報告者：Suleyman Taspinar (Queens College, City University of New York)

日時：7月19日（金）17：15－18：45

会場：2号館11階 経済学部会議室B

使用言語：英語

幹事： 長谷部　拓也

タイトル：Bayesian Inference for Spatial Stochastic Volatility Models

要旨：In this study, we consider a spatial stochastic volatility model in which latent log-volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the log-volatility terms introduces spatial dependence in the outcome equation. We develop an MCMC algorithm in which the latent log-volatility terms are considered as additional parameters to facilitate the posterior simulation. To introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation approach, we transform the model such that the outcome equation takes the form of log-squared terms. We approximate the distribution of the log-squared error terms in the outcome equation with a finite mixture of normal distributions such that the transformed model turns into a linear Gaussian state-space model. Our simulation results indicate that the Bayesian estimator has satisfactory finite sample properties. We investigate empirical validity of our specification using price returns of residential properties in broader Chicago Metropolitan area.

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