VIX_modelization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CBOE Volatility Index (VIX)\n",
    "This Jupyter Notebook is an application of the information provided in the CBOE white paper, which you can browse [here](https://www.cboe.com/micro/vix/vixwhite.pdf).\n",
    "<br><br>Introduced in 1993, the VIX index was originally designed to measure the market's expectation of 30-day volatility, implied by at-the-money S&P 500 index option prices. It was updated in 2003 to better reflect expected volatility. The new VIX estimates expected volatility by aggregating the weighted prices of SPX (S&P 500) puts and calls over a wide range of strike prices. It was further expanded in 2014 to include series of SPX weeklys.\n",
    "<br>The VIX is a premier benchmark for US stock market volatility, being regularly featured in US news, newspaper, cable, etc.\n",
    "<br>\n",
    "<br>The VIX index is tradable through futures on the CBOE Futures Exchange (CFE), i.e. volatility is a tradable asset. The goal is to diversify and hedge one's portfolio thanks to the usual negative correlation of volatility to stock market returns.\n",
    "### Terms\n",
    "<b>At-the-money</b>: Situation where an option's strike price (K) is identical to the price of the underlying security (S).\n",
    "<br><b>Bid, ask, and bid-ask spread</b>: The bid price represents the maximum price that a buyer is willing to pay for a security. The ask price represents the minimum price that a seller is willing to receive. A bid-ask spread is the amount by which the ask price exceeds the bid price for an asset in the market.\n",
    "<br><b>Out-of-the-money</b>: An out of the money option has no intrinsic value, but only possesses extrinsic or time value. Being out of the money doesn't mean a trader can't make a profit on that option.\n",
    "<br><b>Weeklys</b>: Weekly options available on many indexes, equities, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import re\n",
    "import requests\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "from scipy import interpolate\n",
    "\n",
    "from yahoo_fin import options as op, stock_info as si\n",
    "# get_expiration_dates() does not work in Jupyter Notebook\n",
    "#chain = op.get_expiration_dates(\"^SPX\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of a call option pricing using Black Scholes\n",
    "To give you an example of how an option's price can be estimated, find below the modelization of a call option using the common and historic Black Scholes formula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the European call option: 8.019103.\n",
      "Value of the European call option: 7.551576.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def black_scholes_modelization(stock_pr, strike_pr, maturity, rf_rate, stock_volat, nb_simul, graph = False):\n",
    "    \"\"\"\n",
    "    Modelizes the Black Scholes formula of option pricing based on the common and historic formula\n",
    "    :param <stock_pr>: Float ; Underlying index/stock price at valuation\n",
    "    :param <strike_pr>: Float ; Strike price of option\n",
    "    :param <maturity>: Float ; Time to maturity in years\n",
    "    :param <rf_rate>: Float ; Risk-free rate\n",
    "    :param <stock_volat>: Float ; Underlying index/stock volatility\n",
    "    :param <nb_simul>: Integer ; Number of simulations\n",
    "    :param <graph>: Boolean ; Boolean to show a graph or not\n",
    "    \"\"\"\n",
    "    S0 = stock_pr\n",
    "    K = strike_pr\n",
    "    T = maturity\n",
    "    r = rf_rate\n",
    "    sigma = stock_volat\n",
    "    I = nb_simul\n",
    "    \n",
    "    np.random.seed(1000)\n",
    "    z = np.random.standard_normal(I)\n",
    "    ST = S0 * np.exp((r-sigma**2/2)*T + sigma * math.sqrt(T)* z) #simulation of index/stock price at maturity\n",
    "    hT = np.maximum(ST - K, 0) #pay-off at maturity\n",
    "    \n",
    "    C0 = math.exp(-r * T) * np.mean(hT) #Monte Carlo estimator\n",
    "    print(\"Value of the European call option: {:5.6f}.\".format(C0))\n",
    "    \n",
    "    if graph == True:\n",
    "        plt.figure(figsize=(20,10))\n",
    "        plt.plot(hT)\n",
    "        plt.plot(ST)\n",
    "        plt.ylabel('$ value')\n",
    "        plt.show()\n",
    "\n",
    "# EXAMPLES\n",
    "black_scholes_modelization(100., 105., 1.0, 0.05, 0.2, 100000)\n",
    "black_scholes_modelization(100., 105., 1.0, 0.05, 0.2, 400, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The VIX Index Calculation\n",
    "The generalized formula used in the FIX index calculation is:\n",
    "\n",
    "$$\\sigma^2 = \\frac{2}{T}*\\sum_{i}\\frac{\\Delta*K_i*e^{R*T}*Q(K_i)}{K_i^2}-\\frac{1}{T}*[\\frac{F}{K_0}-1]^2$$\n",
    "\n",
    "where:\n",
    "\n",
    "$VIX = \\sigma * 100$\n",
    "\n",
    "$T = \\text{time to expiration}$\n",
    "\n",
    "$F = \\text{Forward index level derived from index options prices}$\n",
