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Forecasting VaR using analytic higher moments for GARCH processes

Alexander, C., Lazar, E. ORCID: and Stanescu, S. (2013) Forecasting VaR using analytic higher moments for GARCH processes. International Review of Financial Analysis, 30. pp. 36-45. ISSN 1057-5219

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To link to this item DOI: 10.1016/j.irfa.2013.05.006


It is widely accepted that some of the most accurate Value-at-Risk (VaR) estimates are based on an appropriately specified GARCH process. But when the forecast horizon is greater than the frequency of the GARCH model, such predictions have typically required time-consuming simulations of the aggregated returns distributions. This paper shows that fast, quasi-analytic GARCH VaR calculations can be based on new formulae for the first four moments of aggregated GARCH returns. Our extensive empirical study compares the Cornish–Fisher expansion with the Johnson SU distribution for fitting distributions to analytic moments of normal and Student t, symmetric and asymmetric (GJR) GARCH processes to returns data on different financial assets, for the purpose of deriving accurate GARCH VaR forecasts over multiple horizons and significance levels.

Item Type:Article
Divisions:Henley Business School > ICMA Centre
ID Code:33049

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