IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/201879.html
   My bibliography  Save this paper

Forecasting (Good and Bad) Realized Exchange-Rate Volatility: Is there a Role for Realized Skewness and Kurtosis?

Author

Listed:
  • Konstantinos Gkillas

    (Department of Business Administration, University of Patras – University Campus, Rio, Greece)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Hamburg, Germany)

Abstract

This study investigates U.S. political cycles and the impact, thereof on stock market volatility in advanced economies (Canada, France, Germany, Italy, Japan, Switzerland and the U.K.) using monthly data over the period January 1921 to December 2017. Overall, the results indicate that the type (Democratic or Republican) of presidential administration does play a role in the behaviour of stock returns, and volatility, but the results and direction of the impact are sample specific. In general, the results tend to suggest an increase in returns and volatility of other stock markets when there is a democratic government in the U.S. This study suggests that there is a need for market participants to start analysing the trajectory of a certain election, beginning at the proposed event window, in order to manage their risks and be at a stable position during these periods of uncertainties.

Suggested Citation

  • Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2018. "Forecasting (Good and Bad) Realized Exchange-Rate Volatility: Is there a Role for Realized Skewness and Kurtosis?," Working Papers 201879, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201879
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    2. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    3. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    4. 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.
    5. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    6. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
    7. Robert J. Barro, 2006. "Rare Disasters and Asset Markets in the Twentieth Century," The Quarterly Journal of Economics, Oxford University Press, vol. 121(3), pages 823-866.
    8. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2016. "Volatility Jumps and Their Economic Determinants," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(1), pages 29-80.
    9. 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.
    10. repec:imf:imfops:235 is not listed on IDEAS
    11. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    12. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    13. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    14. Babikir, Ali & Gupta, Rangan & Mwabutwa, Chance & Owusu-Sekyere, Emmanuel, 2012. "Structural breaks and GARCH models of stock return volatility: The case of South Africa," Economic Modelling, Elsevier, vol. 29(6), pages 2435-2443.
    15. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, June.
    16. Kraus, Alan & Litzenberger, Robert H, 1976. "Skewness Preference and the Valuation of Risk Assets," Journal of Finance, American Finance Association, vol. 31(4), pages 1085-1100, September.
    17. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    18. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    19. Rietz, Thomas A., 1988. "The equity risk premium a solution," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 117-131, July.
    20. Keith Pilbeam & Kjell Langeland, 2015. "Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts," International Economics and Economic Policy, Springer, vol. 12(1), pages 127-142, March.
    21. Longstaff, Francis A. & Piazzesi, Monika, 2004. "Corporate earnings and the equity premium," Journal of Financial Economics, Elsevier, vol. 74(3), pages 401-421, December.
    22. Asteriou, Dimitrios & Masatci, Kaan & Pılbeam, Keith, 2016. "Exchange rate volatility and international trade: International evidence from the MINT countries," Economic Modelling, Elsevier, vol. 58(C), pages 133-140.
    23. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    24. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715, December.
    25. Ghysels, Eric & Sinko, Arthur, 2011. "Volatility forecasting and microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 257-271, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    2. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis," Working Papers 202114, University of Pretoria, Department of Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis," Working Papers 202114, University of Pretoria, Department of Economics.
    2. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, Open Access Journal, vol. 12(10), pages 1-11, May.
    3. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    4. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    5. Bonato, Matteo & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "A note on investor happiness and the predictability of realized volatility of gold," Finance Research Letters, Elsevier, vol. 39(C).
    6. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Risk Aversion and the Predictability of Crude Oil Market Volatility: A Forecasting Experiment with Random Forests," Working Papers 201972, University of Pretoria, Department of Economics.
    7. Harris, Richard D.F. & Nguyen, Linh H. & Stoja, Evarist, 2019. "Systematic extreme downside risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 128-142.
    8. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    9. Rhee, S. Ghon & Wu, Feng (Harry), 2020. "Conditional extreme risk, black swan hedging, and asset prices," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 412-435.
    10. Jozef Barunik & Josef Kurka, 2021. "Frequency-Dependent Higher Moment Risks," Papers 2104.04264, arXiv.org.
    11. Kinateder, Harald & Papavassiliou, Vassilios G., 2019. "Sovereign bond return prediction with realized higher moments," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 53-73.
    12. Alexander, Gordon J. & Baptista, Alexandre M., 2009. "Stress testing by financial intermediaries: Implications for portfolio selection and asset pricing," Journal of Financial Intermediation, Elsevier, vol. 18(1), pages 65-92, January.
    13. Atilgan, Yigit & Bali, Turan G. & Demirtas, K. Ozgur & Gunaydin, A. Doruk, 2019. "Global downside risk and equity returns," Journal of International Money and Finance, Elsevier, vol. 98(C), pages 1-1.
    14. David Backus & Mikhail Chernov & Ian Martin, 2011. "Disasters Implied by Equity Index Options," Journal of Finance, American Finance Association, vol. 66(6), pages 1969-2012, December.
    15. Jondeau, Eric & Zhang, Qunzi & Zhu, Xiaoneng, 2019. "Average skewness matters," Journal of Financial Economics, Elsevier, vol. 134(1), pages 29-47.
    16. DiTraglia, Francis J. & Gerlach, Jeffrey R., 2013. "Portfolio selection: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 305-323.
    17. Chabi-Yo, Fousseni & Ruenzi, Stefan & Weigert, Florian, 2018. "Crash Sensitivity and the Cross Section of Expected Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1059-1100, June.
    18. Francisco Ruge‐Murcia, 2017. "Skewness Risk and Bond Prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 379-400, March.
    19. Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian & Shahzad, Syed Jawad Hussain, 2020. "The predictive power of oil price shocks on realized volatility of oil: A note," Resources Policy, Elsevier, vol. 69(C).
    20. Roberto Marfè & Julien Penasse, 2016. "The Time-Varying Risk of Macroeconomic Disasters," Carlo Alberto Notebooks 463, Collegio Carlo Alberto.

    More about this item

    Keywords

    Exchange rates; Realized volatility; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:201879. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/decupza.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.