This paper derives the moments for a range of Markov switching models. We characterise in detail the patterns of volatility, skewness and kurtosis that these models can produce as a function of the transition probability and parameters of the underlying state densities entering the switching...

This paper applies an extended and generalised version of the recursive modelling strategy developed in Persaran and Timmermann (1995) to the UK stock market. The focus of the analysis is to simulate investors search in in real time for a model that can forecast stock returns. It demonstrates...

This paper presents new empirical evidence on the existence of structural breaks in the fundamentals process underlying US stock prices and develops an asset pricing model which considers the possibility of such breaks. Three break points are identified: The Great Depression, World War II, and...

We consider performance measurement and evaluation for managed funds. Similarities and differences−both in econometric practice and in interpretation of outcomes of empirical tests−between performance measurement and conventional asset pricing models are analyzed. We also discuss how...

Markov switching models with time-varying means, variances and mixing weights are applied to characterize business cycle variation in the probability distribution and higher order moments of stock returns. This allows us to provide a comprehensive characterization of risk that goes well beyond...

Economics is primarily a non-experimental science. Typically, we cannot generate new data sets on which to test hypotheses independently of the data that may have led to a particular theory. The common practice of using the same data set to formulate and test hypotheses introduces data-snooping...

This paper shows that many of the empirical biases of the Black and Scholes option pricing model can be explained by Bayesian learning effects. In the context of an equilibrium model where dividend news evolve on a binomial lattice with unknown but recursively updated probabilities we derive...