    "\n",
    "$K_0 = \\text{First strike below the forward index level, F}$\n",
    "\n",
    "$K_i = \\text{Strike price of } i^\\text{th} \\text{ out-of-the-money option; a call if } K_i > K_0 \\text{ and a put if } K_i < K_0 \\text{ ; both put and call if } K_i = K_0$\n",
    "\n",
    "$\\Delta*K_i = \\text{Interval between strike prices - half the difference between the strike on either side of } K_i$\n",
    "\n",
    "$$\\Delta*K_i = \\frac{K_{i+1} - K_{i-1}}{2}$$\n",
    "\n",
    "$R = \\text{Risk-free interest rate to expiration}$\n",
    "\n",
    "$Q(K_i) = \\text{The midpoint of the bid-ask spread for each option with strike }K_i$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling T\n",
    "The VIX measures 30-day expected volatility of the S&P 500 Index. <b>The components of the VIX are near- and next-term put and call options with more than 23 days and less than 37 days to expiration</b>. These include SPX options with \"standard\" 3rd Friday expiration dates and \"weekly\" SPX options that expire every Friday, except the 3rd Friday of each month.\n",
    "<br>Once each week, the SPX options used to calculate the VIX Index \"roll\" to new contract maturities (shortest: 24 - 30 day expirations ; latest: 30 - 37 day expirations).\n",
    "\n",
    "$$T = \\frac{M_\\text{Current day} + M_\\text{Settlement day} + M_\\text{Other days}}{\\text{Minutes in a year}}$$\n",
    "\n",
    "where:\n",
    "\n",
    "$M_\\text{Current day} = \\text{minutes remaining to midnight of the current day}$\n",
    "\n",
    "$\\text{Either: }M_\\text{Settlement day} = \\text{minutes from midnight until 08:30am (EST) for \"standard\" SPX expirations}$\n",
    "\n",
    "$\\text{Or: }M_\\text{Settlement day} = \\text{minutes from midnight until 04:00pm (EST) for \"weekly\" SPX expirations}$\n",
    "\n",
    "$M_\\text{Other days} = \\text{Total minutes in the days between current day and expiration day}$\n",
    "\n",
    "$\\text{Minutes in a year} = 525600$\n",
    "\n",
    "<b>Two T values are calculated</b>: $T_1$ for the near-term options, $T_2$ for the next-term options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_to_expiration(term = True):\n",
    "    \"\"\"\n",
    "    Calculates the time to expiration.\n",
    "    :param <term>: boolean ; True == \"Near-term\", False == \"Next-term\"\n",
    "    \"\"\"\n",
    "    #current time + tomorrow\n",
    "    now = datetime.datetime.now()\n",
    "    day = datetime.date.today()\n",
    "    tomorrow = datetime.datetime(now.year, now.month, now.day) + datetime.timedelta(1)\n",
    "    \n",
    "    #near- or next-term\n",
    "    if term == True:\n",
    "        val = 0\n",
    "    else:\n",
    "        val = 7\n",
    "    \n",
    "    #calculation of minutes remaining until midnight of the current day\n",
    "    minutes_to_midnight_today = int(round(abs(tomorrow - now).seconds / 60,0))\n",
    "    \n",
    "    #calculation of total minutes in the days between current day and expiration day\n",
    "    for index in range(24,38):\n",
    "        day = datetime.datetime(now.year, now.month, now.day) + datetime.timedelta(index)\n",
    "        if day.weekday() == 4:\n",
    "            days_to_expiration = index + val - 1\n",
    "            if day.weekday() == 4 and 15 <= day.day <= 21:\n",
    "                minutes_to_settlement = 510\n",
    "            else:\n",
    "                minutes_to_settlement = 900\n",
    "            break\n",
    "    \n",
    "    return (minutes_to_midnight_today + minutes_to_settlement + (days_to_expiration) * 24 * 60)/525600"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling R\n",
    "To go with $T_1$ and $T_2$, two risk-free interest rates are also calculated. $R_1$ and $R_2$ are yields based on the US Treasury yield curve rates, also commonly referred to as \"Constant Maturity Treasury\" rates or CMTs.\n",
    "\n",
    "<b>Fitting a yield curve using cubic spline</b>:\n",
    "<br>The goal is to fit a cubic polynomial to the existing yields and maturities of the US Treasury bonds, which together form the \"yield curve\"--it is actually a discrete list of points in a graph. The goal is to solve a cubic polynomial by minimizing the sum of squared residuals.\n",
    "$$\\hat{r}(t) = \\beta_0 + \\beta_1 * t + \\beta_2 * t^2 + \\beta_3 * t^3$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def us_treasury_yield_curve():\n",
    "    \"\"\"\n",
    "    Retrieves and formats the us treasury yield curve.\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    response = requests.get(\"https://www.treasury.gov/resource-center/data-chart-center/interest-rates/pages/XmlView.aspx?data=yield\")\n",
    "    root = ET.fromstring(response.content)\n",
    "\n",
    "    temp_us_treasury_dict={}\n",
    "    us_treasury_dict={}\n",
    "    \n",
    "    for elt in root.iter():\n",
    "        temp_us_treasury_dict[elt.tag[-8:]] = elt.text\n",
