主 题: Shape-constrained Semiparametric Additive Stochastic Volatility Models
报告人: Xinyi Xu (Ohio State University)
时 间: 2015-04-02 14:30 - 15:30
地 点: 理科一号楼 1114
Abstract：The Gaussian stochastic process is the most commonly used approach for modeling time series data. The Gaussianity assumption, however, is known to be insufficient or inappropriate in many problems. On the other hand, nonparametric stochastic volatility models provide great flexibility for modeling the volatility equation, but they often fail to account for useful shape information. For example, a model may not use the knowledge that the autoregressive component of the volatility equation is monotonically increasing as the lagged volatility increases. In this work, we propose a class of additive stochastic volatility models, which capture the asymmetry and heavy tails of many real-world time series data and allow for different shape constraints to improve estimation efficiency. We develop a Bayesian fitting algorithm and demonstrate model performances on simulated and empirical datasets. Unlike general nonparametric models, our model sacrifices little when the true volatility equation is linear. In nonlinear situations we improve the model fit and the ability to estimate volatilities over general, unconstrained, nonparametric models.
About the speaker: Xinyi Xu is currently an Associate Professor at the Ohio State University. She obtained her BS degree from USTC in 2001, and obtained her Ph.D. degree from University of Pennsylvania in 2005. Her main research areas are Bayesian hierarchical modeling, model selection and model averaging, nonparametric Bayes, decision theory, and the applications of statistical methods in biostatistics, marketing, finance, etc.
