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学术讲座:Stochastic tail index model for high frequency financial data with Bayesian analysis

讲座题目:Stochastic tail index model for high frequency financial data with Bayesian analysis

主讲人:卯光宇

时间:6月19日下午1:30-2:30

地点:博学925

主讲人简介:

卯光宇,北京大学博士,北京交通大学经管学院金融系副教授。研究兴趣是计量和统计理论,以及相关的金融、宏观经验研究。曾在《Journal of Econometrics》、《Econometric Reviews》、《Econometrics Journal》等期刊上发表过论文。此外,他还是《Journal of Econometrics》、《Biometrika》、《Journal of Business and Economic Statistics》等期刊的匿名审稿人。

讲座摘要:

This paper proposes a new dynamic model called Stochastic Tail Index (STI) model to analyze time-varying tail index for financial asset using high frequency return data. Bayesian tools are developed to estimate the model, make related inferences, and perform model selection. To construct efficient posterior sampler for the STI model by an approximation approach, a new algorithm called ALSO (Auxiliary Least Squares Optimization) is introduced, which can quickly make sufficient approximation to a given random variable using Gaussian mixture variables. The posterior sampler takes advantages of the BFGS optimization method to tailor the proposal densities in Metropolis–Hastings chains, and is computationally faster than the existing samplers in literature. Simulation shows that the proposed posterior sampler works well for the STI model. To illustrate the use of the STI model in the real world, we analyze two real high frequency data sets associated with two markets. It is found that the estimated daily tail indexes generally follow a time-varying pattern and tend to fall when large negative events occur. Besides, they significantly drop below 2 during some periods, which implies that the variances of the return distributions during those periods may be infinite, and hence any variance-based risk management for the two markets may be questionable.