    "    \n",
    "    for key in temp_us_treasury_dict.keys():\n",
    "        if (key.find(\"MONTH\") + key.find(\"YEAR\") + key.find(\"DATE\") != -3):\n",
    "            us_treasury_dict[re.sub(r'.*_', '', key)] = temp_us_treasury_dict[key]\n",
    "    \n",
    "    return us_treasury_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yield_curve_minute_conversion(us_treasury_yields):\n",
    "    \"\"\"\n",
    "    Formats a series of US Treasury yields in minute to expiration\n",
    "    :param <us_treasury_yields>: dictionary ; contains the list of yield curves of the US treasury bonds\n",
    "    \"\"\"\n",
    "    # Based on the average number of day in a month: 30.42\n",
    "    minutes_in_a_month = 43804\n",
    "    minutes_in_a_year = 525600\n",
    "    \n",
    "    #minute axis\n",
    "    x = []\n",
    "    \n",
    "    #yield axis\n",
    "    y = []\n",
    "    \n",
    "    #yield_curve_in_minutes = {}\n",
    "    for key in us_treasury_yields.keys():\n",
    "        if key.find(\"MONTH\") != -1:\n",
    "            x.append(int(key[:-5])*minutes_in_a_month)\n",
    "            y.append(us_treasury_yields[key])\n",
    "        elif key.find(\"YEAR\") != -1:\n",
    "            x.append(int(key[:-4])*minutes_in_a_year)\n",
    "            y.append(us_treasury_yields[key])\n",
    "    \n",
    "    return [x,y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cubic_spline_risk_free_rate_function(time_to_expiration, minute_data = False):\n",
    "    \"\"\"\n",
    "    Estimates the risk free rate (based on the US treasury yield) at a specific time to expiration\n",
    "    :param <time_to_expiration>: integer ; time to expiration in day or minutes \n",
    "    :param <minute_data>: boolean ; indicates if the argument <time_to_expiration> is in minutes or days\n",
    "    \"\"\"\n",
    "    yield_curve = yield_curve_minute_conversion(us_treasury_yield_curve())\n",
    "    x_points = yield_curve[0]\n",
    "    y_points = yield_curve[1]\n",
    "    tck = interpolate.splrep(x_points, y_points)\n",
    "    \n",
    "    if minute_data == False:\n",
    "        #current time + tomorrow\n",
    "        now = datetime.datetime.now()\n",
    "        tomorrow = datetime.datetime(now.year, now.month, now.day) + datetime.timedelta(1) \n",
    "        \n",
    "        #calculation of total minutes in the days between current day and expiration day\n",
    "        for index in range(24,38):\n",
    "            day = datetime.datetime(now.year, now.month, now.day) + datetime.timedelta(index)\n",
    "            if day.weekday() == 4:\n",
    "                days_to_expiration = index - 1\n",
    "                if day.weekday() == 4 and 15 <= day.day <= 21:\n",
    "                    minutes_to_settlement = 510\n",
    "                else:\n",
    "                    minutes_to_settlement = 900\n",
    "                break\n",
    "        \n",
    "        #calculation of minutes remaining until midnight of the current day\n",
    "        minutes_to_midnight_today = int(round(abs(tomorrow - now).seconds / 60,0))\n",
    "        time_to_expiration = (time_to_expiration-1) * 24 * 60 + minutes_to_settlement + minutes_to_midnight_today        \n",
    "        \n",
    "        return interpolate.splev(time_to_expiration, tck)\n",
    "    \n",
    "    else:\n",
    "        return interpolate.splev(time_to_expiration, tck)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated risk free rates at expiration date can be estimated as such:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = [time_to_expiration(True),time_to_expiration(True)]\n",
    "\n",
    "estimated_risk_free_rate = []\n",
    "\n",
    "for date in dates:\n",
    "    estimated_risk_free_rate.append(float(cubic_spline_risk_free_rate_function(date*525600, True)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting the options to be used in the VIX Index Calculation\n",
    "The selected options are out-of-the-money SPX calls and out-of-the-money SPX puts centered around an at-the-money strike price $K_0$. Volatility rises and falls constantly. It implies that the number of options used in the VIX Index calculation may vary from month-to-month, day-to-day, and even minute-to-minute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Extracting option data and working on them</b>\n",
    "<br>The goal is to find the \"near-term\" and \"next-term\" option strike prices for which, the absolute difference between the call and put prices is the smallest. Those two values will be used to compute a \"forward\" level, denoted $F$ such as:\n",
    "\n",
    "$$F = \\text{Strike Price} + e^{R * T} * (\\text{Call Price} - \\text{Put Price})$$\n",
    "\n",
    "Settlement dates for S&P options occur every two days during the workweek (Monday, Wednesday, Friday). We are looking to extract the option data for the \"Near-term\" and \"next-term\" Fridays.\n",
