Kalogeropoulos, Konstantinos and Roberts, Gareth O. and Dellaportas, Petros (2007): Inference for stochastic volatility model using time change transformations.
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Abstract
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 reparametrisation 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.
Item Type: | MPRA Paper |
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Original Title: | Inference for stochastic volatility model using time change transformations |
Language: | English |
Keywords: | Imputation, Markov chain Monte Carlo, Stochastic volatility |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General |
Item ID: | 5697 |
Depositing User: | Konstantinos Kalogeropoulos |
Date Deposited: | 10 Nov 2007 14:07 |
Last Modified: | 27 Sep 2019 16:49 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/5697 |