1.0)\n",
    "    me_vstoxx.add_constant('theta', 1.2 * initial_value)\n",
    "    me_vstoxx.add_constant('volatility', 1.0)\n",
    "    \n",
    "    vstoxx_model = square_root_diffusion('vstoxx_model', me_vstoxx)\n",
    "      # square-root diffusion for volatility modeling\n",
    "      # mean-reverting, positive process\n",
    "    \n",
    "    # option parameters and payoff\n",
    "    me_vstoxx.add_constant('maturity', maturity)\n",
    "    payoff_func = 'np.maximum(maturity_value - strike, 0)'\n",
    "    \n",
    "    option_models = {}\n",
    "    for option in option_selection.index:\n",
    "        strike = option_selection['STRIKE'].ix[option]\n",
    "        me_vstoxx.add_constant('strike', strike)\n",
    "        option_models[option] = \\\n",
    "                            valuation_mcs_european_single(\n",
    "                                    'eur_call_%d' % strike,\n",
    "                                    vstoxx_model,\n",
    "                                    me_vstoxx,\n",
    "                                    payoff_func)\n",
    "    return vstoxx_model, option_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `calculate_model_values` estimates and returns **model value estimates for all relevant options** given a parameter set for the `square_root_diffusion` risk factor model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_model_values(p0):\n",
    "    ''' Returns all relevant option values.\n",
    "    \n",
    "    Parameters\n",
    "    ===========\n",
    "    p0 : tuple/list\n",
    "        tuple of kappa, theta, volatility\n",
    "    \n",
    "    Returns\n",
    "    =======\n",
    "    model_values : dict\n",
    "        dictionary with model values\n",
    "    '''\n",
    "    kappa, theta, volatility = p0\n",
    "    vstoxx_model.update(kappa=kappa,\n",
    "                        theta=theta,\n",
    "                        volatility=volatility)\n",
    "    model_values = {}\n",
    "    for option in option_models:\n",
    "       model_values[option] = \\\n",
    "         option_models[option].present_value(fixed_seed=True)\n",
    "    return model_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean-Squared Error Calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calibration of the pricing model is based on the **minimization of the mean-squared error (MSE)** of the model values vs. the market quotes. The MSE calculation is implemented by the function `mean_squared_error` which also **penalizes** economically implausible parameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "def mean_squared_error(p0):\n",
    "    ''' Returns the mean-squared error given\n",
    "    the model and market values.\n",
    "    \n",
    "    Parameters\n",
    "    ===========\n",
    "    p0 : tuple/list\n",
    "        tuple of kappa, theta, volatility\n",
    "    \n",
    "    Returns\n",
    "    =======\n",
    "    MSE : float\n",
    "        mean-squared error\n",
    "    '''\n",
    "    if p0[0] < 0 or p0[1] < 5. or p0[2] < 0 or p0[2] > 10.:\n",
    "        return 100\n",
    "    global i, option_selection, vstoxx_model, option_models, first, last\n",
    "    pd = dt.datetime.strftime(\n",
    "            option_selection['DATE'].iloc[0].to_pydatetime(),\n",
    "            '%d-%b-%Y')\n",
    "    mat = dt.datetime.strftime(\n",
    "            option_selection['MATURITY'].iloc[0].to_pydatetime(),\n",
    "            '%d-%b-%Y')\n",
    "    model_values = calculate_model_values(p0)\n",
    "    option_diffs = {}\n",
    "    for option in model_values:\n",
    "        option_diffs[option] = (model_values[option]\n",
    "                              - option_selection['PRICE'].loc[option])\n",
    "    MSE = np.sum(np.array(list(option_diffs.values())) ** 2) / len(option_diffs)\n",
    "    if i % 150 == 0:\n",
    "        # output every 0th and 100th iteration\n",
    "        if i == 0:\n",
    "            print('%12s %13s %4s  %6s  %6s  %6s  --> %6s' % \\\n",
    "                 ('pricing_date', 'maturity_date', 'i', 'kappa',\n",
    "                  'theta', 'vola', 'MSE'))\n",
    "        print('%12s %13s %4d  %6.3f  %6.3f  %6.3f  --> %6.3f' % \\\n",
    "                (pd, mat, i, p0[0], p0[1], p0[2], MSE))\n",
    "    i += 1\n",
    "    return MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the Calibration Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `get_parameter_series` calibrates the model to the market data for **every date** contained in the `pricing_date_list` object for **all maturities** contained in the `maturity_list` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.optimize as spo\n",
    "def get_parameter_series(pricing_date_list, maturity_list):\n",
    "    global i, option_selection, vstoxx_model, option_models, first, last\n",
    "    # collects optimization results for later use (eg. visualization)\n",
    "    parameters = pd.DataFrame()\n",
    "    for maturity in maturity_list:\n",
    "        first = True\n",
    "        for pricing_date in pricing_date_list:\n",
