options_pricing_binomial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Options Pricing with Binomial Model\n",
    "\n",
    "The objective is to try to value index options (european) and stock options (american) on the Swedish stock exchange Nasaq OMXS. The binomial model is relativley simple, however, care has to be taken when dealing with dividend paying underlyings. In this notebook, only the stock on SAAB B has a dividend. The index option on OMXS30 has no dividend yield during the pricing period, due to Swedish stocks primarly paying dividends in Q1. \n",
    "\n",
    "The methodology of building a recombinding tree can be made extremely efficiently with the right data structures. This is not done here.\n",
    "\n",
    "As with Black-Sholes a model for the underlying has to be specified. In this case the underlying follows a (discrete) stochastic process called CRR (Cox Ross Rubenstein) in which\n",
    "$$\n",
    "S_t = S_0 \\prod_{i=1}^t \\xi_i \\quad \\xi_i \\overset{\\text{iid}}{\\sim}\\text{Be}(q) \\quad \\forall i \\in \\{1,\\ldots, t\\}.\n",
    "$$\n",
    "In this model, the random variable $\\xi_i$ can take on values\n",
    "$$\n",
    "\\xi_i=\\left\\{\n",
    "\\begin{array}{ll}\n",
    "u &\\text{with probability } p, \\\\ \n",
    "d &\\text{with probability } q.\n",
    "\\end{array} \n",
    "\\right.\n",
    "$$\n",
    "In this way the stock can move up or down with the multiplicative factor of $u$ and $d$ respectively. In the CRR model, the $u$ and $d$ are parameterized as $u = \\exp{\\sigma\\sqrt{\\Delta t}}$ and $d=1/u$.\n",
    "\n",
    "The risk neutral probability can be computed as \n",
    "$$\n",
    "    q = \\frac{e^{r \\Delta t - d}}{u-d},\n",
    "$$\n",
    "for underlyings without dividend yields and \n",
    "$$\n",
    "q = \\frac{e^{(r-q)\\Delta t - d}}{u-d},\n",
    "$$\n",
    "for underlyings with dividend yields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "sns.set_theme()\n",
    "sns.set_style(\"whitegrid\", {'grid.linestyle': '--'})\n",
    "seq_col_brew = sns.color_palette(\"flag_r\", 4)\n",
    "sns.set_palette(seq_col_brew)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valuing Index Call Options\n",
    "\n",
    "Some data for the OMXS30 index call options provided by Reuters.\n",
    "\n",
    "* Options Price (bid/ask): 55.50/59.50\n",
    "* Option Vol (bid/ask):    22.85/24.61\n",
    "* OMXS30 Points (close): 1965.29 \n",
    "* OIS discount factor: 0.9984 (1M) 24 days\n",
    "* Futures Price: 1964.50 @ 18 NOV\n",
    "* Time To Maturity (years): 24/365\n",
    "* Time Step: 1/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk Free Rate: 1.9215376403686548 %\n"
     ]
    }
   ],
   "source": [
    "rate = -1/(1/12) * np.log(0.9984)\n",
    "print(f\"Risk Free Rate: {rate*100} %\")\n",
    "div_yield = rate - 1/(24/365) * np.log(1964.50/1965.29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "def black76(imp_vol, forward, strike, expiry, rate, type=\"C\"):\n",
    "    d1 = 1/(imp_vol * np.sqrt(expiry)) * (np.log(forward/strike) + imp_vol**2/2 * expiry)\n",
    "    d2 = d1 - imp_vol * np.sqrt(expiry)\n",
    "    discount = np.exp(-rate * expiry)\n",
    "    return discount * (forward * norm.cdf(d1) - strike * norm.cdf(d2)) \n",
    "\n",
    "def black_sholes(imp_vol, spot, strike, expiry, rate, type=\"C\"):\n",
    "    d1 = 1/(imp_vol * np.sqrt(expiry)) * (np.log(spot/strike) + (rate + imp_vol**2/2) * expiry)\n",
    "    d2 = d1 - imp_vol * np.sqrt(expiry)\n",
    "    discount = np.exp(-rate * expiry)\n",
    "    return spot * norm.cdf(d1) - strike * norm.cdf(d2) * discount "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are valuing the bid price, hence 55.50 IV calculation. We can note, that the IV from the option is about 1 % higher than the quoted bid IV from Reuters, suggesting something is slightly off. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      converged: True\n",
