Essays in empirical asset pricing

Daniel Robert Smith
2002
This thesis consists of two essays which contribute to different but related aspects of the empirical asset pricing literature. The common theme is that incorrect restrictions can lead to inaccurate decisions. The first essay demonstrates that failure to account for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness of short-term interest rate volatility, while the second essay demonstrates that we need to incorporate skewness to develop models that
more » ... models that adequately account for the cross-section of equity returns. Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. We find that either Markov-switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this paper is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. Essay 2 proposes a new approach to estimating and testing nonlinear pricing models using GMM. The methodology extends the GMM based conditional mean-variance asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over moments higher than variance. In particular we explore the empirical usefulness of the conditional coskewness of an assets return with the market return in explaining the cross-section of equity returns. The methodology is both flexible and parsimonious. We avoid modelling any asset specific parameters and avoid making restrictive assumptions on the dynamics of co-moments. By using GMM to estimate the models' parameters we also avoid makin [...]
doi:10.14288/1.0090704 fatcat:ianq6e2eqbf2taelr2nyfa3nqy