---
title: Volatility Dynamics in Regime-Switching Models
url: https://www.ml-quant.com/papers/ssrn/4987141/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4987141
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4987141
featured: 2024-10-17
citations: unknown
topic: Derivatives & Volatility
---


# Volatility Dynamics in Regime-Switching Models

The study contrasts the forecasting performances of OSBetatEGARCH and MSBetatEGARCH in the US stock market, proving OSBetatEGARCH's superiority.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4987141
- Identifier: SSRN 4987141
- Released: 2024-10-14
- First featured: Quant Letter No. 70 (2024-10-17): https://www.ml-quant.com/issues/2024-10-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Volatility modeling in a Markovian environment: Two Ornstein-Uhlenbeck-related approaches](https://www.ml-quant.com/papers/arxiv/2407.05866/): The COGARCH and Barndorff-Nielsen and Shephard models are adapted to a Markov-switching environment, allowing for sudden volatility jumps at regime switch times and capturing key characteristics of financial time-series.
- [Consistent asset modelling with random coefficients and switches between regimes](https://www.ml-quant.com/papers/arxiv/2401.09955/): A stochastic model is examined that incorporates external influences and uncertainty in the parametrization of stochastic dynamics, using a Markov-modulated approach for regime switching, applied to option pricing in a numerical experiment.
- [Asset Price Volatility and Recessions](https://www.ml-quant.com/papers/ssrn/5146210/): The paper introduces a method for analyzing the relationship between stock market volatility and output growth, using a bivariate Markov switching model on a selection of developed countries.
- [Quantum Estimation of Volatility Models](https://www.ml-quant.com/papers/ssrn/5274549/): The paper introduces two new methods for estimating stochastic volatility diffusions using Quantum-Inspired Classical Hidden Markov Models and Quantum Hidden Markov Models.
- [Volatility and Regime Switching in Sustainable Indices](https://www.ml-quant.com/papers/ssrn/4579948/): The performance and volatility of sustainable indices are influenced by regime-switching, with positive shocks impacting volatility differently than negative ones, as demonstrated by a study of major sustainable indices from 2009 to 2017.
- [Duration Dependent Volatility Models with Value-weighted Approach](https://www.ml-quant.com/papers/ssrn/4570144/): Using different duration values in a Markov-switching model can improve the prediction of bitcoin returns, outperforming GARCH-type models.