    "            option_selection, forward = \\\n",
    "                    get_option_selection(pricing_date, maturity)\n",
    "            vstoxx_model, option_models = \\\n",
    "                    get_option_models(pricing_date, maturity, option_selection)\n",
    "            if first is True:\n",
    "                # use brute force for the first run\n",
    "                i = 0\n",
    "                opt = spo.brute(mean_squared_error,\n",
    "                    ((0.5, 2.51, 1.),   # range for kappa\n",
    "                     (10., 20.1, 5.),   # range for theta\n",
    "                     (0.5, 10.51, 5.0)),  # range for volatility\n",
    "                     finish=None)\n",
    "            i = 0\n",
    "            opt = spo.fmin(mean_squared_error, opt,\n",
    "                 maxiter=200, maxfun=350, xtol=0.0000001, ftol=0.0000001)\n",
    "            parameters = parameters.append(\n",
    "                     pd.DataFrame(\n",
    "                     {'pricing_date' : pricing_date,\n",
    "                      'maturity' : maturity,\n",
    "                      'initial_value' : vstoxx_model.initial_value,\n",
    "                      'kappa' : opt[0],\n",
    "                      'theta' : opt[1],\n",
    "                      'sigma' : opt[2],\n",
    "                      'MSE' : mean_squared_error(opt)}, index=[0,]),\n",
    "                     ignore_index=True)\n",
    "            first = False\n",
    "            last = opt\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Calibration Itself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This completes the set of necessary function to implement such a **larger calibration effort**. The following code defines the **dates** for which a calibration shall be conducted and for which **maturities** the calibration is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 03-Mar-2014   18-Jul-2014    0   0.500  10.000   0.500  -->  4.507\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 03-Mar-2014   18-Jul-2014    0   2.500  15.000   5.500  -->  0.022\n",
      " 03-Mar-2014   18-Jul-2014  150   2.490  17.012   4.665  -->  0.005\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.004840\n",
      "         Iterations: 146\n",
      "         Function evaluations: 296\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 04-Mar-2014   18-Jul-2014    0   2.497  17.010   4.674  -->  0.048\n",
      " 04-Mar-2014   18-Jul-2014  150   2.474  17.633   4.738  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002750\n",
      "         Iterations: 70\n",
      "         Function evaluations: 164\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 05-Mar-2014   18-Jul-2014    0   2.474  17.633   4.738  -->  0.008\n",
      " 05-Mar-2014   18-Jul-2014  150   3.042  17.856   4.919  -->  0.003\n",
      " 05-Mar-2014   18-Jul-2014  300   4.407  17.995   5.667  -->  0.003\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 06-Mar-2014   18-Jul-2014    0   4.407  17.995   5.668  -->  0.004\n",
      " 06-Mar-2014   18-Jul-2014  150   4.543  18.209   5.661  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.003179\n",
      "         Iterations: 76\n",
      "         Function evaluations: 175\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 07-Mar-2014   18-Jul-2014    0   4.543  18.209   5.661  -->  0.030\n",
      " 07-Mar-2014   18-Jul-2014  150   4.958  18.332   5.553  -->  0.005\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.004775\n",
      "         Iterations: 84\n",
      "         Function evaluations: 183\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 10-Mar-2014   18-Jul-2014    0   4.958  18.332   5.553  -->  0.086\n",
      " 10-Mar-2014   18-Jul-2014  150   4.816  18.733   5.722  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002975\n",
      "         Iterations: 75\n",
      "         Function evaluations: 173\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 11-Mar-2014   18-Jul-2014    0   4.816  18.733   5.722  -->  0.006\n",
      " 11-Mar-2014   18-Jul-2014  150   4.281  19.060   5.162  -->  0.004\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.004069\n",
      "         Iterations: 100\n",
      "         Function evaluations: 210\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 12-Mar-2014   18-Jul-2014    0   4.281  19.060   5.162  -->  0.008\n",
      " 12-Mar-2014   18-Jul-2014  150   4.461  18.959   5.231  -->  0.005\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.004915\n",
      "         Iterations: 67\n",
      "         Function evaluations: 164\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 13-Mar-2014   18-Jul-2014    0   4.461  18.959   5.231  -->  0.007\n",
      " 13-Mar-2014   18-Jul-2014  150   4.515  18.920   5.333  -->  0.006\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.005971\n",
      "         Iterations: 84\n",
      "         Function evaluations: 189\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 14-Mar-2014   18-Jul-2014    0   4.515  18.920   5.333  -->  0.017\n",
