The Role of High-Frequency Prices, Long Memory and Jumps for Value-at-Risk Prediction

34 Pages Posted: 29 May 2012

See all articles by Ana-Maria Fuertes

Ana-Maria Fuertes

Cass Business School, City University of London

Jose Olmo

Universidad de Zaragoza; University of Southampton

Date Written: May 29, 2012


This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight returns, are confronted with those from ARFIMA realized volatility models. The out-of-sample evaluation is based on a novel difference-in-proportions test that exploits the frequency of individual VaR rejections and a block-bootstrap unconditional coverage test that is robust to estimation uncertainty and model risk. ARFIMA models produce better backtesting results than GARCH models but fare worse in terms of independence of the hits sequence. Encompassing tests further suggest that GARCH and ARFIMA models can be fruitfully combined to produce more competitive VaR measures. We find evidence that intraday jumps also have forecasting potential. The techniques are illustrated for a small portfolio of large-cap stocks.

Keywords: Encompassing, High-frequency data, Model uncertainty, Realized volatility, Risk management

JEL Classification: C52, C53, G15

Suggested Citation

Fuertes, Ana-Maria and Olmo, Jose, The Role of High-Frequency Prices, Long Memory and Jumps for Value-at-Risk Prediction (May 29, 2012). Available at SSRN: or

Ana-Maria Fuertes (Contact Author)

Cass Business School, City University of London ( email )

Faculty of Finance
106 Bunhill Row
London, EC1Y 8TZ
United Kingdom
+44 207 477 0186 (Phone)
+44 207 477 8881 (Fax)


Jose Olmo

Universidad de Zaragoza ( email )

Gran Via, 2
50005 Zaragoza, Zaragoza 50005

University of Southampton ( email )

United Kingdom

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