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Modelling daily value-at-risk using realized volatility and arch type models

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Author Info
Giot,Pierre
Laurent,Sebastien (METEOR)

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Abstract

In this paper we show how to compute a daily VaR measure for two stock indexes (CAC40 and SP500) using the one-day-ahead forecast of the daily realized volatility. The daily re-alized volatility is equal to the sum of the squared intraday returns over a given day and thus uses intraday information to define an aggregated daily volatility measure. While the VaR specification based on an ARFIMAX(0,d,1)-skewed Student model for the daily realized volatility provides adequate one-day-ahead VaR forecasts, it does not really improve on the performance of a VaR model based on the skewed Student APARCH model and estimated using daily data. Thus, for the two financial assets considered in an univariate framework, both methods seem to be equivalent. This paper also shows that daily returns standardized by the square root of the one-day-ahead forecast of the daily realized volatility are not normally distributed.

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Paper provided by Maastricht : METEOR, Maastricht Research School of Economics of Technology and Organization in its series Research Memoranda with number 014.

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Date of creation: 2001
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Handle: RePEc:dgr:umamet:2001014

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Keywords: mathematical economics;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
  2. Georges Dionne & Pierre Duchesne & Maria Pacurar, 2005. "Intraday Value at Risk (IVaR) Using Tick-by-Tick Data with Application to the Toronto Stock Exchange," Cahiers de recherche 0533, CIRPEE. [Downloadable!]
  3. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute. [Downloadable!]
    Other versions:
  4. BELTRAN, Helena & DURRE, Alain & GIOT, Pierre, 2005. "Volatility regimes and the provision of liquidity in order book markets," CORE Discussion Papers 2005012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
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  5. Lima, Luiz Renato Regis de Oliveira & Neri, Breno de Andrade Pinheiro, 2006. "Comparing Value-at-Risk Methodologies," Economics Working Papers (Ensaios Economicos da EPGE) 629, Graduate School of Economics, Getulio Vargas Foundation (Brazil). [Downloadable!]
    Other versions:
  6. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics. [Downloadable!]
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  7. Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Series Working Papers 438, University of Oxford, Department of Economics. [Downloadable!]
  8. BAUWENS, Luc & BEN OMRANE, Walid, 2003. "News annoucements, market activity and volatility in the Euro/Dollar foreign exchange market," CORE Discussion Papers 2003029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
    Other versions:
  9. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics. [Downloadable!]
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  10. Ivana Komunjer, 2007. "Asymmetric power distribution: Theory and applications to risk measurement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 891-921. [Downloadable!]
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  11. Jouchi Nakajima, 2008. "EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns," IMES Discussion Paper Series 08-E-23, Institute for Monetary and Economic Studies, Bank of Japan. [Downloadable!]
  12. Helena Beltran & Alain Durré & Pierre Giot, 2004. "How does liquidity react to stress periods in a limit order market?," Research series 200405-5, National Bank of Belgium. [Downloadable!]
  13. Wolfgang Härdle & Julius Mungo, 2008. "Value-at-Risk and Expected Shortfall when there is long range dependence," SFB 649 Discussion Papers SFB649DP2008-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
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  15. RENGIFO, Erick & ROMBOUTS, Jeroen, 2004. "Dynamic optimal portfolio selection in a VaR framework," CORE Discussion Papers 2004057, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
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  16. Stavros Degiannakis & Evdokia Xekalaki, 2007. "Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models," Applied Financial Economics, Taylor and Francis Journals, vol. 17(2), pages 149-171, January. [Downloadable!] (restricted)
  17. Chien-Liang Chiu & Ming-Chih Lee & Jui-Cheng Hung, 2005. "Estimation of Value-at-Risk under jump dynamics and asymmetric information," Applied Financial Economics, Taylor and Francis Journals, vol. 15(15), pages 1095-1106, October. [Downloadable!] (restricted)
  18. Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," OFRC Working Papers Series 2009fe02, Oxford Financial Research Centre. [Downloadable!]
  19. Christian T. Brownlees & Giampiero Gallo, 2007. "Volatility Forecasting Using Explanatory Variables and Focused Selection Criteria," Econometrics Working Papers Archive wp2007_04, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
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  23. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies. [Downloadable!]
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