QuantLib: DiscreteHedging.cpp

A free/open-source library for quantitative finance Reference manual - version 1.20

DiscreteHedging.cpp

This example computes profit and loss of a discrete interval hedging strategy and compares with the outcome with the results of Derman and Kamal's Goldman Sachs Equity Derivatives Research Note "When You Cannot Hedge Continuously: The Corrections to Black-Scholes". It shows the use of the Monte Carlo framework.

/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/* This example computes profit and loss of a discrete interval hedging
strategy and compares with the results of Derman & Kamal's (Goldman Sachs
Equity Derivatives Research) Research Note: "When You Cannot Hedge
Continuously: The Corrections to Black-Scholes"
http://www.ederman.com/emanuelderman/GSQSpapers/when_you_cannot_hedge.pdf
Suppose an option hedger sells an European option and receives the
Black-Scholes value as the options premium.
Then he follows a Black-Scholes hedging strategy, rehedging at discrete,
evenly spaced time intervals as the underlying stock changes. At
expiration, the hedger delivers the option payoff to the option holder,
and unwinds the hedge. We are interested in understanding the final
profit or loss of this strategy.
If the hedger had followed the exact Black-Scholes replication strategy,
re-hedging continuously as the underlying stock evolved towards its final
value at expiration, then, no matter what path the stock took, the final
P&L would be exactly zero. When the replication strategy deviates from
the exact Black-Scholes method, the final P&L may deviate from zero. This
deviation is called the replication error. When the hedger rebalances at
discrete rather than continuous intervals, the hedge is imperfect and the
replication is inexact. The more often hedging occurs, the smaller the
replication error.
We examine the range of possibilities, computing the replication error.
*/
#include <ql/qldefines.hpp>
#ifdef BOOST_MSVC
# include <ql/auto_link.hpp>
#endif
#include <ql/methods/montecarlo/montecarlomodel.hpp>
#include <ql/processes/blackscholesprocess.hpp>
#include <ql/termstructures/yield/flatforward.hpp>
#include <ql/termstructures/volatility/equityfx/blackconstantvol.hpp>
#include <ql/pricingengines/blackcalculator.hpp>
#include <ql/quotes/simplequote.hpp>
#include <ql/time/calendars/target.hpp>
#include <ql/time/daycounters/actual365fixed.hpp>
#include <iostream>
#include <iomanip>
using namespace QuantLib;
#if defined(QL_ENABLE_SESSIONS)
namespace QuantLib {
ThreadKey sessionId() { return 0; }
}
#endif
/* The ReplicationError class carries out Monte Carlo simulations to evaluate
the outcome (the replication error) of the discrete hedging strategy over
different, randomly generated scenarios of future stock price evolution.
*/
class ReplicationError
{
public:
ReplicationError(Option::Type type,
Time maturity,
Real strike,
Real s0,
Volatility sigma,
Rate r)
: maturity_(maturity), payoff_(type, strike), s0_(s0),
sigma_(sigma), r_(r) {
// value of the option
DiscountFactor rDiscount = std::exp(-r_*maturity_);
DiscountFactor qDiscount = 1.0;
Real forward = s0_*qDiscount/rDiscount;
Real stdDev = std::sqrt(sigma_*sigma_*maturity_);
ext::shared_ptr<StrikedTypePayoff> payoff(
new PlainVanillaPayoff(payoff_));
BlackCalculator black(payoff,forward,stdDev,rDiscount);
std::cout << "Option value: " << black.value() << std::endl;
// store option's vega, since Derman and Kamal's formula needs it
vega_ = black.vega(maturity_);
std::cout << std::endl;
std::cout << std::setw(8) << " " << " | "
<< std::setw(8) << " " << " | "
<< std::setw(8) << "P&L" << " | "
<< std::setw(8) << "P&L" << " | "
<< std::setw(12) << "Derman&Kamal" << " | "
<< std::setw(8) << "P&L" << " | "
<< std::setw(8) << "P&L" << std::endl;
std::cout << std::setw(8) << "samples" << " | "
<< std::setw(8) << "trades" << " | "
<< std::setw(8) << "mean" << " | "
<< std::setw(8) << "std.dev." << " | "
<< std::setw(12) << "formula" << " | "
<< std::setw(8) << "skewness" << " | "
<< std::setw(8) << "kurtosis" << std::endl;
std::cout << std::string(78, '-') << std::endl;
}
// the actual replication error computation
void compute(Size nTimeSteps, Size nSamples);
private:
Time maturity_;
PlainVanillaPayoff payoff_;
Real s0_;
Volatility sigma_;
Rate r_;
Real vega_;
};
// The key for the MonteCarlo simulation is to have a PathPricer that
// implements a value(const Path& path) method.
