Modelling and Forecasting Intraday Market Risk with Application to Stock Indices

25 Pages Posted: 16 Feb 2014

See all articles by Abhay Kumar Singh

Abhay Kumar Singh

Edith Cowan University

David E. Allen

School of Mathematics and Statistics, The University of Sydney; Financial Research Network (FIRN); Department of Finance; School of Business and Law, Edith Cowan University

Robert J. Powell

Edith Cowan University - School of Accounting, Finance and Economics; Financial Research Network (FIRN)

Date Written: February 13, 2014

Abstract

On the afternoon of May 6, 2010 the Dow Jones Industrial Average (DJIA) plunged about 1000 points (about 9%) in a matter of minutes before rebounding almost as quickly. This was the biggest one day point decline on an intraday basis in the DJIA's history. An almost similar dramatic change in intraday volatility was observed on April 4, 2000 when the DJIA dropped by 4.8%. These historical events present a very compelling argument for the need for robust econometrics models which can forecast intraday asset volatility. There are numerous models available in the finance literature to model financial asset volatility. Various Autoregressive Conditional Heteroskedastic (ARCH) time series models are widely used for modelling daily (end of day) volatility of the financial assets. The family of basic GARCH models works well for modelling daily volatility but they are proven to be not as efficient for intraday volatility. The last two decades have seen some research augmenting the GARCH family of models to forecast intraday volatility, the Multiplicative Component GARCH (MCGARCH) model of Engle & Sokalska (2012) being the most recent of them. MCGARCH models the conditional variance as the multiplicative product of daily, diurnal, and stochastic intraday volatility of the financial asset. In this paper we use the MCGARCH model to forecast the intraday volatility of Australia's S&P/ASX-50 stock market index and the USA Dow Jones Industrial Average (DJIA) stock market index. We also use the model to forecast their intraday Value at Risk (VaR) and Expected Shortfall (ES). As the model requires a daily volatility component, we test a GARCH based estimate of the daily volatility component against the daily realized volatility (RV) estimates obtained from the Heterogeneous Autoregressive model for Realized Volatility (HARRV). The results in the paper show that 1 minute VaR forecasts obtained from the MCGARCH model using the HARRV based daily volatility component outperform the ones obtained using the GARCH based daily volatility component.

Keywords: Intraday returns, VaR, Expected Shortfall, GARCH, realized variance

Suggested Citation

Singh, Abhay Kumar and Allen, David Edmund and Powell, Robert J., Modelling and Forecasting Intraday Market Risk with Application to Stock Indices (February 13, 2014). Available at SSRN: https://ssrn.com/abstract=2395610 or http://dx.doi.org/10.2139/ssrn.2395610

Abhay Kumar Singh (Contact Author)

Edith Cowan University ( email )

Joondalup Drive
Perth
Joondalup, WA 6027
Australia

David Edmund Allen

School of Mathematics and Statistics, The University of Sydney ( email )

School of Mathematics and Statistics F07
University of Sydney
Sydney, New South Wales 2006
Australia

HOME PAGE: http://www.maths.usyd.edu.au

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Department of Finance ( email )

Taiwan
Taiwan

School of Business and Law, Edith Cowan University

100 Joondalup Drive
Joondalup, WA 6027
Australia

HOME PAGE: http://www.dallenwapty.com

Robert J. Powell

Edith Cowan University - School of Accounting, Finance and Economics ( email )

Joondalup Campus
Perth
Joondalup 6027, WA
Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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