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- W3200536741 abstract "We propose a continuous-time Markov-switching generalized autoregressive conditional heteroskedasticity (COMS-GARCH) process for handling irregularly spaced time series with multiple volatility states. We employ a Gibbs sampler in the Bayesian framework to estimate the COMS-GARCH model parameters, the latent state path and volatilities. To improve the computational efficiency and robustness of the identified state path and estimated volatilities, we propose a multi-path sampling scheme and incorporate the Bernoulli noise injection in the computational procedure. We provide theoretical justifications for the improved stability and robustness with the Bernoulli noise injection through the concept of ensemble learning and the low sensitivity of the objective function to external perturbation in the time series. The experiment results demonstrate that our proposed COMS-GARCH process and computational procedure are able to predict volatility regimes and volatilities in a time series with satisfactory accuracy." @default.
- W3200536741 created "2021-09-27" @default.
- W3200536741 creator A5000093771 @default.
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- W3200536741 date "2021-01-01" @default.
- W3200536741 modified "2023-10-16" @default.
- W3200536741 title "Continuous-Time Markov-Switching GARCH Process with Robust State Path Identification and Volatility Estimation" @default.
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- W3200536741 doi "https://doi.org/10.1007/978-3-030-86486-6_23" @default.
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