      "           flag: 'converged'\n",
      " function_calls: 7\n",
      "     iterations: 6\n",
      "           root: 0.23027960372852338\n",
      "55.500000000000455\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d7/rpl_88f12ln2z1s2h4m9ybjm0000gn/T/ipykernel_23346/2261038978.py:10: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  d1 = 1/(imp_vol * np.sqrt(expiry)) * (np.log(spot/strike) + (rate + imp_vol**2/2) * expiry)\n"
     ]
    }
   ],
   "source": [
    "# Extracting the implied volatility.\n",
    "from scipy.optimize import root_scalar\n",
    "\n",
    "def formatted_function(imp_vol):\n",
    "    return black_sholes(imp_vol, 1965.29, 1950, 24/365, rate, \"C\") - 55.50\n",
    "print(root_scalar(formatted_function, bracket=[0, 0.5]))\n",
    "print(black_sholes(0.23027960372852604, 1965.29, 1950, 24/365, rate, \"C\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We construct the binomial tree is made in a upper triangular matrix as\n",
    "\n",
    "$$\n",
    "    \\begin{bmatrix} \n",
    "    S_0 & \\dots  & S_0u^n\\\\\n",
    "    \\vdots & \\ddots & \\vdots\\\\\n",
    "    0 & \\dots  & S_0d^n \n",
    "    \\end{bmatrix},\n",
    "$$\n",
    "\n",
    "where any element in the matrix is $[S]_{ij} = S_0u^{j}d^{i-j}$ and the # of steps is simply $n = \\lfloor{\\frac{T}{\\Delta t}}\\rfloor{}$. \n",
    "\n",
    "The payoff function of the call option is simply $\\text{max}\\{S_T-K ,0 \\}$. This is applied on the last column of the spot matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_payoff(spot, strike):\n",
    "    for i in range(len(spot)):\n",
    "        spot[i] = np.max([spot[i]-strike,0])    \n",
    "    return spot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euro_call(next, q, rate, delta_t):\n",
    "    number_of_prices = len(next)\n",
    "    current = np.zeros(number_of_prices-1)\n",
    "    for i in range(len(current)):\n",
    "        current[i] = np.exp(-rate * delta_t) * (q * next[i] + (1-q) * next[i+1])\n",
    "    return current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "def american_call(next, q, spot, rate, delta_t, strike):\n",
    "    number_of_prices = len(next)\n",
    "    current = np.zeros(number_of_prices-1)\n",
    "    for i in range(len(current)):\n",
    "        current[i] = np.max([np.exp(-rate * delta_t) * (q * next[i] + (1-q) * next[i+1]), spot[i]- strike])\n",
    "    return current"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The option's price is the calculated recursively backwards. It suffices to keep the precious array of options prices, making this computation highly efficient.\n",
    "\n",
    "For european call options we have:\n",
    "\n",
    "$$\n",
    "C_{ij} = e^{-r\\Delta t}(q C_{i j} + (1-q) C_{i j-1}).\n",
    "$$\n",
    "\n",
    "For american call options we have:\n",
    "$$\n",
    "C_{ij} = \\text{max} \\{S_{ij}-K, e^{-r\\Delta t}(q C_{i j} + (1-q) C_{i j-1}) \\}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "def price_option(imp_vol, spot, strike, expiry, delta_t, rate, div_yield, div=0, div_expiry = 0, type=\"CE\"):\n",
    "    \"\"\"\n",
    "    type: Call European (CE) or Call American (CA).\n",
    "    \"\"\"\n",
    "\n",
    "    # Initialize parameters.\n",
    "    u = np.exp(imp_vol*np.sqrt(delta_t))\n",
    "    d = 1/u\n",
    "    q = (np.exp((rate-div_yield) * delta_t) - d) / (u - d)\n",
    "    steps = int(np.ceil(expiry/delta_t))\n",
    "    steps_until_dividend = int(np.floor(div_expiry/delta_t))\n",
    "\n",
    "    # Adjustment of spot if dividends are non-zero.\n",
    "    \n",
    "\n",
    "    # Build spot matrix. No diviends.\n",
    "    spot_matrix = np.zeros((steps+1, steps+1))\n",
    "    for i in range(steps+1):\n",
    "        for j in range(i, steps+1):\n",
    "            if j >= i:\n",
