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Analysis of asymmetric GARCH volatility models with applications to margin measurement
Gespeichert in:
| Personen und Körperschaften: | , |
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| Weitere Verfasser: | Shen, Xiangjin [VerfasserIn] |
| Format: | E-Book |
| Sprache: | Englisch |
| veröffentlicht: |
[Ottawa]
Bank of Canada
[2018]
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| Gesamtaufnahme: |
Bank of Canada: Staff working paper ; 2018, 21 (May 2018)
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| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
| Anmerkungen: | Zusammenfassung in französischer Sprache |
| Zusammenfassung: | We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms. Based on maximum likelihood estimation of S&P 500 returns, S&P/TSX returns and Monte Carlo numerical example, we find that the proposed more general asymmetric volatility model has better fit, higher persistence of negative news, higher degree of risk aversion and significant effects of macroeconomic variables on the lowfrequency volatility component. We then apply a variety of volatility models in setting initial margin requirements for a central clearing counterparty (CCP). Finally, we show how to mitigate procyclicality of initial margins using a three-regime threshold autoregressive model. |
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| Umfang: | 1 Online-Ressource (circa 58 Seiten); Illustrationen |
| Anmerkungen: | Zusammenfassung in französischer Sprache |