    "<br><br><b>Known issue of yahoo_fin in Jupyter Notebook</b>: the method \"get_expiration_date()\" does not work due to a Runtime error. Jupyter being an event loop, the method throws the following error: \"Cannot use HTMLSession within an existing event loop. Use AsyncHTMLSession instead.\" To keep this notebook self-contained, we will have to rework the expiration date of each option from their name in the option chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_snp_price():\n",
    "    \"\"\"\n",
    "    Returns the live price of the S&P index.\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    return si.get_live_price(\"^GSPC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def day_of_expiration():\n",
    "    \"\"\"\n",
    "    Determines the date of near-term and next-term settlement Fridays.\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    now = datetime.datetime.now()\n",
    "    \n",
    "    for index in range(24,38):\n",
    "        day = datetime.datetime(now.year, now.month, now.day) + datetime.timedelta(index)\n",
    "        if day.weekday() == 4:\n",
    "            near_term = datetime.datetime(now.year, now.month, now.day) + datetime.timedelta(index)\n",
    "            next_term = datetime.datetime(now.year, now.month, now.day) + datetime.timedelta(index+7)\n",
    "            break\n",
    "    \n",
    "    return [near_term, next_term]       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def option_chain_v0():\n",
    "    \"\"\"\n",
    "    Return the out of the money SPX calls and puts last price on the market.\n",
    "    [[{out of the money near-term calls last price}, {out of the money next-term calls last price}], \n",
    "    [{out of the money near-term puts last price},{out of the money next-term puts last price}]]\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    dates = day_of_expiration()\n",
    "    snp_price = get_snp_price()\n",
    "    call_list = [{},{}]\n",
    "    put_list = [{},{}]\n",
    "    for idx1, date in enumerate(dates):\n",
    "        snp_option_chain = op.get_options_chain(\"^SPX\",str(date.strftime(\"%x\")))\n",
    "        for idx2, item in enumerate(snp_option_chain[\"calls\"][\"Strike\"]):\n",
    "            if item > snp_price:\n",
    "                call_list[idx1][item] = snp_option_chain[\"calls\"][\"Last Price\"][idx2]\n",
    "                \n",
    "        for idx3, item in enumerate(snp_option_chain[\"puts\"][\"Strike\"]):\n",
    "            if item < snp_price:\n",
    "                put_list[idx1][item] = snp_option_chain[\"puts\"][\"Last Price\"][idx3]\n",
    "    return [call_list, put_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def option_chain():\n",
    "    \"\"\"\n",
    "    Return the out of the money SPX calls and puts last price on the market.\n",
    "    [[{out of the money near-term calls last price}, {out of the money next-term calls last price}], \n",
    "    [{out of the money near-term puts last price},{out of the money next-term puts last price}]]\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    dates = day_of_expiration()\n",
    "    snp_price = get_snp_price()\n",
    "    call_list = [{},{}]\n",
    "    put_list = [{},{}]\n",
    "    for idx1, date in enumerate(dates):\n",
    "        snp_option_chain = op.get_options_chain(\"^SPX\",str(date.strftime(\"%x\")))\n",
    "        for idx2, item in enumerate(snp_option_chain[\"calls\"][\"Bid\"]):\n",
    "            if item != 0:\n",
    "                call_list[idx1][snp_option_chain[\"calls\"][\"Strike\"][idx2]] = snp_option_chain[\"calls\"][\"Last Price\"][idx2]\n",
    "                \n",
    "        for idx3, item in enumerate(snp_option_chain[\"puts\"][\"Bid\"]):\n",
    "            if item != 0:\n",
    "                put_list[idx1][snp_option_chain[\"puts\"][\"Strike\"][idx3]] = snp_option_chain[\"puts\"][\"Last Price\"][idx3]\n",
    "    return [call_list, put_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_puts_calls_dict(chain):\n",
    "    \"\"\"\n",
    "    Merges out of the money puts and calls by last price and near- or next-term.\n",
    "    :param <chain>: data structure ; list of lists of dictionaries that contains out of the money, \n",
    "        near- and next-term puts and calls last prices\n",
    "    \"\"\"\n",
    "    \n",
    "    dates = day_of_expiration()\n",
    "    result = []\n",
    "    chain = option_chain()\n",
    "    \n",
    "    for index, date in enumerate(dates):\n",
    "        d = {}\n",
    "        keys = []\n",
    "        \n",
    "        for key in set(list(chain[0][index].keys()) + list(chain[1][index].keys())):\n",
    "            try:\n",
    "                d.setdefault(key,[]).append(chain[index][0][key])        \n",
    "            except KeyError:\n",
    "                pass\n",
