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Estimating parameters of a stochastic volatility model using the expectation-maximization algorithm coupled with a Gaussian particle filter |
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| รหัสดีโอไอ | |
| Creator | Tanit Malakorn |
| Title | Estimating parameters of a stochastic volatility model using the expectation-maximization algorithm coupled with a Gaussian particle filter |
| Contributor | Thanapat Iamtan |
| Publisher | Asia-Pacific Journal of Science and Technology |
| Publication Year | 2561 |
| Journal Title | Asia-Pacific Journal of Science and Technology |
| Journal Vol. | 23 |
| Journal No. | 4 |
| Page no. | 1-11 |
| Keyword | Maximum likelihood, Parameter estimation, Bootstrap filter,Daily exchange rates |
| URL Website | https://www.tci-thaijo.org/index.php/APST/index |
| Website title | https://tci-thaijo.org/index.php/APST/article/view/92442 |
| ISSN | 2539-6293 |
| Abstract | In this paper, the expectation-maximization algorithm coupled with a Gaussian particle filter for maximum likelihood parameter estimation of a stochastic volatility model is investigated. Two data sets are provided for demonstration purposes: simulated data and daily foreign exchange rates data. Simulation studies illustrate that the parameter estimate trajectories are likely to converge to the true ones. When comparing the empirical results obtained from the conventional method and the proposed method, it can be seen that the parameter estimates from both methods are consistent with each other; however, the computational time is considerably reduced when using the method presented here. |
