This paper develops a Monte Carlo simulation method for solving option valuation problems. The method simulates the process generating the returns on the underlying asset and invokes the risk neutrality assumption to derive the value of the option. Techniques for improving the efficiency of the method are introduced. Some numerical examples are given to illustrate the procedure and additional applications are suggested.

This paper analyses the distribution of returns on a hedged portfolio, consisting of a European call option and its associated stock, when the portfolio is rebalanced at discrete time intervals. Under the assumptions of the Black-Scholes model this distribution is particularly skew. In tests of the average return on a hedged portfolio this skewness leads to biased t-statistics. The paper explores the nature and extent of this bias and suggests procedures for overcoming it. Other aspects of discrete hedging are also discussed.

This paper examines some implications of using an estimate of the variance in option valuation models. This procedure produces biased option values. It is shown that the magnitude of this bias is not large. The dispersion induced in the option price is more significant particularly for parameter values of practical interest. The nature and extent of this dispersion is examined by numerical examples. The paper suggests how a Bayesian approach could be used to cope with the estimation error.

In 1987, Lo derived an upper bound on the price of a European call option on a single asset. Lo's bound depends only on the mean and variance of the terminal asset price and is termed a semi-parametric bound. This paper derives similar semi-parametric bounds on a European call on the maximum of any number of assets. A distribution-free bound for the price of this option is obtained. The bound depends only on the means and covariance matrix of the returns on n underlying assets. The bound is obtained by optimizing over the entries of a positive definite matrix A. This can be accomplished by a technique known as semidefinite programming. We demonstrate the methodology using two specific applications. The first concerns the valuation of a European call option on the maximum of several assets. This is known as an outperformance option and is of some practical interest. The second application concerns the valuation of a discretely adjusted lookback option. These lookback options are of interest in connection with certain equity annuity insurance products.