// This method prices the portfolio for each Path of the random variable
class ReplicationPathPricer : public PathPricer<Path> {
public:
// real constructor
ReplicationPathPricer(Option::Type type,
Real strike,
Rate r,
Time maturity,
Volatility sigma)
: type_(type), strike_(strike),
r_(r), maturity_(maturity), sigma_(sigma) {
QL_REQUIRE(strike_ > 0.0, "strike must be positive");
QL_REQUIRE(r_ >= 0.0,
"risk free rate (r) must be positive or zero");
QL_REQUIRE(maturity_ > 0.0, "maturity must be positive");
QL_REQUIRE(sigma_ >= 0.0,
"volatility (sigma) must be positive or zero");
}
// The value() method encapsulates the pricing code
Real operator()(const Path& path) const;
private:
Option::Type type_;
Real strike_;
Rate r_;
Time maturity_;
Volatility sigma_;
};
// Compute Replication Error as in the Derman and Kamal's research note
int main(int, char* []) {
try {
std::cout << std::endl;
Time maturity = 1.0/12.0; // 1 month
Real strike = 100;
Real underlying = 100;
Volatility volatility = 0.20; // 20%
Rate riskFreeRate = 0.05; // 5%
ReplicationError rp(Option::Call, maturity, strike, underlying,
volatility, riskFreeRate);
Size scenarios = 50000;
Size hedgesNum;
hedgesNum = 21;
rp.compute(hedgesNum, scenarios);
hedgesNum = 84;
rp.compute(hedgesNum, scenarios);
return 0;
} catch (std::exception& e) {
std::cerr << e.what() << std::endl;
return 1;
} catch (...) {
std::cerr << "unknown error" << std::endl;
return 1;
}
}
/* The actual computation of the Profit&Loss for each single path.
In each scenario N rehedging trades spaced evenly in time over
the life of the option are carried out, using the Black-Scholes
hedge ratio.
*/
Real ReplicationPathPricer::operator()(const Path& path) const {
Size n = path.length()-1;
QL_REQUIRE(n>0, "the path cannot be empty");
// discrete hedging interval
Time dt = maturity_/n;
// For simplicity, we assume the stock pays no dividends.
Rate stockDividendYield = 0.0;
// let's start
Time t = 0;
// stock value at t=0
Real stock = path. front();
// money account at t=0
Real money_account = 0.0;
/************************/
/*** the initial deal ***/
/************************/
// option fair price (Black-Scholes) at t=0
DiscountFactor rDiscount = std::exp(-r_*maturity_);
DiscountFactor qDiscount = std::exp(-stockDividendYield*maturity_);
Real forward = stock*qDiscount/rDiscount;
Real stdDev = std::sqrt(sigma_*sigma_*maturity_);
ext::shared_ptr<StrikedTypePayoff> payoff(
new PlainVanillaPayoff(type_,strike_));
BlackCalculator black(payoff,forward,stdDev,rDiscount);
// sell the option, cash in its premium
money_account += black.value();
// compute delta
Real delta = black.delta(stock);
// delta-hedge the option buying stock
Real stockAmount = delta;
money_account -= stockAmount*stock;
/**********************************/
/*** hedging during option life ***/
/**********************************/
for (Size step = 0; step < n-1; step++){
// time flows
t += dt;
// accruing on the money account
money_account *= std::exp( r_*dt );
// stock growth:
stock = path[step+1];
// recalculate option value at the current stock value,
// and the current time to maturity
rDiscount = std::exp(-r_*(maturity_-t));
qDiscount = std::exp(-stockDividendYield*(maturity_-t));
forward = stock*qDiscount/rDiscount;
stdDev = std::sqrt(sigma_*sigma_*(maturity_-t));
BlackCalculator black(payoff,forward,stdDev,rDiscount);
// recalculate delta
delta = black.delta(stock);
// re-hedging
money_account -= (delta - stockAmount)*stock;
stockAmount = delta;
}
/*************************/
/*** option expiration ***/
/*************************/
// last accrual on my money account
money_account *= std::exp( r_*dt );
// last stock growth
stock = path[n];
// the hedger delivers the option payoff to the option holder
Real optionPayoff = PlainVanillaPayoff(type_, strike_)(stock);
money_account -= optionPayoff;
// and unwinds the hedge selling his stock position
money_account += stockAmount*stock;
// final Profit&Loss
return money_account;
}
// The computation over nSamples paths of the P&L distribution
void ReplicationError::compute(Size nTimeSteps, Size nSamples)
{
QL_REQUIRE(nTimeSteps>0, "the number of steps must be > 0");
// hedging interval
// Time tau = maturity_ / nTimeSteps;
/* Black-Scholes framework: the underlying stock price evolves
lognormally with a fixed known volatility that stays constant
throughout time.
