논문 해외 국제전문학술지(SCI급) Bayesian Inference for continuous time GARCH diffusion limit stochastic volatility models
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2022-09
- 저자명 박정민
- 학술지명 Journal of Economic Theory and Econometrics
- 발행처명 Korean Econometric Society
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 2
홈페이지논문 초록 (Abstract)
We propose a new Bayesian Markov chain Monte Carlo algorithm for continuous time GARCH diffusion limit stochastic volatility models and demonstrate the performance of our algorithm through simulation experiments and empirical analyses. Our algorithm exploits the normal distribution approximation of the posterior density of conditional variance using the one-step NewtonRaphson algorithm. Our algorithm can be applied not only to the continuous time GARCH diffusion limit stochastic volatility model but also to the continuous time stochastic volatility models of which the marginal probability density functions or the probability kernels of them are known. We present an empirical analysis results of the Feller square-root stochastic volatility model as well as the continuous time GARCH diffusion limit stochastic volatility model as an exhibition of the generality of our MCMC algorithm.