Market Risk Prediction under Long Memory: When VaR is Higher than Expected

Author(s):  
Harald Kinateder ◽  
Niklas F. Wagner

Author(s):  
Salim Ben Sassi ◽  
Azza Bejaoui

This chapter investigates the influence of the long memory behavior in returns and volatility on the market risk for four emerging stock markets during the pre- and post-crisis periods. In this respect, the authors consider four major political events (Tunisian revolution, Egyptian revolution, assassination of Prime Minister Rafik El Hariri, and a series of suicide bombings in Morocco). Using the modified R/S test and GPH test, they show the long memory property in returns and volatility over the two sub-periods. To explore the dual long memory property, the authors apply the joint ARFIMA–FIGARCH specification on the returns and volatility of the four emerging stock markets. The dual long memory property is prevalent in the returns and volatility of the emerging stock markets over the pre-crisis period. During the post-crisis period, the dual long memory process is only detected in the Moroccan market. The authors also display the dynamic behavior of VaR during the two sub-periods. In addition, based on the backtesting test, VaR performed better during the two sub-periods for all countries.



2014 ◽  
Vol 15 (1) ◽  
pp. 4-32 ◽  
Author(s):  
Harald Kinateder ◽  
Niklas Wagner

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.



2012 ◽  
Vol 45 (12) ◽  
pp. 10-11
Author(s):  
MARY ANN MOON
Keyword(s):  


1984 ◽  
Vol 29 (7) ◽  
pp. 576-577
Author(s):  
Leonard D. Stern
Keyword(s):  


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