    "            try:\n",
    "                d.setdefault(key,[]).append(chain[index][1][key])          \n",
    "            except KeyError:\n",
    "                pass\n",
    "        \n",
    "        for key in d.keys():\n",
    "            if len(d[key]) != 2: keys.append(key)\n",
    "        \n",
    "        for key in keys:\n",
    "            del d[key]\n",
    "        \n",
    "        result.append(d)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def call_put_difference_for_single_strike_white_paper_method():\n",
    "    \"\"\"\n",
    "    Provide the strike price and the associated difference between Call and Put prices, for which it is the smallest.\n",
    "    This version uses the method explicited in the CBOE white paper.\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    chain = merge_puts_calls_dict(option_chain())\n",
    "    difference = []\n",
    "    for term in range(2):\n",
    "        diff = -1.\n",
    "        strike= 0.\n",
    "        for key in chain[term].keys():\n",
    "            if (diff == -1 or diff > abs(chain[term][key][0] - chain[term][key][1])) and abs(chain[term][key][0] -\n",
    "                                                                                             chain[term][key][1]) != 0:\n",
    "                diff = abs(chain[term][key][0] - chain[term][key][1])\n",
    "                strike = key\n",
    "        difference.append([strike, diff])\n",
    "    return difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_F():\n",
    "    \"\"\"\n",
    "    Provide the forward index prices for the near- and next-term options\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    dates = [time_to_expiration(True),time_to_expiration(False)]\n",
    "    estimate = call_put_difference_for_single_strike_white_paper_method()\n",
    "    F = []\n",
    "    for i in range(2):\n",
    "        F.append(estimate[i][0] + math.exp(estimated_risk_free_rate[i]*dates[i]) * estimate[i][1])\n",
    "    return F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Determining $K_0$ from the forward index level $F$</b>:\n",
    "<br>From $F$ we can determine $K_{0,1}$ and $K_{0,2}$ (near-term and next-term), the strike price immediately below the forward index level. It is rounded to the closest multiple of 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strike_price_under_F():\n",
    "    \"\"\"\n",
    "    Returns the strike price immediately below the forward index level F for the near-and next-term options.\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    dates = day_of_expiration()\n",
    "    F = find_F()\n",
    "    K = []\n",
    "    for counter, date in enumerate(dates):\n",
    "        snp_option_chain = op.get_options_chain(\"^SPX\",str(date.strftime(\"%x\")))\n",
    "        strikes = sorted(list(snp_option_chain[\"puts\"][\"Strike\"])+list(snp_option_chain[\"calls\"][\"Strike\"]))\n",
    "        for item in range(1, len(strikes)):\n",
    "            if strikes[item] > F[counter]:\n",
    "                K.append(strikes[item-1])\n",
    "                break\n",
    "        if len(K) < counter+1: K.append(round(F[counter]-5))\n",
    "    return K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "F_values = find_F()\n",
    "K = strike_price_under_F()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Selection of the options to be used in the VIX index calculation:</b>\n",
    "<br>Select out-of-the-money call options with strike prices > $K_{0}$. Start with the put strike immediately higher than $K_{0}$ and move to successively higher strike prices. Exclude any call option that has a bid price equal to zero (i.e. no bid). Once two calls with consecutive strike prices are found to have zero bid prices, no calls with higher strikes are considered for inclusion.\n",
    "<br><br>Select out-of-the-money put options with strike prices < $K_{0}$. Start with the put strike immediately higher than $K_{0}$ and move to successively lower strike prices. Exclude any put option that has a bid price equal to zero (i.e. no bid). Once two puts with consecutive strike prices are found to have zero bid prices, no puts with lower strikes are considered for inclusion.\n",
    "<br><br>We include the formula below the options value at strike price for ease of use for the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We construct a table that contains the options used to calculate the VIX. We select both the put and call retrieved using the function above along with the put and call average at strike price $K_{0}$. The VIX used the average of quoted bid and ask, or mid-quote, prices for each option selection. The $K_{0}$ put and call prices are average to produce a single value.\n",
    "\n",
    "We reproduce this method both for near- and next-term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retained_options_out_of_the_money():\n",
    "    \"\"\"\n",
