We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

This paper documents the existence of a slowly evolving trend in the dividend-price ratio, dp, determined by a demographic variable, MY: the middle-aged to young ratio. Deviations of the dividend-price ratio from this slowly evolving long-run component explain transitory but persistent...

This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility model. The distribution of the logarithm of the squared return is flexibly modelled using an infinite mixture of Normal distributions. This allows efficient Markov chain Monte...

In this paper, we explore the interconnection and existing relationships between the Sovereign Credit Default Swaps (henceforth, CDS) and the stock markets of the main European countries. Thus, the goal of this paper is to test if the CDS premia can predict the stock market returns of the most...

In this paper we explore the dynamics of Implied Volatility Surfaces (IVS) both in a single-currency framework, and in the context of a global, integrated market. We construct a parametric function of “moneyness” and “time-to-maturity” factors that correspond to common shapes of IVS with...

This study constructs a Bayesian nonparametric model to investigate whether stock market returns predict real economic growth. Unlike earlier studies, our use of an infinite hidden Markov model enables parameters to be time-varying across an infinite number of Markov-switching states estimated...

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk,...