Measuring tail-risk cross-country exposures in the banking Industry

  1. Antonio Rubia 1
  2. Lidia Sanchis Marco 2
  1. 1 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

  2. 2 Universidad de Castilla-La Mancha
    info

    Universidad de Castilla-La Mancha

    Ciudad Real, España

    ROR https://ror.org/05r78ng12

Revista:
Revista de economía aplicada

ISSN: 1133-455X

Ano de publicación: 2017

Volume: 25

Número: 74

Páxinas: 27-74

Tipo: Artigo

Outras publicacións en: Revista de economía aplicada

Resumo

Este artículo analiza la vulnerabilidad de la industria bancaria en economías avanzadas y emergentes frente a shocks en distintas áreas en periodos de inestabilidad financiera. El principal objetivo es, por un lado, medir las sensibilidades cruzadas entre las diferentes zonas que caracterizan el contagio sistémico en el sistema bancario internacional. Por otro lado, se pretende caracterizar la forma en la que se propagan los shocks sistémicos a lo largo del tiempo. Para ello estimamos las sensibilidades de la función de pérdida esperada en la industria bancaria del cada área local frente a shocks contemporáneos en las funciones de pérdida esperada de las áreas extranjeras utilizando un sistema de ecuaciones dinámico. Controlamos por exposiciones globales a factores comunes y consideramos diferentes escenarios o estados de inestabilidad económica. Para asegurar la robustez de las estimaciones frente a la endogeneidad implementamos una estimación instrumental y calculamos las funciones de impulso respuesta para analizar la duración esperada del contagio en las colas. El estudio revela que las vulnerabilidades cruzadas entre países dependen del estado de la economía de tal forma que se incrementan durante periodos de gran inestabilidad económica y tienen un mayor efecto a largo plazo en el resto de sistemas. Por el contrario, para periodos más tranquilos los shocks tienen muy poco impacto y son rápidamente absorbidos por los sistemas domésticos. El análisis también muestra evidencia acerca de la existencia de direccionalidad en el contagio siendo US el sector bancario que produce mayores contagios y el más resistente frente a shocks en otras áreas. Obtenemos también que las exposiciones sistemáticas al área de Europa Central son más significativas que a la Europa Periférica, siendo US el país más vulnerable frente a shocks originados en Europa Central. Finalmente, US y la Eurozona son sensibles frente a shocks en el sistema bancario de la zona de países emergentes.

Información de financiamento

Financial support from the Spanish Department of Education and Innovation (projects ECO2012-33619 and ECO2014-58434P) is gratefully acknowledged.

Financiadores

  • Spanish Department of Education and Innovation Spain
    • ECO2012-33619
    • ECO2014-58434P

