Can multivariate GARCH models really improve value-at-risk forecasts?

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Can multivariate GARCH models really improve value-at-risk forecasts?

© 2020 Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015. All rights reserved. This paper evaluates the performance of multivariate conditional volatility models in forecasting Value-at-Risk (VaR). The paper considers the Constant Conditional Correlation (CCC) model...

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Bibliographic Details
Main Authors:	Sia, C.S., Chan, Felix
Other Authors:	Weber, T
Format:	Conference Paper
Language:	English
Published:	MODELLING & SIMULATION SOC AUSTRALIA & NEW ZEALAND INC 2015
Subjects:	Science & Technology Technology Physical Sciences Computer Science, Interdisciplinary Applications Operations Research & Management Science Mathematics, Applied Computer Science Mathematics Value-at-Risk (VaR) Multivariate GARCH AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY ASYMPTOTIC THEORY GENERALIZED ARCH EXCHANGE-RATES VOLATILITY HETEROSKEDASTICITY BANKS
Online Access:	http://hdl.handle.net/20.500.11937/79404

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