A dynamic Markov regime-switching GARCH model and its cumulative impulse response function
- Authors
- Kim, Yujin; Hwang, Eunju
- Issue Date
- Aug-2018
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- Regime-switching GARCH process; Volatility; Forecasting; Cumulative impulse response
- Citation
- STATISTICS & PROBABILITY LETTERS, v.139, pp.20 - 30
- Journal Title
- STATISTICS & PROBABILITY LETTERS
- Volume
- 139
- Start Page
- 20
- End Page
- 30
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/3493
- DOI
- 10.1016/j.spl.2018.02.059
- ISSN
- 0167-7152
- Abstract
- This paper concerns with a generalized regime-switching GARCH model to capture dynamic behavior of volatility in financial market. Four-state Markov chain regime-switching is adopted with white noise, stationary, integrated and explosive states. We consider time-dependent transition probabilities of the Markov chain and derive time-dependent probability of each state under the assumption of conditional normality on the noise of the GARCH model. Multi-step ahead volatility is formulated and cumulative impulse response function, which is a measure of persistence in volatility, is discussed. A Monte Carlo experiment shows the dynamics of the volatilities and time-dependent probabilities as well as the behaviors of the cumulative impulse response functions. (C) 2018 Elsevier B.V. All rights reserved.
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