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A Generalized Multiscale Analysis Of The Predictive Content Of Eurodollar Implied Volatilities

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  • ALESSANDRO CARDINALI

    (School of Mathematics, University of Bristol, University Walk, BS8 1TW Bristol, UK)

Abstract

It is widely believed that implied volatilities contains information that would enable prediction of spot volatility for a wide range of financial assets. Lead-lag analysis based on the Discrete Wavelet Transform has been proposed as one method for identifying and extracting that predictive information. Unfortunately this approach can fail to identify periodic components that are not proportional to an increasing dyadic scale. We propose a multiscale analysis of the Eurodollar realized volatility and at-the-money (ATM) implied volatilities. After filtering the long memory components we produce a decomposition of cross-correlation by using wavelet packet methods. A threshold cost functional based on asymptotic confidence intervals was used along with the best basis algorithm in order to select an adaptive frequency partition of the sample cross-correlation. We found substantial evidence that Eurodollar implied volatilities contain predictive information about realized volatilities. Moreover, in our analysis the new technique outperforms the lead-lag analysis based on the nondecimated Discrete Wavelet Transform. Therefore we contend that the proposed technique will improve detection of predictive information and recommend further testing in a range of applied contexts.

Suggested Citation

  • Alessandro Cardinali, 2009. "A Generalized Multiscale Analysis Of The Predictive Content Of Eurodollar Implied Volatilities," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 1-18.
    • Handle: RePEc:wsi:ijtafx:v:12:y:2009:i:01:n:s0219024909005130
    DOI: 10.1142/S0219024909005130
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    References listed on IDEAS

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    1. Ramazan Gencay & Nikola Gradojevic & Faruk Selcuk & Brandon Whitcher, 2010. "Asymmetry of information flow between volatilities across time scales," Quantitative Finance, Taylor & Francis Journals, vol. 10(8), pages 895-915.
    2. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2004. "Information flow between volatilities across time scales," MPRA Paper 10355, University Library of Munich, Germany.
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    Cited by:

    1. Silvo Dajčman, 2013. "Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis," Prague Economic Papers, Prague University of Economics and Business, vol. 2013(1), pages 28-49.
    2. Takaki Hayashi & Yuta Koike, 2016. "Wavelet-based methods for high-frequency lead-lag analysis," Papers 1612.01232, arXiv.org, revised Nov 2018.
    3. Tata Subba Rao & Granville Tunnicliffe Wilson & Alessandro Cardinali & Guy P. Nason, 2017. "Locally Stationary Wavelet Packet Processes: Basis Selection and Model Fitting," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 151-174, March.

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