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Non-parametric quantile dependencies between volatility discontinuities and political risk

Author

Listed:
  • Gkillas, Konstantinos
  • Boako, Gideon
  • Vortelinos, Dimitrios
  • Vasiliadis, Lavrentios

Abstract

In this paper, we investigate the non-parametric relation between political risk and Mexican financial markets. We focus on stock, foreign exchange, financial institutions bond, corporate bond and sovereign bond markets. We apply a quantile correlation approach between five categories of the most used political risk indicators and volatility discontinuities (jumps) in a pairwise comparison. Our findings suggest that dependencies of political risk factors with stock and foreign exchange markets appear to be generally positive, while those with financial institutions and corporate bonds are adverse.

Suggested Citation

  • Gkillas, Konstantinos & Boako, Gideon & Vortelinos, Dimitrios & Vasiliadis, Lavrentios, 2020. "Non-parametric quantile dependencies between volatility discontinuities and political risk," Finance Research Letters, Elsevier, vol. 32(C).
    • Handle: RePEc:eee:finlet:v:32:y:2020:i:c:s1544612318303829
    DOI: 10.1016/j.frl.2018.12.022
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    as
    1. Alesina, Alberto & Sachs, Jeffrey, 1988. "Political Parties and the Business Cycle in the United States, 1948-1984," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 20(1), pages 63-82, February.
    2. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2013. "Risk, uncertainty and monetary policy," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 771-788.
    3. Bollerslev, Tim & Todorov, Viktor & Xu, Lai, 2015. "Tail risk premia and return predictability," Journal of Financial Economics, Elsevier, vol. 118(1), pages 113-134.
    4. Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
    5. Zaremba, Adam, 2016. "Risk-based explanation for the country-level size and value effects," Finance Research Letters, Elsevier, vol. 18(C), pages 226-233.
    6. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
    7. Christensen, Kim & Oomen, Roel & Podolskij, Mark, 2010. "Realised quantile-based estimation of the integrated variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 74-98, November.
    8. Bilson, Christopher M. & Brailsford, Timothy J. & Hooper, Vincent C., 2002. "The explanatory power of political risk in emerging markets," International Review of Financial Analysis, Elsevier, vol. 11(1), pages 1-27.
    9. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    10. repec:hal:journl:peer-00741630 is not listed on IDEAS
    11. Gkillas, Konstantinos & Longin, François, 2018. "Financial market activity under capital controls: Lessons from extreme events," Economics Letters, Elsevier, vol. 171(C), pages 10-13.
    12. Andersen, Torben G. & Fusari, Nicola & Todorov, Viktor, 2015. "The risk premia embedded in index options," Journal of Financial Economics, Elsevier, vol. 117(3), pages 558-584.
    13. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    14. Pedro Santa-Clara & Shu Yan, 2010. "Crashes, Volatility, and the Equity Premium: Lessons from S&P 500 Options," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 435-451, May.
    15. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 456-499.
    16. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    17. Alberto Alesina & Alex Cukierman, 1990. "The Politics of Ambiguity," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 105(4), pages 829-850.
    18. Vortelinos, Dimitrios I. & Saha, Shrabani, 2016. "The impact of political risk on return, volatility and discontinuity: Evidence from the international stock and foreign exchange markets," Finance Research Letters, Elsevier, vol. 17(C), pages 222-226.
    19. Bailey, Warren & Chung, Y. Peter, 1995. "Exchange Rate Fluctuations, Political Risk, and Stock Returns: Some Evidence from an Emerging Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(4), pages 541-561, December.
    20. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    21. Cecilia Mancini, 2009. "Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 270-296, June.
    22. repec:hal:journl:peer-00732538 is not listed on IDEAS
    23. Dimic, Nebojsa & Orlov, Vitaly & Piljak, Vanja, 2015. "The political risk factor in emerging, frontier, and developed stock markets," Finance Research Letters, Elsevier, vol. 15(C), pages 239-245.
    24. Guodong Li & Yang Li & Chih-Ling Tsai, 2015. "Quantile Correlations and Quantile Autoregressive Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 246-261, March.
    25. Duong, Diep & Swanson, Norman R., 2015. "Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction," Journal of Econometrics, Elsevier, vol. 187(2), pages 606-621.
    26. Dimic, Nebojsa & Orlov, Vitaly & Piljak, Vanja, 2016. "The effect of political risk on currency carry trades," Finance Research Letters, Elsevier, vol. 19(C), pages 75-78.
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    More about this item

    Keywords

    Mexico; Political risk; Quantile correlation; Volatility jumps;
    All these keywords.

    JEL classification:

    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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