Three essays on financial econometrics.
详细信息
文摘
This dissertation has three chapters on financial econometrics and its applications in finance.;The first chapter provides closed-form likelihood approximations for multivariate jump-diffusion processes widely used in finance. The maximum-likelihood estimator (MLE) computed from this approximate likelihood achieves the asymptotic efficiency of the true yet uncomputable MLE estimator. The approximation, based on Kolmogorov equations common to Markov processes, can be generalized beyond jump-diffusions. This method is then used to uncover the realignment probability of the Chinese Yuan. Since February 2002, the realignment intensity implicit in the financial market has increased fivefold. The term structure of the forward realignment rate, which completely characterizes realignment probabilities in the future, is hump-shaped and peaks at six months from the end of 2003. The realignment probability responds quickly to news releases on the Sino-US trade surplus, state-owned enterprise reform, Chinese government tax revenue and, most importantly, government interventions.;The second chapter, co-authored with my advisor Yacine Ait-Sahalia, proposes saddlepoint expansions as a means to generate closed-form approximations to the transition densities and cumulative distribution functions of Markov processes. This method is applicable to a large class of models considered in finance, for which a Laplace or characteristic functions, but not the transition density, can be found in closed form. But even when such a computation is not possible explicitly, we go one step further by showing how useful approximations can be obtained by replacing the Laplace or characteristic functions by an expansion in small time. This chapter is under revision with intention to accept at the Journal of Econometrics.;The third chapter revisits state price estimation and proposes an estimator that explicitly accounts for possible model misspecification. The estimator approaches the parametric efficiency when a good model is available. It achieves nonparametric robustness even if a model is poor. Simulations confirm its proposed properties.