Shape-constrained semiparametric additive stochastic volatility models  

作　　者：Jiangyong Yin Peter F.Craigmile Xinyi Xu Steven MacEachern 

机构地区：[1]Department of Statistics,The Ohio State University,Columbus,OH,USA

出　　处：《Statistical Theory and Related Fields》2019年第1期71-82,共12页统计理论及其应用（英文）

基　　金：Peter Craigmile and Jiangyong Yin were supported in part by the National Science Foundation(NSF)under grant DMS-0906864;Xinyi Xu,Jiangyong Yin and Steven MacEachern were supported in part by the NSF under grant DMS-1209194;Peter Craigmile is additionally supported in part by the NSF under grants SES-1024709,DMS-1407604 and SES-1424481;the National Cancer Institute of the National Institutes of Health under Award Number 1R21CA212308-01;the project title is‘Evaluating how licensing-law strategies will change neighborhood disparities in tobacco retailer density’.Xinyi Xu and Steven MacEachern are supported under grant DMS-1613110.

摘　　要：Nonparametric stochastic volatility models,although providing great flexibility for modelling thevolatility equation,often fail to account for useful shape information.For example,a model maynot use the knowledge that the autoregressive component of the volatility equation is monotonically increasing as the lagged volatility increases.We propose a class of additive stochasticvolatility models that allow for different shape constraints and can incorporate the leverageeffect–asymmetric impact of positive and negative return shocks on volatilities.We developa Bayesian fitting algorithm and demonstrate model performance on simulated and empiricaldatasets.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 estimatevolatilities over general,unconstrained,nonparametric models.

关 键 词：Bayesian isotonic regression leverage effect Markov chain Monte Carlo nonlinear time series particle filter state space model 

分 类 号：O17[理学—数学]