      " 14-Mar-2014   18-Jul-2014  150   5.124  18.963   5.952  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002936\n",
      "         Iterations: 131\n",
      "         Function evaluations: 280\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 17-Mar-2014   18-Jul-2014    0   5.223  19.002   5.997  -->  0.025\n",
      " 17-Mar-2014   18-Jul-2014  150   5.330  18.581   6.097  -->  0.004\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.003809\n",
      "         Iterations: 81\n",
      "         Function evaluations: 185\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 18-Mar-2014   18-Jul-2014    0   5.330  18.581   6.097  -->  0.006\n",
      " 18-Mar-2014   18-Jul-2014  150   3.838  18.503   5.161  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002652\n",
      "         Iterations: 144\n",
      "         Function evaluations: 300\n",
      " 18-Mar-2014   18-Jul-2014  300   3.191  18.288   4.852  -->  0.003\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 19-Mar-2014   18-Jul-2014    0   3.191  18.288   4.852  -->  0.005\n",
      " 19-Mar-2014   18-Jul-2014  150   3.136  18.084   4.968  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.003397\n",
      "         Iterations: 67\n",
      "         Function evaluations: 169\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 20-Mar-2014   18-Jul-2014    0   3.136  18.084   4.968  -->  0.010\n",
      " 20-Mar-2014   18-Jul-2014  150   2.936  18.441   4.849  -->  0.002\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002263\n",
      "         Iterations: 128\n",
      "         Function evaluations: 267\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 21-Mar-2014   18-Jul-2014    0   2.928  18.450   4.842  -->  0.044\n",
      " 21-Mar-2014   18-Jul-2014  150   2.935  19.134   4.876  -->  0.004\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.003655\n",
      "         Iterations: 64\n",
      "         Function evaluations: 158\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 24-Mar-2014   18-Jul-2014    0   2.935  19.134   4.876  -->  0.021\n",
      " 24-Mar-2014   18-Jul-2014  150   5.169  18.555   6.217  -->  0.004\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.004381\n",
      "         Iterations: 138\n",
      "         Function evaluations: 297\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 25-Mar-2014   18-Jul-2014    0   5.653  18.592   6.449  -->  0.014\n",
      " 25-Mar-2014   18-Jul-2014  150   6.252  18.525   6.554  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002918\n",
      "         Iterations: 107\n",
      "         Function evaluations: 231\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 26-Mar-2014   18-Jul-2014    0   6.251  18.525   6.553  -->  0.014\n",
      " 26-Mar-2014   18-Jul-2014  150   5.189  18.301   6.063  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002839\n",
      "         Iterations: 107\n",
      "         Function evaluations: 243\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 27-Mar-2014   18-Jul-2014    0   5.189  18.301   6.063  -->  0.006\n",
      " 27-Mar-2014   18-Jul-2014  150   5.789  18.693   6.112  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002992\n",
      "         Iterations: 112\n",
      "         Function evaluations: 248\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 28-Mar-2014   18-Jul-2014    0   5.788  18.693   6.111  -->  0.003\n",
      " 28-Mar-2014   18-Jul-2014  150   5.684  18.828   5.974  -->  0.003\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.002811\n",
      "         Iterations: 97\n",
      "         Function evaluations: 216\n",
      "pricing_date maturity_date    i   kappa   theta    vola  -->    MSE\n",
      " 31-Mar-2014   18-Jul-2014    0   5.683  18.828   5.974  -->  0.009\n",
      " 31-Mar-2014   18-Jul-2014  150  12.121  18.656   8.053  -->  0.004\n",
      " 31-Mar-2014   18-Jul-2014  300  15.247  18.578   8.978  -->  0.004\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "CPU times: user 1min 6s, sys: 278 ms, total: 1min 6s\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pricing_date_list = pd.date_range('2014/3/1', '2014/3/31', freq='B')\n",
    "maturity_list = [third_fridays[7]]\n",
    "parameters = get_parameter_series(pricing_date_list, maturity_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are now stored in the pandas `DataFrame` called `parameters`. We set a new index and inspect the last results. Throughout the MSE is pretty low indicated a good fit of the model to the market quotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>MSE</th>\n",
       "      <th>initial_value</th>\n",
       "      <th>kappa</th>\n",
       "      <th>maturity</th>\n",
       "      <th>sigma</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pricing_date</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>2014-03-25</th>\n",
       "      <td>0.002918</td>\n",
       "      <td>18.2637</td>\n",
       "      <td>6.250875</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>6.553352</td>\n",
       "      <td>18.525022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-26</th>\n",