*/
Calendar calendar = TARGET();
Date today = Date::todaysDate();
DayCounter dayCount = Actual365Fixed();
Handle<Quote> stateVariable(
ext::shared_ptr<Quote>(new SimpleQuote(s0_)));
Handle<YieldTermStructure> riskFreeRate(
ext::shared_ptr<YieldTermStructure>(
new FlatForward(today, r_, dayCount)));
Handle<YieldTermStructure> dividendYield(
ext::shared_ptr<YieldTermStructure>(
new FlatForward(today, 0.0, dayCount)));
Handle<BlackVolTermStructure> volatility(
ext::shared_ptr<BlackVolTermStructure>(
new BlackConstantVol(today, calendar, sigma_, dayCount)));
ext::shared_ptr<StochasticProcess1D> diffusion(
new BlackScholesMertonProcess(stateVariable, dividendYield,
riskFreeRate, volatility));
// Black Scholes equation rules the path generator:
// at each step the log of the stock
// will have drift and sigma^2 variance
PseudoRandom::rsg_type rsg =
PseudoRandom::make_sequence_generator(nTimeSteps, 0);
bool brownianBridge = false;
typedef SingleVariate<PseudoRandom>::path_generator_type generator_type;
ext::shared_ptr<generator_type> myPathGenerator(new
generator_type(diffusion, maturity_, nTimeSteps,
rsg, brownianBridge));
// The replication strategy's Profit&Loss is computed for each path
// of the stock. The path pricer knows how to price a path using its
// value() method
ext::shared_ptr<PathPricer<Path> > myPathPricer(new
ReplicationPathPricer(payoff_.optionType(), payoff_.strike(),
r_, maturity_, sigma_));
// a statistics accumulator for the path-dependant Profit&Loss values
Statistics statisticsAccumulator;
// The Monte Carlo model generates paths using myPathGenerator
// each path is priced using myPathPricer
// prices will be accumulated into statisticsAccumulator
MonteCarloModel<SingleVariate,PseudoRandom>
MCSimulation(myPathGenerator,
myPathPricer,
statisticsAccumulator,
false);
// the model simulates nSamples paths
MCSimulation.addSamples(nSamples);
// the sampleAccumulator method
// gives access to all the methods of statisticsAccumulator
Real PLMean = MCSimulation.sampleAccumulator().mean();
Real PLStDev = MCSimulation.sampleAccumulator().standardDeviation();
Real PLSkew = MCSimulation.sampleAccumulator().skewness();
Real PLKurt = MCSimulation.sampleAccumulator().kurtosis();
// Derman and Kamal's formula
Real theorStD = std::sqrt(M_PI/4/nTimeSteps)*vega_*sigma_;
std::cout << std::fixed
<< std::setw(8) << nSamples << " | "
<< std::setw(8) << nTimeSteps << " | "
<< std::setw(8) << std::setprecision(3) << PLMean << " | "
<< std::setw(8) << std::setprecision(2) << PLStDev << " | "
<< std::setw(12) << std::setprecision(2) << theorStD << " | "
<< std::setw(8) << std::setprecision(2) << PLSkew << " | "
<< std::setw(8) << std::setprecision(2) << PLKurt << std::endl;
}