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A High-Low Model of Daily Stock Price Ranges

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Author Info
Yan-Leung Cheung (City University of Hong Kong)
Yin-Wong Cheung (University of California, Santa Cruz)
Alan T. K. Wan (City University of Hong Kong)

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Abstract

We observe that daily highs and lows of stock prices do not diverge over time and, hence, adopt the cointegration concept and the related vector error correction model (VECM) to model the daily high, the daily low, and the associated daily range data. The in-sample results attest the importance of incorporating high-low interactions in modeling the range variable. In evaluating the out-of-sample forecast performance using both mean-squared forecast error and direction of change criteria, it is found that the VECM-based low and high forecasts offer some advantages over some alternative forecasts. The VECM-based range forecasts, on the other hand, do not always dominate - the forecast rankings depend on the choice of evaluation criterion and the variables being forecasted.

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Publisher Info
Paper provided by Hong Kong Institute for Monetary Research in its series Working Papers with number 032009.

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Length: 42 pages
Date of creation: Jan 2009
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Handle: RePEc:hkm:wpaper:032009

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Related research
Keywords: Daily High; Daily Low; VECM Model; Forecast Performance; Implied Volatility;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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  1. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January. [Downloadable!] (restricted)
  2. Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model For Volatility Using Intra-Daily Data," Econometrics Working Papers Archive wp2003_07, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
    Other versions:
  3. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
  4. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November. [Downloadable!] (restricted)
    Other versions:
  5. Parkinson, Michael, 1977. "Option Pricing: The American Put," Journal of Business, University of Chicago Press, vol. 50(1), pages 21-36, January. [Downloadable!] (restricted)
  6. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," NBER Working Papers 11188, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  7. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-80, July.
  8. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-90, June. [Downloadable!] (restricted)
  9. Michael W. Brandt & Francis X. Diebold, 2001. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," PIER Working Paper Archive 03-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Apr 2003. [Downloadable!]
    Other versions:
  10. Chinn, Menzie D. & Meese, Richard A., 1995. "Banking on currency forecasts: How predictable is change in money?," Journal of International Economics, Elsevier, vol. 38(1-2), pages 161-178, February. [Downloadable!] (restricted)
  11. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June. [Downloadable!] (restricted)
  12. Beckers, Stan, 1983. "Variances of Security Price Returns Based on High, Low, and Closing Prices," Journal of Business, University of Chicago Press, vol. 56(1), pages 97-112, January. [Downloadable!] (restricted)
  13. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January. [Downloadable!] (restricted)
  14. Peter F. Christoffersen & Francis X. Diebold, 1997. "Cointegration and Long-Horizon Forecasting," NBER Technical Working Papers 0217, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  15. Kunitomo, Naoto, 1992. "Improving the Parkinson Method of Estimating Security Price Volatilities," Journal of Business, University of Chicago Press, vol. 65(2), pages 295-302, April. [Downloadable!] (restricted)
  16. Cheung, Yin-Wong & Wong, Clement Yuk-Pang, 2000. "A survey of market practitioners' views on exchange rate dynamics," Journal of International Economics, Elsevier, vol. 51(2), pages 401-419, August. [Downloadable!] (restricted)
  17. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June. [Downloadable!] (restricted)
  18. Ser-Huang Poon & Clive W. J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  19. Fernandes, Marcelo & de Sa Mota, Bernardo & Rocha, Guilherme, 2005. "A multivariate conditional autoregressive range model," Economics Letters, Elsevier, vol. 86(3), pages 435-440, March. [Downloadable!] (restricted)
  20. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November. [Downloadable!] (restricted)
  21. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-18, March.
  22. Inoue, Atsushi & Kilian, Lutz, 2002. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," CEPR Discussion Papers 3671, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  23. Abhyankar, Abhay & Sarno, Lucio & Valente, Giorgio, 2005. "Exchange rates and fundamentals: evidence on the economic value of predictability," Journal of International Economics, Elsevier, vol. 66(2), pages 325-348, July. [Downloadable!] (restricted)
    Other versions:
  24. Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 561-82, June.
  25. Yin-Wong Cheung, 2007. "An empirical model of daily highs and lows," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 1-20. [Downloadable!]
    Other versions:
  26. Yang, Dennis & Zhang, Qiang, 2000. "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices," Journal of Business, University of Chicago Press, vol. 73(3), pages 477-91, July. [Downloadable!] (restricted)
  27. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  28. Cheung, Yin-Wong & Chinn, Menzie David, 2001. "Currency traders and exchange rate dynamics: a survey of the US market," Journal of International Money and Finance, Elsevier, vol. 20(4), pages 439-471, August. [Downloadable!] (restricted)
    Other versions:
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