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An Empirical Examination of Jump Risk in U.S. Equity And Bond Markets

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  • Lee Dunham
  • Geoffrey Friesen

Abstract

Actuaries manage risk, and asset price volatility is the most fundamental parameter in models of risk management. This study utilizes recent advances in econometric theory to decompose total asset price volatility into a smooth, continuous component and a discrete (jump) component. We analyze a data set that consists of high-frequency tick-by-tick data for all stocks in the S&P 100 Index, as well as similar futures contract data on three U.S. equity indexes and three U.S. Treasury securities during the period 1999-2005. We find that discrete jumps contribute between 15% and 25% of total asset risk for all equity index futures, and between 45% and 75% of total risk for Treasury bond futures. Jumps occur roughly once every five trading days for equity index futures, and slightly more frequently for Treasury bond futures. For the S&P 100 component stocks, on days when a jump occurs, the absolute jump is between 80% and 90% of the total absolute return for that day. We also demonstrate that, in the cross section of individual stocks, the average jump beta is significantly lower than the average continuous beta. Cross-correlations within the bond and stock markets are significantly higher on days when jumps occur, but stockbond correlations are relatively constant regardless of whether or not a jump occurs. We conclude with a discussion of the implications of our findings for risk management.

Suggested Citation

  • Lee Dunham & Geoffrey Friesen, 2007. "An Empirical Examination of Jump Risk in U.S. Equity And Bond Markets," North American Actuarial Journal, Taylor & Francis Journals, vol. 11(4), pages 76-91.
  • Handle: RePEc:taf:uaajxx:v:11:y:2007:i:4:p:76-91
    DOI: 10.1080/10920277.2007.10597485
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    Cited by:

    1. Shahzad, Syed Jawad Hussain & Raza, Naveed & Balcilar, Mehmet & Ali, Sajid & Shahbaz, Muhammad, 2017. "Can economic policy uncertainty and investors sentiment predict commodities returns and volatility?," Resources Policy, Elsevier, vol. 53(C), pages 208-218.
    2. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "The role of time‐varying rare disaster risks in predicting bond returns and volatility," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 327-340, July.
    3. Demirer, Riza & Gupta, Rangan & Suleman, Tahir & Wohar, Mark E., 2018. "Time-varying rare disaster risks, oil returns and volatility," Energy Economics, Elsevier, vol. 75(C), pages 239-248.
    4. Mehmet Balcilar & Matteo Bonato & Riza Demirer & Rangan Gupta, 2016. "The Effect of Investor Sentiment on Gold Market Dynamics," Working Papers 201638, University of Pretoria, Department of Economics.
    5. Wang Gao & Jiajia Wei & Shixiong Yang, 2023. "The Asymmetric Effects of Extreme Climate Risk Perception on Coal Futures Return Dynamics: Evidence from Nonparametric Causality-In-Quantiles Tests," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    6. Zhou, Haigang & Zhu, John Qi, 2019. "Firm characteristics and jump dynamics in stock prices around earnings announcements," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    7. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    8. Zhang, Hongwei & Demirer, Riza & Huang, Jianbai & Huang, Wanjun & Tahir Suleman, Muhammad, 2021. "Economic policy uncertainty and gold return dynamics: Evidence from high-frequency data," Resources Policy, Elsevier, vol. 72(C).
    9. Amaro de Matos, João & Silva, Nuno, 2014. "Consuming durable goods when stock markets jump: A strategic asset allocation approach," Journal of Economic Dynamics and Control, Elsevier, vol. 42(C), pages 86-104.
    10. Friesen, Geoffrey C. & Weller, Paul A. & Dunham, Lee M., 2009. "Price trends and patterns in technical analysis: A theoretical and empirical examination," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1089-1100, June.
    11. Rehman, Mobeen Ur & Vinh Vo, Xuan, 2020. "Cryptocurrencies and precious metals: A closer look from diversification perspective," Resources Policy, Elsevier, vol. 66(C).
    12. Haigang Zhou & John Zhu, 2011. "Jump risk and cross section of stock returns: evidence from China’s stock market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 35(3), pages 309-331, July.
    13. Rehman, Mobeen Ur & Apergis, Nicholas, 2019. "Determining the predictive power between cryptocurrencies and real time commodity futures: Evidence from quantile causality tests," Resources Policy, Elsevier, vol. 61(C), pages 603-616.

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