IDEAS home Printed from https://ideas.repec.org/a/bla/eufman/v19y2013i5p935-955.html
   My bibliography  Save this article

An International Perspective on Risk Management Quality

Author

Listed:
  • Svetlana Mira
  • Nicholas Taylor

Abstract

This paper introduces an alternative method for assessing the quality of risk management models. Specifically, using the forecast efficiency notion that forecast errors should be independent of a pertinent information set, we consider the extent to which unanticipated downside risk (extreme risk) is independent of overseas extreme risk. This is achieved using a bootstrap version of the non†causality test recently introduced by Hong et al. (), data covering 45 international equity markets, and by measuring extreme risk via a class of risk management models recently introduced by Xiao and Koenker (). In doing this, we find significant evidence of transmission (causality) across national borders. Moreover, we discuss how risk managers in developed and emerging markets can parsimoniously incorporate such information (international dependency) into their risk management models to produce measures of downside risk that have more desirable ex post properties (viz. forecast efficiency properties).

Suggested Citation

  • Svetlana Mira & Nicholas Taylor, 2013. "An International Perspective on Risk Management Quality," European Financial Management, European Financial Management Association, vol. 19(5), pages 935-955, November.
  • Handle: RePEc:bla:eufman:v:19:y:2013:i:5:p:935-955
    DOI: 10.1111/j.1468-036X.2011.00611.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1468-036X.2011.00611.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1468-036X.2011.00611.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    2. Michael J. Fleming & Jose A. Lopez, 1999. "Heat waves, meteor showers, and trading volume: an analysis of volatility spillovers in the U.S. Treasury market," Working Papers in Applied Economic Theory 99-09, Federal Reserve Bank of San Francisco.
    3. Engle, Robert F & Ito, Takatoshi & Lin, Wen-Ling, 1990. "Meteor Showers or Heat Waves? Heteroskedastic Intra-daily Volatility in the Foreign Exchange Market," Econometrica, Econometric Society, vol. 58(3), pages 525-542, May.
    4. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89.
    5. Asgharian, Hossein & Nossman, Marcus, 2011. "Risk contagion among international stock markets," Journal of International Money and Finance, Elsevier, vol. 30(1), pages 22-38, February.
    6. Bekaert, Geert & Harvey, Campbell R. & Lundblad, Christian, 2005. "Does financial liberalization spur growth?," Journal of Financial Economics, Elsevier, vol. 77(1), pages 3-55, July.
    7. John Beirne & Guglielmo Maria Caporale & Marianne Schulze-Ghattas & Nicola Spagnolo, 2013. "Volatility Spillovers and Contagion from Mature to Emerging Stock Markets," Review of International Economics, Wiley Blackwell, vol. 21(5), pages 1060-1075, November.
    8. Michael Mcaleer & Bernardo da Veiga, 2008. "Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 1-19.
    9. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    10. Marcello Pericoli & Massimo Sbracia, 2003. "A Primer on Financial Contagion," Journal of Economic Surveys, Wiley Blackwell, vol. 17(4), pages 571-608, September.
    11. Hong, Yongmiao & Li, Haitao & Zhao, Feng, 2007. "Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates," Journal of Econometrics, Elsevier, vol. 141(2), pages 736-776, December.
    12. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    13. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    14. 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.
    15. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    16. Egorov, Alexei V. & Hong, Yongmiao & Li, Haitao, 2006. "Validating forecasts of the joint probability density of bond yields: Can affine models beat random walk?," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 255-284.
    17. Charlotte Christiansen, 2007. "Volatility‐Spillover Effects in European Bond Markets," European Financial Management, European Financial Management Association, vol. 13(5), pages 923-948, November.
    18. Zhijie Xiao & Roger Koenker, 2009. "Conditional Quantile Estimation for GARCH Models," Boston College Working Papers in Economics 725, Boston College Department of Economics.
    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. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
    2. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    3. Makushkin, Mikhail & Lapshin, Victor, 2020. "Modelling tail dependencies between Russian and foreign stock markets: Application for market risk valuation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 30-52.
    4. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 29-56, Winter.
    5. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    6. André A. P. Santos & Francisco J. Nogales & Esther Ruiz, 2013. "Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk," Journal of Financial Econometrics, Oxford University Press, vol. 11(2), pages 400-441, March.
    7. Nieto, María Rosa, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    9. Halbleib, Roxana & Pohlmeier, Winfried, 2012. "Improving the value at risk forecasts: Theory and evidence from the financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1212-1228.
    10. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    11. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 958-969.
    12. Ñíguez, Trino-Manuel & Perote, Javier, 2017. "Moments expansion densities for quantifying financial risk," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 53-69.
    13. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    14. Huang, Alex YiHou & Peng, Sheng-Pen & Li, Fangjhy & Ke, Ching-Jie, 2011. "Volatility forecasting of exchange rate by quantile regression," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 591-606, October.
    15. Stavros Degiannakis & Apostolos Kiohos, 2014. "Multivariate modelling of 10-day-ahead VaR and dynamic correlation for worldwide real estate and stock indices," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 41(2), pages 216-232, March.
    16. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    17. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    18. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723, September.
    19. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    20. Wu, Qi & Yan, Xing, 2019. "Capturing deep tail risk via sequential learning of quantile dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).

    More about this item

    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:bla:eufman:v:19:y:2013:i:5:p:935-955. See general information about how to correct material in RePEc.

    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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/efmaaea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.