In this thesis I discuss flexible Bayesian treatment of the linear factor stochastic volatility model with latent factors, which proves to be essential in order to preserve parsimony when the number of cross section in the data grows. Based on the Bayesian model selection literature, I introduce...

Option pricing is an integral part of modern financial risk management. The well-known Black and Scholes (1973) formula is commonly used for this purpose. This paper is an attempt to extend their work to a situation in which the unconditional volatility of the original asset is increasing during...

This paper proposes a new approach to strategic asset allocation for central banks’ management of foreign reserves. This eclectic approach combines the behavioural portfolio management in the framework of mean-variance mental accounting (MVMA) with the improvements on asset return forecast...

Markowitz portfolio selection is challenged by huge implementation barriers. This paper addresses the parameter uncertainty and deviation from normality in a Bayesian framework. The non-normal asset returns are modeled as finite Gaussian mixtures. Gibbs sampler is employed to obtain draws from...

Departure from normality poses implementation barriers to the Markowitz mean-variance portfolio selection. When assets are affected by common and idiosyncratic shocks, the distribution of asset returns may exhibit Markov switching regimes and have a Gaussian mixture distribution conditional on...

The relationship between risk and return is one of the most studied topics in finance. The majority of the literature is based on a linear, parametric relationship between expected returns and conditional volatility. However, there is no theoretical justification for the relationship to be...

The paper presents a comparative study of conventional beta adjustment techniques and suggests an improved Bayesian model for beta forecasting. The seminal papers of Blume (1971) and Levy (1971) suggested that for both single security and portfolio there was a tendency for relatively high and...

Over the last two decades, alternative expected return proxies have been proposed with substantially lower variation than realized returns. This helped to reduce parameter uncertainty and to identify many seemingly robust relations between expected returns and variables of interest, which would...

In this paper, we study the risk-return relationship in monthly U.S. stock returns (1928:1— 2004:12) using GARCH-in-Mean models. In particular, we consider the robustness of the relationship with respect to the omission of the intercept term in the equation for the expected excess return...

We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A...