What question did this study set out to answer?

The research aims to enhance the estimation of Markov-switching GARCH models by developing a robust estimator that can handle outliers in financial data.

July 30, 2026Open Access

Robust estimation of Markov-switching GARCH models

JDJean S. M. DinizUniversidade Estadual de Campinas (UNICAMP)LHLuiz Koodi HottaUniversidade Estadual de Campinas (UNICAMP)

Key Points

The research aims to enhance the estimation of Markov-switching GARCH models by developing a robust estimator that can handle outliers in financial data.
Proposed a robust estimator for Markov-switching GARCH models.
Conducted simulations to assess performance under data contamination.
Applied the estimator to empirical data to compare performance with QML-t estimator.
The robust estimator demonstrates competitiveness against conventional estimators under contamination.
It shows no uniform superiority over the QML-t estimator while providing advantages in certain situations.

Abstract

Abstract The study of volatility is important in several areas of finance, and GARCH models have been widely used in the literature due to their ability to capture key stylised facts of financial time series. However, in some cases, financial time series exhibit structural changes in volatility dynamics, for which standard GARCH models may be inadequate; in such situations, Markov-switching GARCH models provide a more suitable framework. On the other hand, outliers are often present in empirical data. This paper shows that conventional estimators can be strongly affected by outliers and proposes an estimator that is more robust to their presence. The simulation results and empirical applications indicate that the proposed robust estimator is competitive and useful under contamination, but not uniformly superior to the QML-t estimator.

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