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An Empirical Model of Daily Highs and Lows

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  • Yin-wong Cheung

    (University of California, Santa Cruz)

Abstract

We construct an empirical model for daily highs and daily lows of US stock indexes based on the intuition that highs and lows do not drift apart over time. Our empirical results show that daily highs and lows of three main US stock price indexes are cointegrated. Data on openings, closings, and trading volume are found to offer incremental explanatory power for variations in highs and lows within the VECM framework. With all these variables, the augmented VECM models explain 40% to 50% of variations in daily highs and lows. The generalized impulse response analysis shows that the responses of daily highs and daily lows to the shocks depend on whether data on openings, closings, and trading volume are included in the analysis.

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Bibliographic Info

Paper provided by Hong Kong Institute for Monetary Research in its series Working Papers with number 072006.

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Length: 33 pages
Date of creation: May 2006
Date of revision:
Handle: RePEc:hkm:wpaper:072006

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Keywords: High; Low Open; Close; Trading Volume; VECM Model;

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  1. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, 08.
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  6. Gallant, A. Ronald & Hsu, Chien-Te & Tauchen, George, 2000. "Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance," Working Papers 00-04, Duke University, Department of Economics.
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Citations

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Cited by:
  1. Angela He & Alan Wan, 2009. "Predicting daily highs and lows of exchange rates: a cointegration analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204.
  2. Caporin, Massimiliano & Ranaldo, Angelo & Santucci de Magistris, Paolo, 2012. "On the Predictability of Stock Prices: a Case for High and Low Prices," Working Papers on Finance 1213, University of St. Gallen, School of Finance.
  3. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
  4. Yan-Leung Cheung & Yin-Wong Cheung & Alan T.K. Wan, 2008. "A High-Low Model of Daily Stock Price Ranges," CESifo Working Paper Series 2387, CESifo Group Munich.
  5. Javier Arroyo & Rosa Espínola & Carlos Maté, 2011. "Different Approaches to Forecast Interval Time Series: A Comparison in Finance," Computational Economics, Society for Computational Economics, vol. 37(2), pages 169-191, February.
  6. Cheung, Yan-Leung & Cheung, Yin-Wong & He, Angela W.W. & Wan, Alan T.K., 2010. "A trading strategy based on Callable Bull/Bear Contracts," Pacific-Basin Finance Journal, Elsevier, vol. 18(2), pages 186-198, April.
  7. García-Ascanio, Carolina & Maté, Carlos, 2010. "Electric power demand forecasting using interval time series: A comparison between VAR and iMLP," Energy Policy, Elsevier, vol. 38(2), pages 715-725, February.

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