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

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

Internet

http://hdl.handle.net/20.500.11937/79404

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