A Note on Option Pricing with the Use of Discrete-Time Stochastic Volatility Processes

In this paper we show that in the lognormal discrete-time stochastic volatility model with predictable conditional expected returns, the conditional expected value of the discounted payoff of a European call option is infinite. Our empirical illustration shows that the characteristics of the predictive distributions of the discounted payoffs, obtained using Monte Carlo methods, do not indicate directly that the expected discounted payoffs are infinite.option pricing, SV model, Bayesian forecasting

Bayesian Analysis of the Box-Cox Transformation in Stochastic Volatility Models

In the paper, we consider the Box-Cox transformation of financial time series in Stochastic Volatility models. Bayesian approach is applied to make inference about the Box-Cox transformation parameter (l). Using daily data (quotations of stock indices), we show that in the Stochastic Volatility models with fat tails and correlated errors (FCSV), the posterior distribution of parameter l strongly depends on the prior assumption about this parameter. In the majority of cases the values of l close to 0 are more probable a posteriori than the ones close to 1.Box-Cox transformation, SV model, Bayesian inference.