riskless-rate-curve-construction-ameribor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "rolled-nightmare",
   "metadata": {},
   "source": [
    "## Constructing a riskless AMERIBOR rate curve for USD\n",
    "The first thing we need to do to price any option on a US equity is construct the so-called riskless sport rate (aka zero-rate) curve for USD.  This is because pricing of derivatives is done in _the risk-neutral world_ where all investors are assumed not to require a premium for additional risks, hence future cashflows are discounted using the riskless rate to obtain the NPV of the cashflow.\n",
    "\n",
    "I this notebook I use [Ameribor](https://www.investopedia.com/ameribor-6260927) rates as proxies for riskless rates. They are expressed in Actual/360 day count convention.\n",
    "\n",
    "I use the following dataseries from FRED to accomplish the task: [AMERIBOR Term Structure](https://fred.stlouisfed.org/release/tables?rid=504&eid=1227517).\n",
    "\n",
    "In the `pricing.curves.YieldCurve` class I use cubic splines by default to interpolate when constructing the curve. See [this article](http://web.math.ku.dk/~rolf/HaganWest.pdf) for more details on interpolation methods if you want to know more. Cubic splines is pretty advanced.\n",
    "\n",
    "**NB**: Unfortunately Federal Reserve Bank of St. Louis stopped publishing the term structure of Ameribor at the end of 2023. Therefore this notebook should no longer be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "selected-snake",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment if running in Google Colaboratory, otherwise the import of the curves module in the cell below will fail\n",
    "#!git clone -l -s https://github.com/ilchen/options-pricing.git cloned-repo\n",
    "#%cd cloned-repo\n",
    "\n",
    "# Install the latest version of pandas datareader\n",
    "#!pip install pandas-datareader -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "positive-mentor",
   "metadata": {},
   "outputs": [],
   "source": [
    "import locale\n",
    "from datetime import date, datetime\n",
    "\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import pandas_datareader.data as web\n",
    "from pandas.tseries.offsets import BDay\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from volatility import parameter_estimators\n",
    "from volatility import volatility_trackers\n",
    "from pricing import curves, options\n",
    "\n",
    "_ = locale.setlocale(locale.LC_ALL, '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "floral-poison",
   "metadata": {},
   "outputs": [],
   "source": [
    "today = date.today()\n",
    "\n",
    "# Offsetting to the next business day if today is not a business day, not doing it would artificially raise\n",
    "# the price of options priced.\n",
    "today = BDay(1).rollforward(today)\n",
    "\n",
    "# Constructing the riskless yield curve based on the current fed funds rate and treasury yields\n",
    "data = web.get_data_fred(\n",
    "    ['AMERIBOR', 'AMBOR1W', 'AMBOR1M', 'AMBOR3M', 'AMBOR6M', 'AMBOR1Y', 'AMBOR2Y'],\n",
    "    today - BDay(10), today)\n",
    "data.dropna(inplace=True)\n",
    "\n",
    "cur_date_curve = data.index[-1].date()\n",
    "\n",
    "# Convert to percentage points\n",
    "data /= 100.\n",
    "\n",
    "offsets = [relativedelta(), relativedelta(weeks=+1), relativedelta(months=+1), relativedelta(months=+3), relativedelta(months=+6),\n",
    "           relativedelta(years=+1), relativedelta(years=+2)]\n",
    "\n",
    "\n",
    "# Some adjustments are required to bring AMERIBOR rates to an actual/actual day count convention\n",
    "data *= 365 / 360 # to x/actual\n",
    "\n",
    "# Define the riskless yield curve\n",
    "curve = curves.YieldCurve(today, offsets, data[cur_date_curve:cur_date_curve + BDay()].to_numpy()[0, :],\n",
    "                          compounding_freq=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "monetary-mentor",
   "metadata": {},
   "source": [
    "### Optional: Visualizing the riskless AMERIBOR yield curve for USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "physical-emphasis",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "curve_points = curve.get_curve_points(120)\n",
    "ax = curve_points.plot(figsize=(20,10), grid=True, title='AMERIBOR Curve on ' + cur_date_curve.strftime('%Y-%m-%d'),\n",
    "                       xlim=(curve_points.index[0], curve_points.index[-1]),\n",
    "                       xticks=curve_points.index[::4], xlabel='', rot=45)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "_ = ax.set_xticklabels(list(map(lambda x: x.strftime('%Y-%m'), curve_points.index[::4])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "frequent-royal",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert curve.date == today.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "laughing-simpson",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}