    "    Retrieves the out-of-the-money option data: strike, bid, ask\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    dates = day_of_expiration()\n",
    "    \n",
    "    call_data = [[[],[],[]],[[],[],[]]]\n",
    "    put_data = [[[],[],[]],[[],[],[]]]\n",
    "    \n",
    "    for date in range(2):\n",
    "        snp_option_chain = op.get_options_chain(\"^SPX\",str(dates[date].strftime(\"%x\")))\n",
    "        counter = 0\n",
    "        for index in range(len(snp_option_chain[\"calls\"][\"Strike\"])):           \n",
    "            if snp_option_chain[\"calls\"][\"Strike\"][index] > K[date]:\n",
    "                if snp_option_chain[\"calls\"][\"Bid\"][index] == 0:\n",
    "                    if counter == 1:\n",
    "                        break\n",
    "                    else:\n",
    "                        counter += 1\n",
    "                else:\n",
    "                    counter = 0\n",
    "                    call_data[date][0].append(snp_option_chain[\"calls\"][\"Strike\"][index])\n",
    "                    call_data[date][1].append(snp_option_chain[\"calls\"][\"Bid\"][index])\n",
    "                    call_data[date][2].append(snp_option_chain[\"calls\"][\"Ask\"][index])\n",
    "        \n",
    "        counter = 0\n",
    "        for index in reversed(range(len(snp_option_chain[\"puts\"][\"Strike\"]))):\n",
    "            if snp_option_chain[\"puts\"][\"Strike\"][index] < K[date]:\n",
    "                if snp_option_chain[\"puts\"][\"Bid\"][index] == 0:\n",
    "                    if counter == 1:\n",
    "                        break\n",
    "                    else:\n",
    "                        counter += 1\n",
    "                else:\n",
    "                    counter = 0\n",
    "                    put_data[date][0].append(snp_option_chain[\"puts\"][\"Strike\"][index])\n",
    "                    put_data[date][1].append(snp_option_chain[\"puts\"][\"Bid\"][index])\n",
    "                    put_data[date][2].append(snp_option_chain[\"puts\"][\"Ask\"][index])\n",
    "        \n",
    "    return [call_data, put_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retained_options_at_the_money():\n",
    "    \"\"\"\n",
    "    Retrieves the at-the-money option data: strike, bid, ask\n",
    "    No arguments.\n",
    "    \"\"\"\n",
    "    dates = day_of_expiration()\n",
    "    \n",
    "    call_data = [[[],[],[]],[[],[],[]]]\n",
    "    put_data = [[[],[],[]],[[],[],[]]]\n",
    "    \n",
    "    for date in range(2):\n",
    "        snp_option_chain = op.get_options_chain(\"^SPX\",str(dates[date].strftime(\"%x\")))\n",
    "        try:\n",
    "            index = list(snp_option_chain[\"calls\"][\"Strike\"]).index(K[date])\n",
    "            call_data[date][0] = snp_option_chain[\"calls\"][\"Strike\"][index]\n",
    "            call_data[date][1] = snp_option_chain[\"calls\"][\"Bid\"][index]\n",
    "            call_data[date][2] = snp_option_chain[\"calls\"][\"Ask\"][index]\n",
    "        except Exception as e:\n",
    "            pass\n",
    "        try:\n",
    "            index = list(snp_option_chain[\"puts\"][\"Strike\"]).index(K[date])\n",
    "            put_data[date][0] = snp_option_chain[\"puts\"][\"Strike\"][index]\n",
    "            put_data[date][1] = snp_option_chain[\"puts\"][\"Bid\"][index]\n",
    "            put_data[date][2] = snp_option_chain[\"puts\"][\"Ask\"][index]\n",
    "        except Exception as e:\n",
    "            pass\n",
    "    \n",
    "    return [call_data, put_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Calculate volatility for both near- and next-term options:</b>\n",
    "<br>We now need to apply the VIX formula to the near-term andnext-term options with time to expiration $T_{1}$ and $T_{2}$:\n",
    "\n",
    "$$\\sigma^2_{1} = \\frac{2}{T_{1}}*\\sum_{i}\\frac{\\Delta*K_i*e^{R_{1}*T_{1}}*Q(K_i)}{K_i^2}-\\frac{1}{T_{1}}*[\\frac{F_{1}}{K_0}-1]^2$$\n",
    "\n",
    "$$\\sigma^2_{2} = \\frac{2}{T_{2}}*\\sum_{i}\\frac{\\Delta*K_i*e^{R_{2}*T_{2}}*Q(K_i)}{K_i^2}-\\frac{1}{T_{2}}*[\\frac{F_{2}}{K_0}-1]^2$$\n",
    "<br>The VIX index is an amalgam of the information reflected in the prices of all of the selected options. The contribution of a single option to the IX value is proportional to $\\Delta*K$ and the price of that option, and inversely proportional to the square of the option's strike price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to determine $\\Delta*K$:\n",
    "<br>Generally, $\\Delta*K_i$ is half the difference between the strike prices on either side of $K_i$. At the upper and lower edges of any given strip of options, $\\Delta*K_i$ is simply the difference between $K_i$ and the adjacent strike price. We speak of a near-term options to the index through the following formula:\n",
    "\n",
    "$$\\frac{\\Delta*K_i*e^{R_{1}*T_{1}}*Q(K_i)}{K_i^2}$$\n",
    "\n",
