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Value at Risk models with long memory features and their economic performance

Oberoi, Jaideep S and Mitrodima, Evangelia (2015) Value at Risk models with long memory features and their economic performance. Working paper. Social Science Research Network 10.2139/ssrn.2649348. (doi:10.2139/ssrn.2649348) (KAR id:50894)


We study alternative dynamics for Value at Risk (VaR) that incorporate a slow moving component and information on recent aggregate returns in established quantile (auto) regression models. These models are compared on their economic performance, and also on metrics of first-order importance such as violation ratios. By better economic performance, we mean that changes in the VaR forecasts should have a lower variance to reduce transaction costs and should lead to lower exceedance sizes without raising the average level of the VaR. We find that, in combination with a targeted estimation strategy, our proposed models lead to improved performance in both statistical and economic terms.

Item Type: Reports and Papers (Working paper)
DOI/Identification number: 10.2139/ssrn.2649348
Uncontrolled keywords: Long memory time series; Quantile forecasts; Conditional loss
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Jaideep Oberoi
Date Deposited: 13 Oct 2015 00:17 UTC
Last Modified: 08 Dec 2022 20:35 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Oberoi, Jaideep S.

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Mitrodima, Evangelia.

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