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An empirical model of daily highs and lows

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  • Yin-Wong Cheung

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 40-50% 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. Copyright © 2007 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/ijfe.303
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal International Journal of Finance & Economics.

Volume (Year): 12 (2007)
Issue (Month): 1 ()
Pages: 1-20

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Handle: RePEc:ijf:ijfiec:v:12:y:2007:i:1:p:1-20

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  1. 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, Informatica, Applicazioni "G. Parenti".
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Cited by:
  1. Massimiliano Caporin & Angelo Ranaldo & Paolo Santucci de Magistris, 2011. "On the Predictability of Stock Prices: A Case for High and Low Prices," "Marco Fanno" Working Papers 0136, Dipartimento di Scienze Economiche "Marco Fanno".
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.

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