    "                spot_matrix[i,j] = spot * u **(j-i) * d**(i)\n",
    "    \n",
    "    # Build spot matrix. Dividends.\n",
    "    spot_matrix = np.zeros((steps+1, steps+1))\n",
    "    for i in range(steps+1):\n",
    "        for j in range(i, steps+1):\n",
    "            if j >= i:\n",
    "                if i <= steps_until_dividend and div != 0:\n",
    "                    spot_matrix[i,j] = spot * u **(j-i) * d**(i) + div * np.exp(-rate * (div_expiry - delta_t * i))\n",
    "                else:\n",
    "                    spot_matrix[i,j] = spot * u **(j-i) * d**(i)\n",
    "\n",
    "\n",
    "    # Pricing EURPOEAN CALL OPTIONS HERE:\n",
    "    if type == \"CE\":\n",
    "        next = call_payoff(spot_matrix[:,-1], strike)\n",
    "        for i in range(steps):\n",
    "            current = euro_call(next, q, rate, delta_t)\n",
    "            next = current\n",
    "\n",
    "    # Pricing AMERICAN CALL OPTIONS HERE:\n",
    "    if type == \"CA\":\n",
    "        next = call_payoff(spot_matrix[:,-1], strike)\n",
    "        for i in range(steps):\n",
    "            current = american_call(next, q, spot_matrix[:,-i-1]-strike, rate, delta_t, strike)\n",
    "            next = current\n",
    "\n",
    "    options_price = next[0]\n",
    "    return options_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = []\n",
    "steps = []\n",
    "reuters_iv = 0.2285\n",
    "for i in range(5,250):\n",
    "    prices.append(price_option(0.23027960372852604, 1965.29, 1950, 24/365, 24/365/(i), rate, 0))\n",
    "    steps.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 268,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(6,4)\n",
    "ax.plot(steps, len(steps)*[55.50], color='red')\n",
    "ax.plot(steps, prices)\n",
    "ax.set_title(\"Binomial Model Convergence OMXS30 CALL\", fontsize=16)\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valuing American Stock Options with Discrete Dividends\n",
    "\n",
    "We are also to value an american call option that is going to pay a discrete (known) dividend. For this we have the following date:\n",
    "\n",
    "* Spot price of SAAB is 232.6 SEK\n",
    "* Strike price is 250 SEK\n",
    "* The expiry is in 288 days\n",
    "* In 130 days there is an expected dividend of 4.174 SEK (ex-div)\n",
    "* The IV is 28.5 % \n",
    "* The rate is -0.05 %\n",
    "\n",
    "To properly account for a discrete dividend, while keeping the structure of the recombining binomial tree, we must build a discounted tree of $S_t^{*} = S_t - D e^{-rt}$. However, before an actual payment has been made, we must add back the discounting value of the dividend. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.784045179474738"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_option(0.285, 232.60-4.174*np.exp(0.05/100*288/365), 250, 288/365, 0.1/365, -0.05/100, 0, 4.174, 130/365, \"CA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual price being $C_0 = 14.75$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A short and concise explanation of the Binomial model can be found at:\n",
    "http://homepage.ntu.edu.tw/~jryanwang/courses/Financial%20Computation%20or%20Financial%20Engineering%20(graduate%20level)/FE_Ch04%20Binomial%20Tree%20Model.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_american = []\n",
    "steps_american = []\n",
    "for i in range(5, 500):\n",
    "    prices_american.append(price_option(0.285, 232.60-4.174*np.exp(0.05/100*288/365), 250, 288/365, 288/365/(i), -0.05/100, 0, 4.174, 130/365, \"CA\"))\n",
    "    steps_american.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 268,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(6,4)\n",
    "ax.plot(steps_american, 495*[14.75], color='red')\n",
    "ax.plot(steps_american, prices_american)\n",
    "ax.set_title(\"Binomial Model Convergence SAAB CALL\", fontsize=16)\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}