Essays on bivariate option pricing via copula and heteroscedasticity models: a classical and bayesian approach

Abstract

This dissertation is composed of two main and independents essays, but complementary. In the first one, we discuss the option price under a bayesian perspective. This essay aims to price and analyze the fair price behavior of the call-on-max (bivariate) option considering marginal heteroscedastic models with dependence structure modeled via copulas. Concerning inference, we adopt a Bayesian perspective and computationally intensive methods based on Monte Carlo simulations via Markov Chain (MCMC). A simulation study examines the bias and the root mean squared errors of the posterior means for the parameters. Real stocks prices of Brazilian banks illustrate the approach. For the proposed method is verified the effects of strike and dependence structure on the fair price of the option. The results show that the prices obtained by our heteroscedastic model approach and copulas differ substantially from the prices obtained by the model derived from Black and Scholes. Empirical results are presented to argue the advantages of our strategy. In the second chapter, we consider the GARCH-in-mean models with asymmetric variance specifications to model the volatility of the assets-objects under the risk-neutral dynamics. Moreover, the copula functions model the joint distribution, with the objective of capturing non-linear, linear and tails associations between the assets. We aim to provide a methodology to realize a more realistic pricing option. To illustrate the methodology, we use stocks from two Brazilian companies, where our the modeling offered a proper fitting. Confronting the results obtained with the classic model, which is an extension of the Black and Scholes model, we note that considering constant volatility over time underpricing the options, especially in-the-money options.