MCMC Based Estimation of Markov Switching CARR Model
碩士 === 國立交通大學 === 財務金融研究所 === 95 === It is well know that volatility plays an important role in finance. Chou (2005) has proposed the CARR (Conditional Autoregressive Range) model as an alternative volatility model. Markov Switching models are a promising way to capture nonlinearities in time series...
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| Format: | Others |
| Language: | en_US |
| Published: |
2007
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| Online Access: | http://ndltd.ncl.edu.tw/handle/53145126574468084719 |
| Summary: | 碩士 === 國立交通大學 === 財務金融研究所 === 95 === It is well know that volatility plays an important role in finance. Chou (2005) has proposed the CARR (Conditional Autoregressive Range) model as an alternative volatility model. Markov Switching models are a promising way to capture nonlinearities in time series. Combining the elements of Markov Switching models with CARR model poses severe difficulties for the computation of parameter estimators. Thus, we develop a Bayesian analysis for Markov Switching Conditional Autoregressive Range model (MS-CARR), bases on a Markov Chain Monte Carlo algorithm. The main motivation in this paper is to compare MS-CARR model and CARR model for the in-sample forecasting power by using the S&P500 index data. We show that MS-CARR model provide more accurate forecasts that the CARR model.
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