       "      <td>0.002839</td>\n",
       "      <td>17.5869</td>\n",
       "      <td>5.189260</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>6.062754</td>\n",
       "      <td>18.301087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-27</th>\n",
       "      <td>0.002992</td>\n",
       "      <td>17.6397</td>\n",
       "      <td>5.787693</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>6.111093</td>\n",
       "      <td>18.693053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-28</th>\n",
       "      <td>0.002811</td>\n",
       "      <td>17.0324</td>\n",
       "      <td>5.683422</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>5.974289</td>\n",
       "      <td>18.827773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>0.003657</td>\n",
       "      <td>17.6639</td>\n",
       "      <td>15.246458</td>\n",
       "      <td>2014-07-18</td>\n",
       "      <td>8.978325</td>\n",
       "      <td>18.578233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   MSE  initial_value      kappa   maturity     sigma  \\\n",
       "pricing_date                                                            \n",
       "2014-03-25    0.002918        18.2637   6.250875 2014-07-18  6.553352   \n",
       "2014-03-26    0.002839        17.5869   5.189260 2014-07-18  6.062754   \n",
       "2014-03-27    0.002992        17.6397   5.787693 2014-07-18  6.111093   \n",
       "2014-03-28    0.002811        17.0324   5.683422 2014-07-18  5.974289   \n",
       "2014-03-31    0.003657        17.6639  15.246458 2014-07-18  8.978325   \n",
       "\n",
       "                  theta  \n",
       "pricing_date             \n",
       "2014-03-25    18.525022  \n",
       "2014-03-26    18.301087  \n",
       "2014-03-27    18.693053  \n",
       "2014-03-28    18.827773  \n",
       "2014-03-31    18.578233  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paramet = parameters.set_index('pricing_date')\n",
    "paramet.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is also illustrated by the visualization of the time series data for the **calibrated/optimal parameter values**. The **MSE** is below 0.01 throughout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0g8BwIOJ1LbXJ6tUGp5+ezfjxQTp0SPDeeyUKzSKyWe/ecV55JUIwCJdcksWr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GAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f139b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "paramet[['kappa', 'theta', 'sigma', 'MSE']].plot(subplots=True, color='b', figsize=(10, 12))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following generates a plot of the calibration results for the **last calibration day**. The absolute price differences are below **0.10 EUR** for all options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116ec0b00>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/AlxP4HZrbTPwdT8dU0ROQWpiFJdP6MElo/P5qHgHMxdX8NGaHXy0ZgeFOUlM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f0e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = paramet.index[-1]\n",
    "opt = np.array(paramet[['kappa', 'theta', 'sigma']].loc[index])\n",
    "option_selection = get_option_selection(index, maturity_list[0], tol=tol)[0]\n",
    "model_values = np.sort(np.array(list(calculate_model_values(opt).values())))[::-1]\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "fix, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(10, 8))\n",
    "strikes = option_selection['STRIKE'].values\n",
    "ax1.plot(strikes, option_selection['PRICE'], label='market quotes')\n",
    "ax1.plot(strikes, model_values, 'ro', label='model values')\n",
    "ax1.set_ylabel('option values')\n",
    "ax1.grid(True)\n",
    "ax1.legend(loc=0)\n",
    "wi = 0.25\n",
    "ax2.bar(strikes - wi / 2., model_values - option_selection['PRICE'],\n",
    "        label='market quotes', width=wi)\n",
    "ax2.grid(True)\n",
    "ax2.set_ylabel('differences')\n",
    "ax2.set_xlabel('strikes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Copyright, License & Disclaimer**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "DX Analytics (the \"dx library\") is licensed under the GNU Affero General Public License\n",
    "version 3 or later (see [http://www.gnu.org/licenses/](http://www.gnu.org/licenses/)).\n",
    "\n",
    "DX Analytics comes with no representations\n",
    "or warranties, to the extent permitted by applicable law.\n",
    "\n",
    "\n",
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "[http://tpq.io](http://tpq.io) | [team@tpq.io](mailto:team@tpq.io) | [http://twitter.com/dyjh](http://twitter.com/dyjh)\n",
    "\n",
    "**Quant Platform** |\n",
    "[http://quant-platform.com](http://quant-platform.com)\n",
    "\n",
    "**Derivatives Analytics with Python (Wiley Finance)** |\n",
    "[http://derivatives-analytics-with-python.com](http://derivatives-analytics-with-python.com)\n",
    "\n",
    "**Python for Finance (O'Reilly)** |\n",
    "[http://python-for-finance.com](http://python-for-finance.com)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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