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A Cyclical Model of Exchange Rate Volatility

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  • Evarist Stoja
  • Richard D. F. Harris
  • Fatih Yilmaz

Abstract

In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We find that the long run trend is time-varying but highly persistent, while the cyclical component is strongly mean reverting. This has important implications for modelling and forecasting volatility over both short and long horizons. As an illustration, we use the cyclical volatility model to generate out-of-sample forecasts of exchange rate volatility for horizons of up to one year under the assumption that the long run trend is fully persistent. As a benchmark, we compare the forecasts of the cyclical volatility model with those of the two-factor intraday range-based EGARCH model of Brandt and Jones (2006). Not only is the cyclical volatility model significantly easier to estimate than the EGARCH model, but it also offers a substantial improvement in out-of-sample forecast performance.

Suggested Citation

  • Evarist Stoja & Richard D. F. Harris & Fatih Yilmaz, 2010. "A Cyclical Model of Exchange Rate Volatility," Bristol Economics Discussion Papers 10/618, School of Economics, University of Bristol, UK.
    • Handle: RePEc:bri:uobdis:10/618
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    Cited by:

    1. (Jeremy) Chiu, Ching-wai & Harris, Richard D.F. & Stoja, Evarist & Chin, Michael, 2018. "Financial market Volatility, macroeconomic fundamentals and investor Sentiment," Journal of Banking & Finance, Elsevier, vol. 92(C), pages 130-145.
    2. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    3. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    4. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
    5. Rania Jammazi & Chaker Aloui, 2014. "Cyclical components and dual long memory in the foreign exchange rate dynamics: the Tunisian case," Working Papers 2014-198, Department of Research, Ipag Business School.
    6. Orlowski, Lucjan T., 2016. "Co-movements of non-Euro EU currencies with the Euro," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 376-383.
    7. Tang, Tao & Wang, Yanchen, 2022. "Liquidity Shocks, Price Volatilities, and Risk-managed Strategy: Evidence from Bitcoin and Beyond," Journal of Multinational Financial Management, Elsevier, vol. 64(C).
    8. Richard D. F. Harris & Murat Mazibas, 2022. "A component Markov regime‐switching autoregressive conditional range model," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 650-683, April.
    9. Wu, Xinyu & Hou, Xinmeng, 2020. "Forecasting volatility with component conditional autoregressive range model," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

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    More about this item

    Keywords

    Conditional volatility; Intraday range; Hodrick-Prescott filter;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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