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Pricing a Cosine Payoff with Risk-Neutral Valuation and Replication

Article Quant Q&A · Author: Dreason94

Summary

The document explains how to price a claim paying the cosine of a terminal stock price under risk-neutral pricing when the stock follows geometric Brownian motion. It writes the discounted expected payoff as an integral over the risk-neutral lognormal distribution. Unlike standard European calls and puts, this payoff can take negative values, so its price need not be positive. The payoff has no option-like convexity simply because it is a function of the stock price.

Three computational routes are presented: direct integration against the terminal-price density, static replication using a continuum of puts and calls through a payoff decomposition, and numerical methods such as Monte Carlo, trees, or Fourier techniques. The replication expression reduces the cosine payoff to a constant and an integral of calls, though the resulting integral may require numerical approximation. A simulation outline illustrates discounted payoff averaging, but its reported negative result depends on the example’s chosen inputs. The discussion assumes the stated model and a sufficiently smooth payoff; it does not address calibration, transaction costs, or practical hedging error.

Key ideas

  • A sufficiently smooth function of the terminal stock price can be valued by taking its discounted risk-neutral expectation.
  • A cosine payoff can be priced by integration against the risk-neutral terminal-price density.
  • Static replication can express the payoff using a constant and a weighted continuum of call payoffs.
  • Monte Carlo, tree methods, and Fourier methods offer numerical alternatives when integration is difficult.
  • The payoff can be negative, so its price is not subject to the positivity intuition for vanilla calls and puts.

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Full text
# Periodic functions when determining No Arbitrage price


# Periodic functions when determining No Arbitrage price












Is it possible to value a T-claim which has a periodic component? For example a claim such as $X = cos(S(T))$.

We assume here that $S(T)$ is the stock price derived from the dynamics $dS(t)=rS(t)dt+\sigma S(t)dW(t)$. Hence $S(T)=S(t)e^{(r-\sigma^2/2)(T-t)+\sigma (W(T)-W(t))}$.

What is then the No Arbitrage price $\Pi(t;X)=e^{-r(T-t)}E^Q_t[cos(S(T))]$? If this is theoretically possible, how would one compute such price?

## Answer by Kevin (score 10, accepted)

https://quant.stackexchange.com/a/50304

It is, of course, possible to price such a contract in a no-arbitrage market. Indeed, if $f$ is a sufficiently smooth function, then you can price all contracts paying $f(S_T)$. Note that your specific payoff has no optionality and that the payoff may be negative. Bakshi and Madan (2000) discuss the economic meaning of a derivative paying $\cos(S_T)$ in the context of market completion and characteristic functions.

You have many different ways of solving the pricing problem:

### Standard risk-neutral pricing

Look at the simpler case of the time zero price \begin{align} \Pi(0;X) &= e^{-rT}\mathbb{E}^\mathbb{Q}[\cos(S_T)] \\ &= e^{-rT}\int_\mathbb{R}\cos(x)f_{S_T}^\mathbb{Q}(x)\mathrm{d}x \\ &= e^{-rT}\int_0^\infty \cos(x)\frac{1}{x\sqrt{2\pi\sigma^2T}}\exp\left(-\frac{1}{2}\left(\frac{\ln(x)-\ln(S_0)-\left(r-\frac{1}{2}\sigma^2\right)T}{\sigma\sqrt{T}}\right)^2\right)\mathrm{d}x \end{align} In the standard put/call case, the substitution $\xi=\frac{\ln(x)-\ln(S_0)-\left(r-\frac{1}{2}\sigma^2\right)T}{\sigma\sqrt{T}}$ with $\mathrm{d}x=x\sigma\sqrt{T}\mathrm{d}\xi$ concludes the computation, i.e. $$ \Pi(0;X) = e^{-rT}\int_\mathbb{R} \cos(x)\frac{1}{\sqrt{2\pi}}\exp\left(-\frac{1}{2}\xi^2\right)\mathrm{d}\xi.$$ In your case, the integral may be a bit harder to compute due to the additional $\cos(x)$ term, where $x=\exp\left(\xi\sigma\sqrt{T}+\ln(S_0)+\left(r-\frac{1}{2}\sigma^2\right)T\right)$.

### Carr Madan Static Replication Formula

Assuming you know how to price call options in your chosen model (easy in the Black Scholes case), we can use the following formula \begin{align*} f(x) &= f(a) + f'(a)\cdot \left( (x-a)^+ - (a-x)^+ \right)\\ &+ \int_0^a f''(\kappa) (\kappa-x)^+ \mathrm{d}\kappa+ \int_a^\infty f''(\kappa) (x-\kappa)^+ \mathrm{d}\kappa, \end{align*} which holds for any sufficiently smooth $f$ and for any $a\geq 0$. This expresses a payoff function $f$ in terms of the payoff function of puts and calls. Using $f(x)=\cos(x)$ and $a=0$, we get \begin{align*} \cos(x) &= 1 - \int_0^\infty \cos(\kappa) (x-\kappa)^+ \mathrm{d}\kappa \\ \implies \Pi(t,X) &= e^{-r(T-t)}-\int_0^\infty \cos(\kappa)\mathrm{Call}(S_t,\kappa,T)\mathrm{d}\kappa, \end{align*} where $\mathrm{Call}(S_t,\kappa,T)$ denotes the Black Scholes price of a European-style call option with strike price $\kappa$ and maturity $T$. You end up with an integral which may be hard to analitically compute, but it is very easy to approximate the integral with arbitrary precision.

### Standard Numeric Algortihms

It is very easy to find the price of this claim using Monte Carlo simulations.. Just simulate realisations of $S_T$ and apply the cosine function to the terminal stock price. Then, you know your future payoff which you merely need to discount and average. Of course, you can also employ other numerical techniques such as trees and Fourier methods.

Look at this brief `R` example of a Monte Carlo simulation

```
tau = 1 #Time to maturity
s0 = 1.75 # initial stock price
sigma = 0.1 # volatility p.a.
r = 0.1 # interest rate p.a.
nSim = 1000000
Z <- rnorm(nSim, mean=0, sd=1)
WT <- sqrt(tau) * Z
ST = s0*exp((r - 0.5*sigma^2)*tau + sigma*WT) # terminal stock price
simulated_payoffs <- exp(-r*tau)*cos(ST) # discounted payoff
price <- mean(simulated_payoffs)
```

You'll see that the price is negative. This is impossible for put and call options due to their optionality but quite possible for your claim paying $\cos(S_T)$ since the payoff itself may be negative...

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.