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Asymmetry of Information Flow Between Volatilities Across Time Scales

Author

Listed:
  • Ramazan Gencay

    () (Department of Economics, Simon Fraser University)

  • Nikola Gradojevic

    () (Faculty of Business Administration, Lakehead University)

  • Faruk Selcuk

    (Department of Economics, Bilkent University)

  • Brandon Whitcher

    (GlaxoSmithKline Clinical Imaging Centre, Hammersmith Hospital London, United Kingdom)

Abstract

Conventional time series analysis, focusing exclusively on a time series at a given scale, lacks the ability to explain the nature of the data generating process. A process equation that successfully explains daily price changes, for example, is unable to characterize the nature of hourly price changes. On the other hand, statistical properties of monthly price changes are often not fully covered by a model based on daily price changes. In this paper, we simultaneously model regimes of volatilities at multiple time scales through wavelet-domain hidden Markov models. We establish an important stylized property of volatility across different time scales. We call this property asymmetric vertical dependence. It is asymmetric in the sense that a low volatility state (regime) at a long time horizon is most likely followed by low volatility states at shorter time horizons. On the other hand, a high volatility state at long time horizons does not necessarily imply a high volatility state at shorter time horizons. Our analysis provides evidence that volatility is a mixture of high and low volatility regimes, resulting in a distribution that is non-Gaussian. This result has important implications regarding the scaling behavior of volatility, and consequently, the calculation of risk at different time scales.

Suggested Citation

  • Ramazan Gencay & Nikola Gradojevic & Faruk Selcuk & Brandon Whitcher, 2009. "Asymmetry of Information Flow Between Volatilities Across Time Scales," Working Paper series 27_09, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:27_09
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    References listed on IDEAS

    as
    1. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Dieter Hendricks & Tim Gebbie & Diane Wilcox, 2015. "Detecting intraday financial market states using temporal clustering," Papers 1508.04900, arXiv.org, revised Feb 2017.
    2. Jozef Barunik & Lukas Vacha, 2015. "Realized wavelet-based estimation of integrated variance and jumps in the presence of noise," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1347-1364, August.
    3. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2016. "Gold, oil, and stocks: Dynamic correlations," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 186-201.
    4. Takaki Hayashi & Yuta Koike, 2017. "Multi-scale analysis of lead-lag relationships in high-frequency financial markets," Papers 1708.03992, arXiv.org, revised Feb 2018.
    5. repec:eee:phsmap:v:502:y:2018:i:c:p:379-393 is not listed on IDEAS
    6. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.
    7. François Benhmad, 2011. "A wavelet analysis of oil price volatility dynamic," Economics Bulletin, AccessEcon, vol. 31(1), pages 792-806.
    8. Swastika, Purti & Dewandaru, Ginanjar & Masih, Mansur, 2013. "The Impact of Debt on Economic Growth: A Case Study of Indonesia," MPRA Paper 58837, University Library of Munich, Germany.
    9. W. D. Chen & H. C. Li, 2016. "Wavelet decomposition of heterogeneous investment horizon," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(4), pages 714-734, October.
    10. Haiyun Xu, 2016. "Economic policy uncertainty and housing returns in Germany: Evidence from a bootstrap rolling window," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 34(2), pages 309-332.
    11. Benhmad, François, 2012. "Modeling nonlinear Granger causality between the oil price and U.S. dollar: A wavelet based approach," Economic Modelling, Elsevier, vol. 29(4), pages 1505-1514.
    12. repec:wsi:ijtafx:v:12:y:2009:i:01:n:s0219024909005130 is not listed on IDEAS
    13. Gallegati, Marco & Ramsey, James B., 2013. "Bond vs stock market's Q: Testing for stability across frequencies and over time," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 138-150.
    14. Conlon, Thomas & Cotter, John, 2013. "Downside risk and the energy hedger's horizon," Energy Economics, Elsevier, vol. 36(C), pages 371-379.
    15. repec:eee:phsmap:v:482:y:2017:i:c:p:552-568 is not listed on IDEAS
    16. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, Open Access Journal, vol. 5(4), pages 1-26, April.
    17. Marco Gallegati & Mauro Gallegati & James B. Ramsey & Willi Semmler, 2017. "Long waves in prices: new evidence from wavelet analysis," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 11(1), pages 127-151, January.
    18. Jozef Barun'ik & Evv{z}en Kov{c}enda, 2018. "Total, asymmetric and frequency connectedness between oil and forex markets," Papers 1805.03980, arXiv.org.
    19. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    20. Michis, Antonis A., 2014. "Investing in gold: Individual asset risk in the long run," Finance Research Letters, Elsevier, vol. 11(4), pages 369-374.
    21. Christian M. Hafner, 2012. "Cross-correlating wavelet coefficients with applications to high-frequency financial time series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1363-1379, December.
    22. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.

    More about this item

    Keywords

    Discrete wavelet transform; wavelet-domain hidden Markov trees; foreign exchange markets; stock markets; multiresolution analysis; scaling;

    JEL classification:

    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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