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Geographical Diversification with a World Volatility Index

Author

Listed:
  • Julien Chevallier

    (UP8 - Université Paris 8 Vincennes-Saint-Denis)

  • Sofiane Aboura

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper proposes a new ‘World Volatility Index', coined WVIX, by constructing the first index that approximates the aggregate volatility level of the G20 countries. The empirical analysis makes use of the factor dynamic conditional correlation model – with an automated methodology to detect the number of factors – in order to (i) sum up the information contained in the implied volatility indexes belonging to the US, the UK, the Eurozone, Japan and emerging countries, and (ii) examine the time-varying correlation between them. The results reveal that the WVIX evolves around 22%, but its activity can vary sharply depending on its exposure to various sources of geographical risks (e.g. the latest 2010-11 European debt crisis). Thus constructed as an early warning device, the methodology behind the WVIX can be replicated by market practitioners to datasets that better suit their needs.

Suggested Citation

  • Julien Chevallier & Sofiane Aboura, 2015. "Geographical Diversification with a World Volatility Index," Post-Print hal-01529755, HAL.
    • Handle: RePEc:hal:journl:hal-01529755
    DOI: 10.1016/j.mulfin.2015.03.001
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    as
    1. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2013. "Risk, uncertainty and monetary policy," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 771-788.
    2. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Massimo Guidolin & Stuart Hyde, 2008. "Equity portfolio diversification under time-varying predictability and comovements: evidence from Ireland, the US, and the UK," Working Papers 2008-005, Federal Reserve Bank of St. Louis.
    5. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    6. Brian H. Boyer & Tomomi Kumagai & Kathy Yuan, 2006. "How Do Crises Spread? Evidence from Accessible and Inaccessible Stock Indices," Journal of Finance, American Finance Association, vol. 61(2), pages 957-1003, April.
    7. Connor, Gregory & Korajczyk, Robert A, 1993. "A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
    8. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    9. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    10. Andrade, Sandro C., 2009. "A model of asset pricing under country risk," Journal of International Money and Finance, Elsevier, vol. 28(4), pages 671-695, June.
    11. Saini, Krishan G. & Bates, Philip S., 1984. "A survey of the quantitative approaches to country risk analysis," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 341-356, June.
    12. Gupta, R. & Donleavy, G.D., 2009. "Benefits of diversifying investments into emerging markets with time-varying correlations: An Australian perspective," Journal of Multinational Financial Management, Elsevier, vol. 19(2), pages 160-177, April.
    13. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
    14. Maria Kasch & Massimiliano Caporin, 2013. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 11(4), pages 706-742, September.
    15. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    16. Kaeck, Andreas & Alexander, Carol, 2013. "Continuous-time VIX dynamics: On the role of stochastic volatility of volatility," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 46-56.
    17. Agliardi, Elettra & Agliardi, Rossella & Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2012. "A new country risk index for emerging markets: A stochastic dominance approach," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 741-761.
    18. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    19. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    20. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    21. Manuel Ammann & Stephan Suss, 2009. "Asymmetric dependence patterns in financial time series," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 703-719.
    22. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    23. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    24. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    25. Kenourgios, Dimitris, 2014. "On financial contagion and implied market volatility," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 21-30.
    26. Carol Alexander, 2000. "Orthogonal Methods for Generating Large Positive Semi-Definite Covariance Matrices," ICMA Centre Discussion Papers in Finance icma-dp2000-06, Henley Business School, University of Reading.
    27. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    28. Jonathan Eaton & Mark Gersovitz & Joseph E. Stiglitz, 1991. "The Pure Theory of Country Risk," NBER Chapters, in: International Volatility and Economic Growth: The First Ten Years of The International Seminar on Macroeconomics, pages 391-435, National Bureau of Economic Research, Inc.
    29. Kun Zhang & Laiwan Chan, 2009. "Efficient factor GARCH models and factor-DCC models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 71-91.
    30. Kenourgios, Dimitris & Padhi, Puja, 2012. "Emerging markets and financial crises: Regional, global or isolated shocks?," Journal of Multinational Financial Management, Elsevier, vol. 22(1), pages 24-38.
    31. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    32. Yue Peng & Wing Ng, 2012. "Analysing financial contagion and asymmetric market dependence with volatility indices via copulas," Annals of Finance, Springer, vol. 8(1), pages 49-74, February.
    33. Kabir Hassan, M. & Maroney, Neal C. & Monir El-Sady, Hassan & Telfah, Ahmad, 2003. "Country risk and stock market volatility, predictability, and diversification in the Middle East and Africa," Economic Systems, Elsevier, vol. 27(1), pages 63-82, March.
    34. Tibor F. Liska, 2007. "The Liska model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 29(3), pages 363-381, December.
    35. Cosset, Jean-Claude & Siskos, Yannis & Zopounidis, Constantin, 1992. "Evaluating country risk: A decision support approach," Global Finance Journal, Elsevier, vol. 3(1), pages 79-95.
    36. Guidolin, Massimo & Hyde, Stuart, 2008. "Equity portfolio diversification under time-varying predictability: Evidence from Ireland, the US, and the UK," Journal of Multinational Financial Management, Elsevier, vol. 18(4), pages 293-312, October.
    37. Helene Rey, 2013. "Dilemma not trilemma: the global cycle and monetary policy independence," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 1-2.
    38. Lam, Clifford & Yao, Qiwei, 2012. "Factor modeling for high-dimensional time series: inference for the number of factors," LSE Research Online Documents on Economics 45684, London School of Economics and Political Science, LSE Library.
    39. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    40. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
    41. Warnes, Ignacio & Warnes, Pablo E., 2014. "Country risk and the cost of equity in emerging markets," Journal of Multinational Financial Management, Elsevier, vol. 28(C), pages 15-27.
    42. Jiang, George J. & Konstantinidi, Eirini & Skiadopoulos, George, 2012. "Volatility spillovers and the effect of news announcements," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2260-2273.
    43. Oetzel, Jennifer M. & Bettis, Richard A. & Zenner, Marc, 2001. "Country risk measures: how risky are they?," Journal of World Business, Elsevier, vol. 36(2), pages 128-145, July.
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    More about this item

    Keywords

    G20; Financial crisis; Crisis Episodes Detection; World Market Volatility; Diversification; Factor-DCC;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General
    • F30 - International Economics - - International Finance - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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