IDEAS home Printed from https://ideas.repec.org/a/eee/mulfin/v30y2015icp62-82.html
   My bibliography  Save this article

Geographical diversification with a World Volatility Index

Author

Listed:
  • Aboura, Sofiane
  • Chevallier, Julien

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 2010–2011 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

  • Aboura, Sofiane & Chevallier, Julien, 2015. "Geographical diversification with a World Volatility Index," Journal of Multinational Financial Management, Elsevier, vol. 30(C), pages 62-82.
    • Handle: RePEc:eee:mulfin:v:30:y:2015:i:c:p:62-82
    DOI: 10.1016/j.mulfin.2015.03.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1042444X15000195
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    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. 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.
    6. 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.
    7. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    8. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(4), pages 537-572.
    9. 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.
    10. 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.
    11. 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.
    12. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
    13. Maria Kasch & Massimiliano Caporin, 2013. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(4), pages 706-742, September.
    14. 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.
    15. 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.
    16. 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.
    17. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    24. Kenourgios, Dimitris, 2014. "On financial contagion and implied market volatility," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 21-30.
    25. 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, Reading University.
    26. 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.
    27. 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.
    28. Kun Zhang & Laiwan Chan, 2009. "Efficient factor GARCH models and factor-DCC models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 71-91.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    Full references (including those not matched with items on IDEAS)

    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. Aboura, Sofiane & Chevallier, Julien, 2015. "A cross-volatility index for hedging the country risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 25-41.
    2. Aboura, Sofiane & Chevallier, Julien, 2014. "Cross-market index with Factor-DCC," Economic Modelling, Elsevier, vol. 40(C), pages 158-166.
    3. Aboura, Sofiane & Chevallier, Julien, 2015. "Cross-market volatility index with Factor-DCC," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 132-140.
    4. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    5. Aboura, Sofiane & Chevallier, Julien, 2017. "A new weighting-scheme for equity indexes," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 159-175.
    6. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2017. "Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis," Journal of Econometrics, Elsevier, vol. 199(1), pages 74-92.
    7. Hallin, Marc & Lippi, Marco, 2013. "Factor models in high-dimensional time series—A time-domain approach," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2678-2695.
    8. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    9. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
    10. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    11. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    12. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    13. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    14. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
    15. Xia, Qiang & Liang, Rubing & Wu, Jianhong, 2017. "Transformed contribution ratio test for the number of factors in static approximate factor models," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 235-241.
    16. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
    17. Cavicchioli, Maddalena & Forni, Mario & Lippi, Marco & Zaffaroni, Paolo, 2016. "Eigenvalue Ratio Estimators for the Number of Common Factors," CEPR Discussion Papers 11440, C.E.P.R. Discussion Papers.
    18. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    19. Kenourgios, Dimitris, 2014. "On financial contagion and implied market volatility," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 21-30.
    20. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España;Working Papers Homepage.

    More about this item

    Keywords

    Factor-DCC; World market volatility; Diversification; G20; Crisis Episodes Detection;
    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

    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:eee:mulfin:v:30:y:2015:i:c:p:62-82. 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: (Haili He). General contact details of provider: http://www.elsevier.com/locate/mulfin .

    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 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.

    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.