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Trading with Asymmetric Volatility Spillovers

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  • Angel Pardo
  • Hipòlit Torró

Abstract

We study the profitability of trading strategies based on volatility spillovers between large and small firms. By using the Volatility Impulse‐Response Function of Lin (1997) and its extensions, we detect that any volatility shock coming from small companies is important to large companies, but the reverse is only true for negative shocks coming from large firms. To exploit these asymmetric patterns in volatility, different trading rules are designed based on the inverse relationship existing between expected return and volatility. We find that most strategies generate excess after‐transaction cost profits, especially after very bad news and very good news coming from large or small firm markets. These results are of special interest because of their implications for risk and portfolio management.

Suggested Citation

  • Angel Pardo & Hipòlit Torró, 2007. "Trading with Asymmetric Volatility Spillovers," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(9‐10), pages 1548-1568, November.
    • Handle: RePEc:bla:jbfnac:v:34:y:2007:i:9-10:p:1548-1568
    DOI: 10.1111/j.1468-5957.2007.02029.x
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    Cited by:

    1. Qadan, Mahmoud, 2018. "Switches in price discovery: Are U.S. traders more qualified in making valuations?," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 221-234.
    2. Angelidis, Timotheos & Andrikopoulos, Andreas, 2010. "Idiosyncratic risk, returns and liquidity in the London Stock Exchange: A spillover approach," International Review of Financial Analysis, Elsevier, vol. 19(3), pages 214-221, June.
    3. Milunovich, George & Thorp, Susan, 2006. "Valuing volatility spillovers," Global Finance Journal, Elsevier, vol. 17(1), pages 1-22, September.
    4. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 25-41.
    5. Sinem Derindere KOSEOGLU & Emrah Ismail CEVIK, 2013. "Testing for Causality in Mean and Variance between the Stock Market and the Foreign Exchange Market: An Application to the Major Central and Eastern European Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(1), pages 65-86, March.
    6. Mahmod Qadan & Joseph Yagil, 2012. "Fear sentiments and gold price: testing causality in-mean and in-variance," Applied Economics Letters, Taylor & Francis Journals, vol. 19(4), pages 363-366, March.
    7. Kumar, Dilip, 2017. "Realized volatility transmission from crude oil to equity sectors: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 149-167.
    8. Mao, Xiuping & Ruiz, Esther & Veiga, Helena, 2017. "Threshold stochastic volatility: Properties and forecasting," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1105-1123.
    9. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 271-285.
    10. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2021. "Political uncertainty, COVID-19 pandemic and stock market volatility transmission," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).

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