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Time variation in the tail behavior of Bund future returns

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  • Thomas Werner
  • Christian Upper

Abstract

The literature on the tail behavior of asset prices focuses mainly on the foreign exchange and stock markets, with only a few articles dealing with bonds or bond futures. The present article addresses this omission. It focuses on three questions using extreme value analysis: (a) Does the distribution of Bund future returns have heavy tails? (b) Do the tails change over time? (c) Does the tail index provide information that is not captured by a standard VaR approach? The results are as follows: (a) The distribution of high‐frequency returns of the Bund future is indeed characterized by heavy tails. The tails are thinner for lower frequencies, but remain significantly heavy even for daily data. (b) There are statistically significant breaks in the tails of the return distribution. (c) The likelihood of extreme price movements suggested by extreme value theory differs from that obtained by standard risk measures. This suggests that the tail index does indeed provide information not contained in volatility measures. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:387–398, 2004

Suggested Citation

  • Thomas Werner & Christian Upper, 2004. "Time variation in the tail behavior of Bund future returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(4), pages 387-398, April.
    • Handle: RePEc:wly:jfutmk:v:24:y:2004:i:4:p:387-398
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    Cited by:

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    3. Daniele Massacci, 2017. "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness," Management Science, INFORMS, vol. 63(9), pages 3072-3089, September.
    4. Demosthenes Tambakis, 2009. "Feedback trading and intermittent market turbulence," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 477-489.
    5. Andria, Joseph & di Tollo, Giacomo & Kalda, Jaan, 2022. "The predictive power of power-laws: An empirical time-arrow based investigation," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    6. Straetmans, Stefan & Candelon, Bertrand, 2013. "Long-term asset tail risks in developed and emerging markets," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 1832-1844.
    7. Qin, Yiyi & Cai, Jun & Wang, James J.D. & Webb, Robert I., 2023. "Gold-mining stocks, risk factors, and tail patterns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    8. John Cotter & Kevin Dowd, 2010. "Estimating financial risk measures for futures positions: A nonparametric approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(7), pages 689-703, July.
    9. Grobys, Klaus, 2023. "Correlation versus co-fractality: Evidence from foreign-exchange-rate variances," International Review of Financial Analysis, Elsevier, vol. 86(C).
    10. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    11. Wu, Ying, 2019. "Asset pricing with extreme liquidity risk," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 143-165.
    12. Onofrio Panzarino & Francesco Potente & Alfonso Puorro, 2016. "BTP futures and cash relationships: a high frequency data analysis," Temi di discussione (Economic working papers) 1083, Bank of Italy, Economic Research and International Relations Area.
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    14. Palumbo, D., 2021. "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics 2111, Faculty of Economics, University of Cambridge.
    15. Belkhir, Mohamed & Saad, Mohsen & Samet, Anis, 2020. "Stock extreme illiquidity and the cost of capital," Journal of Banking & Finance, Elsevier, vol. 112(C).

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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