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INFERENCE FOR STOCHASTIC VOLATILITY MODELS USING TIME CHANGE TRANSFORMATIONS
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DOI：10.1214/09-AOS702.png)
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摘要
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We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrization defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates.
Keyword:
Imputation
Markov chain Monte Carlo
diffusion processes
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Maximum likelihood estimation of discretely sampled diffusions:: A closed-form approximation approach离散采样扩散的最大似然估计:: 一种封闭形式的近似方法.png)
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