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Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

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  • Liu, Hung-Chun
  • Chiang, Shu-Mei
  • Cheng, Nick Ying-Pin
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    Abstract

    We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Review of Economics & Finance.

    Volume (Year): 22 (2012)
    Issue (Month): 1 ()
    Pages: 78-91

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    Handle: RePEc:eee:reveco:v:22:y:2012:i:1:p:78-91

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    Web page: http://www.elsevier.com/locate/inca/620165

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    Keywords: SPDRs; GARCH; Realized volatility; Realized range; Value-at-risk;

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    1. Gutierrez, Jose A. & Martinez, Valeria & Tse, Yiuman, 2009. "Where does return and volatility come from? The case of Asian ETFs," International Review of Economics & Finance, Elsevier, Elsevier, vol. 18(4), pages 671-679, October.
    2. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, Elsevier, vol. 138(1), pages 181-207, May.
    3. Twm Evans & David McMillan, 2007. "Volatility forecasts: the role of asymmetric and long-memory dynamics and regional evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 17(17), pages 1421-1430.
    4. Panayiotis Theodossiou, 1998. "Financial Data and the Skewed Generalized T Distribution," Management Science, INFORMS, INFORMS, vol. 44(12-Part-1), pages 1650-1661, December.
    5. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 74(1), pages 3-30, September.
    6. Tse, Yiuman & Martinez, Valeria, 2007. "Price discovery and informational efficiency of international iShares funds," Global Finance Journal, Elsevier, vol. 18(1), pages 1-15.
    7. Geoffrey F. Loudon & Wing H. Watt & Pradeep K. Yadav, 2000. "An empirical analysis of alternative parametric ARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 15(2), pages 117-136.
    8. Alan E. H. Speight & David G. McMillan, 2004. "Daily volatility forecasts: reassessing the performance of GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 449-460.
    9. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, Econometric Society, vol. 59(2), pages 347-70, March.
    10. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    11. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 31(3), pages 307-327, April.
    12. David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
    13. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
    14. Fuertes, Ana-Maria & Izzeldin, Marwan & Kalotychou, Elena, 2009. "On forecasting daily stock volatility: The role of intraday information and market conditions," International Journal of Forecasting, Elsevier, Elsevier, vol. 25(2), pages 259-281.
    15. Kwiatkowski, D. & Phillips, P.C.B. & Schmidt, P., 1990. "Testing the Null Hypothesis of Stationarity Against the Alternative of Unit Root : How Sure are we that Economic Time Series have a Unit Root?," Papers, Michigan State - Econometrics and Economic Theory 8905, Michigan State - Econometrics and Economic Theory.
    16. Balaban, Ercan, 2004. "Comparative forecasting performance of symmetric and asymmetric conditional volatility models of an exchange rate," Economics Letters, Elsevier, vol. 83(1), pages 99-105, April.
    17. I.-Yuan Chuang & Jin-Ray Lu & Pei-Hsuan Lee, 2007. "Forecasting volatility in the financial markets: a comparison of alternative distributional assumptions," Applied Financial Economics, Taylor & Francis Journals, vol. 17(13), pages 1051-1060.
    18. Bystrom, Hans N. E., 2005. "Extreme value theory and extremely large electricity price changes," International Review of Economics & Finance, Elsevier, Elsevier, vol. 14(1), pages 41-55.
    19. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 23, pages 365-380, October.
    20. Harper, Joel T. & Madura, Jeff & Schnusenberg, Oliver, 2006. "Performance comparison between exchange-traded funds and closed-end country funds," Journal of International Financial Markets, Institutions and Money, Elsevier, Elsevier, vol. 16(2), pages 104-122, April.
    21. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, Elsevier, vol. 7(3-4), pages 271-300, November.
    22. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    23. Timotheos Angelidis & Stavros Degiannakis, 2008. "Forecasting one-day-ahead VaR and intra-day realized volatility in the Athens Stock Exchange Market," Managerial Finance, Emerald Group Publishing, Emerald Group Publishing, vol. 34(7), pages 489-497.
    24. Eugenie Hol & Siem Jan Koopman & Borus Jungbacker, 2004. "Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements," Computing in Economics and Finance 2004, Society for Computational Economics 342, Society for Computational Economics.
    25. Weixian Wei, 2002. "Forecasting stock market volatility with non-linear GARCH models: a case for China," Applied Economics Letters, Taylor & Francis Journals, vol. 9(3), pages 163-166.
    26. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, Elsevier, vol. 129(1-2), pages 121-138.
    27. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358.
    28. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    29. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, Elsevier, vol. 28(4), pages 467-488, July.
    30. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(2), pages 283-300.
    31. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, Elsevier, vol. 20(2), pages 273-286.
    32. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
    33. Leeves, Gareth, 2007. "Asymmetric volatility of stock returns during the Asian crisis: Evidence from Indonesia," International Review of Economics & Finance, Elsevier, Elsevier, vol. 16(2), pages 272-286.
    34. Imtiaz Mazumder, M. & Chu, Ting-Heng & Miller, Edward M. & Prather, Larry J., 2008. "International day-of-the-week effects: An empirical examination of iShares," International Review of Financial Analysis, Elsevier, vol. 17(4), pages 699-715, September.
    35. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 95-24, Board of Governors of the Federal Reserve System (U.S.).
    36. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 1-46 National Bureau of Economic Research, Inc.
    37. Alexander, C. & Barbosa, A., 2008. "Hedging index exchange traded funds," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 326-337, February.
    38. Charles Corrado & Cameron Truong, 2007. "Forecasting Stock Index Volatility: Comparing Implied Volatility And The Intraday High-Low Price Range," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 30(2), pages 201-215.
    39. Olienyk, John P. & Schwebach, Robert G. & Kenton Zumwalt, J., 1999. "WEBS, SPDRs, and country funds: an analysis of international cointegration," Journal of Multinational Financial Management, Elsevier, Elsevier, vol. 9(3-4), pages 217-232, November.
    40. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
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