Volatility modelling in a Markov-switching environment: two Ornstein–Uhlenbeck-related approaches

Abstract We introduce generalisations of the COGARCH model of Klüppelberg et al. (J. Appl. Probab. 41:601–622 2004) and of the volatility and price model of Barndorff-Nielsen and Shephard (J. R. Stat. Soc., Ser. B Stat. Methodol. 63:167–241 2001) to a Markov-switching environment. These generalisations incorporate exogenous jumps of the volatility at the times of a regime switch. Both models are studied within the framework of Markov-modulated generalised Ornstein–Uhlenbeck processes which allows deriving conditions for stationarity, formulas for moments as well as the autocovariance structure of volatility and price process. It turns out that both models inherit various properties of the original models and therefore are able to capture basic stylised facts of financial time series such as uncorrelated log-returns, correlated squared log-returns and non-existence of higher moments in the COGARCH case.

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Publication Details

Journal
Finance and Stochastics
Published
2025-06-16
DOI
https://doi.org/10.1007/s00780-025-00567-3
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Volatility modelling in a Markov-switching environment: two Ornstein–Uhlenbeck-related approaches

Anita Behme
Finance and Stochastics
Stochastic processes and financial applications
article

Volatility modelling in a Markov-switching environment: two Ornstein–Uhlenbeck-related approaches

Anita Behme
article en

Abstract

Abstract We introduce generalisations of the COGARCH model of Klüppelberg et al. (J. Appl. Probab. 41:601–622 2004) and of the volatility and price model of Barndorff-Nielsen and Shephard (J. R. Stat. Soc., Ser. B Stat. Methodol. 63:167–241 2001) to a Markov-switching environment. These generalisations incorporate exogenous jumps of the volatility at the times of a regime switch. Both models are studied within the framework of Markov-modulated generalised Ornstein–Uhlenbeck processes which allows deriving conditions for stationarity, formulas for moments as well as the autocovariance structure of volatility and price process. It turns out that both models inherit various properties of the original models and therefore are able to capture basic stylised facts of financial time series such as uncorrelated log-returns, correlated squared log-returns and non-existence of higher moments in the COGARCH case.

Finance and StochasticsVol. 29(4)
Technische Universität Dresden (DE)
Technische Universität Dresden
Openalex Percentile: Top 82%
Stochastic processes and financial applications
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