riskless-yield-construction-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "right-scheme",
   "metadata": {},
   "source": [
    "## Constructing a riskless yield 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 yield 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 use the Fed Funds effective rate and US Treasury Yields as proxies for riskless rates. Ideally, using OIS swap rates might be better as treasury yields tend to be slightly lower given the preferred tax treatment of treasuries. However US Treasury Yields are available for free from [FRED](https://fred.stlouisfed.org) with daily frequency while OIS swap rates re not, and the difference is pretty small. Also it'd be better to use treasury spot rates (aka zero-rates) rather than treasury yields. However, treasury spot rates are not made avaliable by FRED.\n",
    "\n",
    "I use the following dataseries from FRED to accomplish the task:\n",
    "* DFF &mdash; Federal Funds Effective Rate (daily frequency)\n",
    "* DGS1MO &mdash; Market Yield on U.S. Treasury Securities at 1-Month Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS3MO &mdash; Market Yield on U.S. Treasury Securities at 3-Month Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS6MO &mdash; Market Yield on U.S. Treasury Securities at 6-Month Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS1 &mdash; Market Yield on U.S. Treasury Securities at 1-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS2 &mdash; Market Yield on U.S. Treasury Securities at 2-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS3 &mdash; Market Yield on U.S. Treasury Securities at 3-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS5 &mdash; Market Yield on U.S. Treasury Securities at 5-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS7 &mdash; Market Yield on U.S. Treasury Securities at 7-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS10 &mdash; Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS20 &mdash; Market Yield on U.S. Treasury Securities at 20-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS30 &mdash; Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "diverse-convertible",
   "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": "occupied-fundamentals",
   "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",
    "import yfinance as yfin\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": 3,
   "id": "brief-makeup",
   "metadata": {},
   "outputs": [],
   "source": [
    "today = date.today()\n",
    "\n",
    "\n",
    "# Constructing the riskless yield curve based on the current fed funds rate and treasury yields\n",
    "data = web.get_data_fred(\n",
    "    ['DFF', 'DGS1MO', 'DGS3MO', 'DGS6MO', 'DGS1', 'DGS2', 'DGS3', 'DGS5', 'DGS7', 'DGS10', 'DGS20', 'DGS30'],\n",
    "    today - BDay(3), 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",
    "# Some adjustments are required to bring FED Funds rate to the same day count convention and compounding frequency\n",
    "# as treasury yields (actual/actual with semi-annual compounding):\n",
    "# 1. https://www.federalreserve.gov/releases/h15/default.htm -> day count convention for Fed Funds Rate needs\n",
    "# to be changed to actual/actual\n",
    "# 2. Conversion to APY: https://home.treasury.gov/policy-issues/financing-the-government/interest-rate-statistics/interest-rates-frequently-asked-questions\n",
    "data.DFF *= (366 if curves.YieldCurve.is_leap_year(cur_date_curve.year) else 365) / 360 # to x/actual\n",
    "data.DFF = 2 * (np.sqrt(data.DFF + 1) - 1)\n",
    "\n",
    "offsets = [relativedelta(), relativedelta(months=+1), relativedelta(months=+3), relativedelta(months=+6),\n",
    "           relativedelta(years=+1), relativedelta(years=+2), relativedelta(years=+3), relativedelta(years=+5),\n",
    "           relativedelta(years=+7), relativedelta(years=+10), relativedelta(years=+20),\n",
    "           relativedelta(years=+30)]\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=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olympic-front",
   "metadata": {},
   "source": [
    "### Optional: Visualizing the riskless yield curve for USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "crucial-quest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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='US Treasury Yield 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": 5,
   "id": "clean-athens",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert curve.date == BDay(1).rollforward(today).date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beneficial-round",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}