In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical...

The need to understand and leverage consumer-brand bonds has become critical in a marketplace characterized by increasing unpredictability, diminishing product differentiation, and heightened competitive pressure. This is especially true for fast moving consumer goods (FMCG) manufacturers and...

In a world with complete markets and no transactions cost, the decision whether to rent or buy a home is separate from a household's professional income risk. If markets are incomplete and have frictions, however, profession- specific income risk, regional house price risk, and mobility needs...

Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The...

German time use data for 2001/02 are used to assess the impact of workplace characteristics on the private life of couples. The major aim is to solve the endogeneity resulting from individual preferences for work and leisure to identify the pure effects of the workplace independent from other...

This chapter deals with nonparametric estimation of the risk neutral density. We present three different approaches which do not require parametric functional assumptions on the underlying asset price dynamics nor on the distributional form of the risk neutral density. The first estimator is a...

Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and...

Dimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most...