Multiple-period market risk prediction under long memory: when VaR is higher than expected

Harald Kinateder (Business Administration and Economics, University of Passau, Passau, Germany)
Niklas Wagner (Business Administration and Economics, University of Passau, Passau, Germany)

Journal of Risk Finance

ISSN: 1526-5943

Publication date: 28 January 2014

Abstract

Purpose

The paper aims to model multiple-period market risk forecasts under long memory persistence in market volatility.

Design/methodology/approach

The paper proposes volatility forecasts based on a combination of the GARCH(1,1)-model with potentially fat-tailed and skewed innovations and a long memory specification of the slowly declining influence of past volatility shocks. As the square-root-of-time rule is known to be mis-specified, the GARCH setting of Drost and Nijman is used as benchmark model. The empirical study of equity market risk is based on daily returns during the period January 1975 to December 2010. The out-of-sample accuracy of VaR predictions is studied for 5, 10, 20 and 60 trading days.

Findings

The long memory scaling approach remarkably improves VaR forecasts for the longer horizons. This result is only in part due to higher predicted risk levels. Ex post calibration to equal unconditional VaR levels illustrates that the approach also enhances efficiency in allocating VaR capital through time.

Practical implications

The improved VaR forecasts show that one should account for long memory when calibrating risk models.

Originality/value

The paper models single-period returns rather than choosing the simpler approach of modeling lower-frequency multiple-period returns for long-run volatility forecasting. The approach considers long memory in volatility and has two main advantages: it yields a consistent set of volatility predictions for various horizons and VaR forecasting accuracy is improved.

Keywords

Citation

Kinateder, H. and Wagner, N. (2014), "Multiple-period market risk prediction under long memory: when VaR is higher than expected", Journal of Risk Finance, Vol. 15 No. 1, pp. 4-32. https://doi.org/10.1108/JRF-07-2013-0051

Download as .RIS

Publisher

:

Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

Please note you might not have access to this content

You may be able to access this content by login via Shibboleth, Open Athens or with your Emerald account.
If you would like to contact us about accessing this content, click the button and fill out the form.
To rent this content from Deepdyve, please click the button.