    "$$\\frac{\\Delta*K_i*e^{R_{2}*T_{2}}*Q(K_i)}{K_i^2}$$\n",
    "<br>A similar calculation is performed for each option. The resulting values for the near-term options are then summed and multiplied by $2/T_1$. Likewise, the resulting values for the next-term options are summed and multiplied by $2/T_2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contribution_to_VIX(retained_data_ootm, retained_data_atm, rate):\n",
    "    \"\"\"\n",
    "    Calculates the contribution to the VIX of each single option strip.\n",
    "    :param <retained_data_ootm>: list ; list of lists containing the out-of-the-money option data \n",
    "        retained to calculate the VIX\n",
    "    :param <retained_data_atm>: list ; list of lists containing the at-the-money option data \n",
    "        retained to calculate the VIX\n",
    "    :param <rate>: float ; estimated risk free rate\n",
    "    \"\"\"\n",
    "    contribution = [[],[]]\n",
    "    dates = day_of_expiration()\n",
    "    expiration = [time_to_expiration(True), time_to_expiration(False)]\n",
    "    \n",
    "    for date in range(2):\n",
    "        \n",
    "        #OUT-OF-THE-MONEY OPTIONS\n",
    "        delta = {}\n",
    "        snp_option_chain = op.get_options_chain(\"^SPX\",str(dates[date].strftime(\"%x\")))\n",
    "        for index, item in enumerate(snp_option_chain[\"calls\"][\"Strike\"]):\n",
    "            if item > K[date]:\n",
    "                if index == 0:\n",
    "                    delta[item] = abs(snp_option_chain[\"calls\"][\"Strike\"][index] - \n",
    "                                      snp_option_chain[\"calls\"][\"Strike\"][index+1])\n",
    "                elif index < len(snp_option_chain[\"calls\"][\"Strike\"]) - 1:\n",
    "                    delta[item] = abs(snp_option_chain[\"calls\"][\"Strike\"][index-1] -\n",
    "                                      snp_option_chain[\"calls\"][\"Strike\"][index+1])/2\n",
    "                else:\n",
    "                    delta[item] = abs(snp_option_chain[\"calls\"][\"Strike\"][index] - \n",
    "                                      snp_option_chain[\"calls\"][\"Strike\"][index-1])\n",
    "        \n",
    "        for index, item in enumerate(snp_option_chain[\"puts\"][\"Strike\"]):\n",
    "            if item < K[date]:\n",
    "                if index == 0:\n",
    "                    delta[item] = abs(snp_option_chain[\"puts\"][\"Strike\"][index] - \n",
    "                                      snp_option_chain[\"puts\"][\"Strike\"][index+1])\n",
    "                elif index < len(snp_option_chain[\"puts\"][\"Strike\"]) - 1:\n",
    "                    delta[item] = abs(snp_option_chain[\"puts\"][\"Strike\"][index-1] -\n",
    "                                      snp_option_chain[\"puts\"][\"Strike\"][index+1])/2\n",
    "                else:\n",
    "                    delta[item] = abs(snp_option_chain[\"puts\"][\"Strike\"][index] - \n",
    "                                      snp_option_chain[\"puts\"][\"Strike\"][index-1])\n",
    "        for index in range(len(retained_data_ootm[0][date][0])):\n",
    "            try:\n",
    "                contrib = (2/expiration[date])*delta[retained_data_ootm[0][date][0][index]]/(K[date]**2)*\\\n",
    "                math.exp(rate[date]/100*expiration[date])*\\\n",
    "                abs(float(retained_data_ootm[0][date][1][index])-float(retained_data_ootm[0][date][2][index]))\n",
    "                contribution[date].append(contrib)\n",
    "            except Exception as e: \n",
    "                print(e)\n",
    "        for index in range(len(retained_data_ootm[1][date][0])):\n",
    "            try:\n",
    "                contrib = (2/expiration[date])*delta[retained_data_ootm[1][date][0][index]]/(K[date]**2)*\\\n",
    "                math.exp(rate[date]/100*expiration[date])*\\\n",
    "                abs(float(retained_data_ootm[1][date][1][index])-float(retained_data_ootm[1][date][2][index]))\n",
    "                contribution[date].append(contrib)\n",
    "            except Exception as e: \n",
    "                print(e)\n",
    "        #AT-THE-MONEY OPTIONS\n",
    "        if isinstance(retained_data_atm[0][date][0],np.float64) == False:\n",
    "            if isinstance(retained_data_atm[1][date][0],np.float64):\n",
    "                try:\n",
    "                    contrib = (2/expiration[date])*abs(delta[retained_data_ootm[1][date][0][index]])/(K[date]**2)*\\\n",
    "                            math.exp(rate[date]/100*expiration[date])*\\\n",
    "                            abs(float(retained_data_atm[1][date][1])-float(retained_data_atm[1][date][2]))\n",
    "                    contribution[date].append(contrib)\n",
    "                except Exception as e: \n",
    "                    print(e)\n",
    "        else:\n",
    "            if isinstance(retained_data_atm[1][date][0],np.float64) == False:\n",
