A0887
Title: Bayesian lower and upper estimates for ether option prices with conditional heteroscedasticity and model uncertainty
Authors: Tak Kuen Siu - Macquarie University (Australia) [presenting]
Abstract: The purpose is to discuss the use of Bayesian nonlinear expectations to construct lower and upper estimates for prices of Ether options with conditional heteroscedasticity and model uncertainty. Specifically, a GARCH model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum. Bayesian nonlinear expectations are adopted to introduce uncertainty about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov's principle is adopted to change probability measures and introduce a family of alternative GARCH models. The Bayesian credible intervals for uncertain drift and volatility parameters obtained from conjugate priors and residuals of the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations, giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD.
