ML Anthology

Continuous-Time Markov-Switching GARCH Process with Robust State Path Identification and Volatility Estimation

Yinan Li, Fang Liu
ECML-PKDD 2021 pp. 370-387
doi:10.1007/978-3-030-86486-6_23 /ecmlpkdd/2021/li2021ecmlpkdd-continuoustime/

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.

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Cite

Text

Li and Liu. "Continuous-Time Markov-Switching GARCH Process with Robust State Path Identification and Volatility Estimation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021. doi:10.1007/978-3-030-86486-6_23

Markdown

[Li and Liu. "Continuous-Time Markov-Switching GARCH Process with Robust State Path Identification and Volatility Estimation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021.](https://mlanthology.org/ecmlpkdd/2021/li2021ecmlpkdd-continuoustime/) doi:10.1007/978-3-030-86486-6_23

BibTeX

@inproceedings{li2021ecmlpkdd-continuoustime,
  title     = {{Continuous-Time Markov-Switching GARCH Process with Robust State Path Identification and Volatility Estimation}},
  author    = {Li, Yinan and Liu, Fang},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2021},
  pages     = {370-387},
  doi       = {10.1007/978-3-030-86486-6_23},
  url       = {https://mlanthology.org/ecmlpkdd/2021/li2021ecmlpkdd-continuoustime/}
}