    "                try:\n",
    "                    contrib = (2/expiration[date])*abs(delta[retained_data_ootm[1][date][0][index]])/(K[date]**2)*\\\n",
    "                    math.exp(rate[date]/100*expiration[date])*\\\n",
    "                    abs(float(retained_data_atm[0][date][1])-float(retained_data_atm[0][date][2]))\n",
    "                    contribution[date].append(contrib)\n",
    "                except Exception as e: \n",
    "                    print(e)\n",
    "            else:\n",
    "                try:\n",
    "                    contrib = (2/expiration[date])*abs(delta[retained_data_ootm[1][date][0][index]])/(K[date]**2)*\\\n",
    "                            math.exp(rate[date]/100*expiration[date])*\\\n",
    "                            (abs(float(retained_data_atm[0][date][1])-float(retained_data_atm[0][date][2]))+\\\n",
    "                             abs(float(retained_data_atm[1][date][1])-float(retained_data_atm[1][date][2])))/2\n",
    "                    contribution[date].append(contrib)\n",
    "                except Exception as e: \n",
    "                    print(e)\n",
    "    return contribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rates = estimated_risk_free_rate\n",
    "retained_options_ootm = retained_options_out_of_the_money()\n",
    "retained_options_atm = retained_options_at_the_money()\n",
    "\n",
    "near_term_sum_of_contribution = sum(contribution_to_VIX(retained_options_ootm, retained_options_atm, rates)[0])\n",
    "next_term_sum_of_contribution = sum(contribution_to_VIX(retained_options_ootm, retained_options_atm, rates)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we calculate:\n",
    "$$\\frac{1}{T_{1}}*[\\frac{F_{1}}{K_0}-1]^2$$\n",
    "and\n",
    "$$\\frac{1}{T_{2}}*[\\frac{F_{2}}{K_0}-1]^2$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "expiration = [time_to_expiration(True), time_to_expiration(False)]\n",
    "\n",
    "near_term_time_component = 1 / expiration[0] * (F_values[0] / K[0] - 1)**2\n",
    "next_term_time_component = 1 / expiration[1] * (F_values[1] / K[1] - 1)**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can calculate $\\sigma^2_{1}$ and $\\sigma^2_{2}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "near_term_sigma_squared = near_term_sum_of_contribution - near_term_time_component\n",
    "next_term_sigma_squared = next_term_sum_of_contribution - next_term_time_component"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the VIX\n",
    "We calculate the 30-day weighted average of $\\sigma^2_{1}$ and $\\sigma^2_{2}$, then take the square root of the value and multiply by 100 to get the VIX value:\n",
    "\n",
    "$$VIX = 100 * \\sqrt{(T_1 * \\sigma^2_{1}*\\frac{N_{T_2}-N_{30}}{N_{T_2}-N_{T_1}} + T_1 * \\sigma^2_{2}*\\frac{N_{30}-N_{T_1}}{N_{T_2}-N_{T_1}}) * \\frac{N_{365}}{N_{30}}}$$\n",
    "<br> where:\n",
    "\n",
    "$N_{T_1}$ = number of minutes to settlement of the near-term options\n",
    "\n",
    "$N_{T_2}$ = number of minutes to settlement of the next-term options\n",
    "\n",
    "$N_{30}$ = number of minutes in 30 days (i.e. 43,200)\n",
    "\n",
    "$N_{365}$ = number of minutes in a 365-day year (i.e. 525,600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "near_term_minutes = time_to_expiration(True) * 525600\n",
    "next_term_minutes = time_to_expiration(False) * 525600\n",
    "month_minutes = 43200\n",
    "year_minutes = 525600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The VIX index is estimated at: 11.04.\n"
     ]
    }
   ],
   "source": [
    "VIX = round(100 * math.sqrt((time_to_expiration(True) * near_term_sigma_squared * \n",
    "                      ((next_term_minutes - month_minutes)/(next_term_minutes - near_term_minutes)) + \n",
    "                     time_to_expiration(False) * next_term_sigma_squared * \n",
    "                      ((month_minutes - near_term_minutes)/(next_term_minutes - near_term_minutes))) * \n",
    "                     (year_minutes / month_minutes)),2)\n",
    "print(f\"The VIX index is estimated at: {VIX}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "As at July 25th, 2019, the calculated VIX is of <b>11.04</b>, compared to a market-provided 12.4.\n",
    "\n",
    "The discrepancy can be explained by the incompleteness of the data available through the Yahoo finance module. It does not always provide puts and calls with the same strike price. This makes hard to calculate an actual VIX as the first step of the calculation (determining the forward SPX level F for the near- and next-term expirations) requires calls and puts of similar strike price.\n",
    "This forces us to exclude otherwise valuable option data, which renders the calculation incomplete. The approximation remains nonetheless valuable for understanding how the index is built."
   ]
  }
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