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Predicting daily highs and lows of exchange rates: a cointegration analysis

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  • Angela He
  • Alan Wan

Abstract

This article presents empirical evidence that links the daily highs and lows of exchange rates of the US dollar against two other major currencies over a 15 year period. We find that the log high and log low of an exchange rate are cointegrated, and the error correction term is well-approximated by the range, which is defined as the difference between the log high and log low. We further assess the empirical relevance of jointly analyzing the highs, lows and the ranges by comparing the range forecasts generated from the cointegration framework with those from random walk and autoregressive integrated moving average (ARIMA) specifications. The ability of range forecasts as predictors of implied volatility for a European style currency option is also evaluated. Our results show that aside from a limited set of exceptions, the cointegration framework generally outperforms the random walk and ARIMA models in an out-of-sample forecast contest.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1191-1204
    DOI: 10.1080/02664760802578304
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    References listed on IDEAS

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    Cited by:

    1. He, Angela W.W. & Kwok, Jerry T.K. & Wan, Alan T.K., 2010. "An empirical model of daily highs and lows of West Texas Intermediate crude oil prices," Energy Economics, Elsevier, vol. 32(6), pages 1499-1506, November.
    2. Xiong, Tao & Li, Chongguang & Bao, Yukun, 2017. "Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model," Economic Modelling, Elsevier, vol. 60(C), pages 11-23.
    3. Caporin, Massimiliano & Ranaldo, Angelo & Santucci de Magistris, Paolo, 2013. "On the predictability of stock prices: A case for high and low prices," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5132-5146.
    4. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    5. 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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