Observable or latent Markov chains for score-driven regime-switching volatility?
We study the statistical and forecasting performances of two regime-switching Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) models, i.e. observable-switching (OS) Beta-t-EGARCH and Markov-switching (MS) Beta-t-EGARCH. Both are non-path-dependent score-driven r...
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| Published in: | Applied economics Vol. 57; no. 52; pp. 8693 - 8709 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Routledge
08.11.2025
|
| Subjects: | |
| ISSN: | 0003-6846, 1466-4283 |
| Online Access: | Get full text |
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| Summary: | We study the statistical and forecasting performances of two regime-switching Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) models, i.e. observable-switching (OS) Beta-t-EGARCH and Markov-switching (MS) Beta-t-EGARCH. Both are non-path-dependent score-driven regime-switching volatility models, and their regime-switching specifications can be related to corresponding non-path-dependent Markov-switching GARCH (MS-GARCH) specifications. We present the estimation procedures for OS-Beta-t-EGARCH and MS-Beta-t-EGARCH. We use data on the weekly log-returns of the Standard & Poor's 500 (S&P 500) index and a random sample of 50 stocks from the S&P 500 from March 1986 to July 2024 (
$T = 2,000$
T
=
2
,
000
). The out-of-sample forecasting window is from May 2005 to July 2024 (
${T_f} = 1,000$
T
f
=
1
,
000
). We compare the in-sample statistical and out-of-sample density forecasting performances of Beta-t-EGARCH, OS-Beta-t-EGARCH, and MS-Beta-t-EGARCH. We find that the statistical and density forecasting performances of OS-Beta-t-EGARCH are superior to MS-Beta-t-EGARCH, motivating its practical use by investors and risk managers. |
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| ISSN: | 0003-6846 1466-4283 |
| DOI: | 10.1080/00036846.2024.2402094 |