Three Essays on Financial Econometrics.
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文摘
This thesis consists of three essays on financial econometrics. The first two essays are about multivariate density forecast evaluations. The third essay is on nonparametric Bayesian change-point VAR model. We develop a method for multivariate density forecast evaluations. The density forecast evaluation is based on checking uniformity and independence conditions of the probability integral transformation of the observed series in question. In the first essay,we propose a new method which is a location-adjusted version of Clements and Smith (2002) that corrects asymmetry problem and increases testing power. In the second essay,we develop a data-driven smooth test for multivariate density forecast evaluation and show some evidences on its finite sample performance using Monte Carlo simulations. Previous to our study,most of the works are up to bivariate model as it is difficult to evaluate with the existing methods. We propose an efficient dimensional reduction approach to reduce the dimension of multivariate density evaluation to a univariate one. We perform various Monte Carlo simulations and two applications on financial asset returns which show that our test performs well. The last essay proposes a nonparametric extension to existing Bayesian change-point model in a multivariate setting. Previous change-point model of Chib (1998) requires specification of the number of change points a priori. Hence a posterior model comparison is needed for different change-point models. We introduce the stick-breaking prior to the change-point process that allows us to endogenize the number of change points into the estimation procedure. Hence,the number of change points is simultaneously determined with other unknown parameters. Therefore our model is robust to model specification. We preform a Monte Carlo simulation of bivariate vector autoregressive VAR(2) process which is subject to four structural breaks. Our model estimate the break locations with high accuracy and the posterior estimates of the 65 parameters are closed to the true values. We apply our model to various hedge fund return processes and the detected change points coincide with market crashes.