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ABSTRACT The CBOE VIX index is a widely recognised benchmark measure of expected stock market volatility. As shown in the literature, probability distributions other than Gaussian are key features required to describe the dynamics of the S&P 500, the variable that ultimately determines the VIX index level. As such, it is important to assess if deviations from the Gaussian distribution have important impacts on the VIX index level. We examine herein how a model articulated over a time-varying non-Gaussian distribution with conditional skewness and kurtosis can contribute to the overall explanation of the VIX dynamics. [ABSTRACT FROM AUTHOR]

Rubinstein developed a binomial lattice technique for pricing European and American derivatives in the context of skewed and leptokurtic asset return distributions. A drawback of this approach is the limited set of skewness and kurtosis pairs for which valid stock return distributions are possible. A solution to this problem is proposed here by extending Rubinstein's Edgeworth tree idea to the case of the Johnson system of distributions which is capable of accommodating all possible skewness and kurtosis pairs. Numerical examples showing the performance of the Johnson tree to approximate the prices of European and American options in Merton's jump diffusion framework and Duan's GARCH framework are examined. [ABSTRACT FROM AUTHOR]

Monte Carlo simulation is commonly used for computing prices of derivative securities when an analytical solution does not exist. Recently, a new simulation technique known as empirical martingale simulation (EMS) has been proposed by Duan and Simonato (1998) as a way of improving simulation accuracy. EMS has one drawback however. Because of the dependency among sample paths created by the EMS adjustment, the standard error of the price estimate is not readily available from using one simulation sample. In this paper, we develop a scheme to estimate the EMS accuracy. The EMS price estimator is first shown to have an asymptotically normal distribution. Through a simulation study, we then find that the asymptotic normal distribution serves as a good approximation for samples consisting of as few as 500 simulation paths. [ABSTRACT FROM AUTHOR]