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Pricing Best-of Options with Margrabe’s Exchange-Option Formula

Article Quant Q&A · Author: vicky113

Summary

The document explains how to price a European best-of option on two correlated assets under geometric Brownian motion. Its key step rewrites the maximum payoff as one asset plus an exchange option that pays the positive difference between the other asset and the first. The first component is valued as the underlying asset; Margrabe’s formula prices the exchange option using the assets’ relative volatility, which incorporates both individual volatilities and their correlation.

The response gives the exchange-option formula and shows how the terms combine into a best-of price. The derivation assumes the stated model setup and illustrates the zero-strike payoff; it does not work through a full derivation or discuss calibration. A second answer points to a separate treatment of worst-of calls with nonzero strike and notes that practical valuation may use Monte Carlo with calibrated local or stochastic volatility. Those model choices go beyond the closed-form formula presented here.

Key ideas

  • A best-of payoff can be decomposed into one underlying asset and an exchange option.
  • Margrabe’s formula prices the exchange option using the relative volatility of the two assets.
  • Relative variance depends on both asset volatilities and their correlation.
  • The closed-form result relies on geometric Brownian motion assumptions.
  • Monte Carlo with calibrated local or stochastic volatility is suggested for practical applications.

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Full text
# Pricing Equation for Best of Options


# Pricing Equation for Best of Options












I am trying to derive a martingale pricing equation (closed form solution) for a best-of option. But I am getting stuck at a point.

There are 2 stocks $U(t)$ and $V(t)$ they both follow GBM with a correlation of $\rho$.

I want to price an European option whose final payoff is $\max \left\{ U(t), V(t) \right\}$. For simplicity I am assuming that interest rates are 0.

$$ V(0) = \mathbb{E} \left[ \max \left\{ U(t), V(t) \right\} \right] $$

By theory of total expectation

$$ V(0) = \mathbb{E} \left[ U(t) \right] \mathbb{P} \left\{ U(t) > V(t) \right\} + \mathbb{E} \left[ V(t) \right] \left( 1 - \mathbb{P} \left\{ U(t) > V(t) \right\} \right) $$

But I guess this is not the correct approach.

Any help is highly appreciated.

## Answer by JejeBelfort (score 2, accepted)

https://quant.stackexchange.com/a/35309

For best-of options, the usual approach is to use Magrabe's formula.

Starting from the following relationship:

$$\max\{U(t),V(t)\} = U(t) + \max\{V(t) - U(t), 0\} ,$$

you end up with the sum of one of the underlying asset, $U(t)$, and an exchange option paying $\max\{V(t) - U(t), 0\}$.

While the present value of the first term is easy to find, the second requires Magrabe's results stating that the price of the exchange option is given by:

$$V(t) N(d_U) e^{-q_V \tau} - U(t) N(d_V) e^{-q_U \tau}$$

where

$$d_V = \frac{\ln\left(V(t) / U(t)\right) + \left( q_U - q_V - \sigma^2 / 2\right)\tau}{\sigma \sqrt{\tau}}$$

$$d_U = d_V + \sigma \sqrt{\tau}$$

and

$$\sigma^2 = \sigma^2_U + \sigma^2_V - 2 \rho \sigma_U \sigma_V$$

with $\tau$ being the time-to-expiration, $q_x$ and $\sigma_x$ being respectively the dividend yield and the volatility of asset $x$, $x \in \{U,V \}$

Therefore, the price of your best-of option becomes:

$$U(t) e^{-q_U \tau} + V(t) N(d_U) e^{-q_V \tau} - U(t) N(d_V) e^{-q_U \tau}$$

which simplifies to

$$V(t) N(d_U) e^{-q_V \tau} + U(t) N(-d_V) e^{-q_U \tau}$$

## Answer by Tom Gladd (score -1)

https://quant.stackexchange.com/a/35324

Assuming GBM dynamics, I work through the (tedious) details of a worst-of call option in the more general case of a nonzero strike.

ntgladd.com

tab = Finance, subsection = Option models, file = Worst Of Call Option - Three Ways

In a real world application, you would use Monte Carlo with some sort of market calibrated local vol or stochastic vol model.

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.