Measuring risk in the stock market context is one of the key challenges of modern finance. Despite of the substantial significance of the topic to investors and market regulators, there is a controversy over what risk factors should be used to price the assets or to determine the cost of...

This paper presents theoretical models and their empirical results for the return and variance dynamics of German stocks. A factor structure is used in order to allow for a parsimonious modeling of the first two moments of returns. Dynamic factor models with GARCH dynamics (GARCH(1,1)-M,...

Investors in equilibrium are modeled as facing investor specific risks across the space of assets. Personalized asset pricing models reflect these risks. Averaging across the pool of investors we obtain a market asset pricing model that reflects market risk exposures. It is observed on invoking...

Maximum likelihood estimation of discretely observed diffusion processes is mostly hampered by the lack of a closed form solution of the transient density. It has recently been argued that a most generic remedy to this problem is the numerical solution of the pertinent Fokker-Planck (FP) or...

We study the dynamics of a Lucas-tree model with finitely lived agents who "learn from experience." Individuals update expectations by Bayesian learning based on observations from their own lifetimes. In this model, the stock price exhibits stochastic boom-and-bust fluctuations around the...

We present an empirical study focusing on the estimation of a fundamental multi-factor model for a universe of European stocks. Following the approach of the BARRA model, we have adopted a cross-sectional methodology. The proportion of explained variance ranges from 7.3% to 66.3% in the weekly...