Referencias bibliográficas

  • Acerbi, C. and Tasche, D. (2002): “On the Coherence of Expected Shortfall”, Journal of Banking and Finance, vol. 26(7), pp. 1487-1503.
  • Acharya, V.V., Pedersen, L., Philippon, T. and Richardson, M. (2010): “Measuring Systemic Risk”, Technical report, Department of Finance, NYU.
  • Acharya, V.V. and Schnabl, P. (2010): “Do Global Banks Spread Global Imbalances? The Case of Asset-Backed Commercial Paper During the Financial Crisis of 2007-09", IMF Economic Review, vol. 58(1), pp. 37-73.
  • Adams, Z., Füss, R. and Gropp, R. (2014): “Spillover Effects among Financial Institutions: A State-Dependent Sensitivity Value-at-Risk (SDSVaR) Approach”, Journal of Financial and Quantitative Analysis, vol. 49(3), pp. 575-598.
  • Adrian, T. and Brunnermeier, M.K (2011): “CoVaR”, Working Paper, Princeton University.
  • Aigner, D.J., Amemiya, T. and Poirier, D.J. (1976): “On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function”, International Economic Review, vol. 17(2), pp. 377-396.
  • Amemiya, T. (1982): “Two Stage Least Absolute Deviations Estimators”, Econometrica, vol. 50(3), pp. 689-711.
  • Ang, A., Chen, J. and Xing, Y. (2006): “Downside Risk”, Review of Financial Studies, vol. 19(4), pp. 1191-1239.
  • Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999): “Coherent Measures of Risk”, Mathematical Finance, vol. 9(3), pp. 203-228.
  • Baele, L. (2005): “Volatility Spillover Effects in European Equity Markets”, Journal of Financial and Quantitative Analysis, vol, 40(2), pp. 373-401.
  • Ballester, L., Casu, B. and Gonzalez-Urteaga, A (2014): “Bank Fragility and Contagion: Evi -dence from the CDS market”, Working Paper.
  • Basel Committee on Banking Supervision (2011): “Global Systemically Important Banks: Assessment Methodology and the Additional Loss Absorbency Requirement”.
  • Becker, K., Finnerty, J. and Gupta, M. (1990): “The Intertemporal Relation between the US and the Japanese Stock Markets”, Journal of Finance, vol. 45(4), pp. 1297-1306.
  • Berkowitz, J. (2001): “Testing Density Forecasts with Applications to Risk Management”, Journal of Business and Economic Statistics, vol. 19(4), pp. 465-474.
  • Berkowitz, J., Christoffersen, P.F. and Pelletier, D. (2011): “Evaluating Value-at-Risk Models with Desk-Level Data”, Management Science, vol. 57(12), pp. 2213-2227.
  • Brownlees, C.T. and Engle, R (2012): “Volatility, Correlation and Tails for Systemic Risk Measurement”, Working Paper, NYU Stern School of Business.
  • Buchholz, M. and Tonzer, L (2013): “Sovereign Credit Risk Co-movements in the Eurozone: Simple Interdependence or Contagion?", Working Paper.
  • Cheung, W., Fung, S. and Tsai, S.C. (2010): “Global Capital Market Interdependence and Spillover Effect of Credit Risk: Evidence from the 2007-2009 Global Financial Crisis”, Applied Financial Economics, vol. 20(1-2), pp. 85-103.
  • Chevapatrakul, T. and Paez-Farrell, J. (2013): “What determines the Sacrifice Ratio? A Quantile Regression Approach”, Economics Bulletin, vol. 33(3), pp. 1863-1874.
  • Christoffersen, P.F. (1998): “Evaluating Interval Forecasts”. International Economic Review, vol. 39(4), pp. 841-862.
  • Christoffersen, P.F. and Pelletier, D. (2004). “Backtesting Value-at-Risk: A Duration-Based Approach”, Journal of Financial Econometrics, vol. 2(1), pp. 84-108.
  • Degryse, H., Elahi, M.A. and Penas, M.F. (2010): “Cross-Border Exposures and Financial Contagion”, International Review of Finance, vol. 10(2), pp. 209-240.
  • De Rossi, G. and Harvey, A.C. (2009): “Quantiles, Expectiles, and Splines”, Journal of Econometrics, vol. 152(2), pp. 179-185.
  • Diebold, F.X. and Yilmaz, K. (2012): “Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillover”, International Journal of Forecasting, vol. 28(1), pp. 57-66.
  • Dungey, M., Fry, R., Gonzalez-Hermosillo, B. and Martin, V. (2005): “Empirical Modelling of Contagion: A Review of Methodologies”, Quantitative Finance, vol. 5(1), pp. 9-24.
  • Efron, B. (1991): “Regression Percentiles using Asymmetric Squared Error Loss”, Statistica Sinica, vol. 1, pp. 93-125.
  • Engle, R.F., Ito T. and Lin W. (1990): “Meteor Shower or Heat Wave? Heteroskedastic Intradaily Volatility in the Foreign Exchange Market”, Econometrica, vol. 58(3), pp. 525-542.
  • Engle, R.F. and Manganelli, S. (2004): “CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles”, Journal of Business and Economic Statistics, 22(4), pp. 367-381.
  • Eun, C.S. and Shim, S. (1989): “International Transmission of Stock Market Movement”, Journal of Financial and Quantitative Analysis, vol. 24(1), pp. 241-260.
  • Hamao, Y., Masulis, R.W. and Ng, V. (1990): “Correlations in Price Changes and Volatility across International Stock Markets”, Review of Financial Studies, vol. 3(2), pp. 281-307.
  • International Monetary Fund (2011a): “Changing Patterns of Global Trade”, Prepared by the Strategy, Policy, and Review Department, Washington.
  • International Monetary Fund (2014): “World Economic Outlook: Recovery Strengthens, Remains Uneven”, IMF, Washington.
  • Jin, X., Lin, X. and Tamvakis, M. (2012): “Volatility Transmission and Volatility Impulse Response Functions in Crude Oil Markets”, Energy Economics, vol. 34(6), pp. 2125-2134.
  • Kim, J.Y. and Hwang, J.B (2012): “Extreme Risk Spillover in Financial Markets: Evidence from the Recent Financial Crisis”, Working Paper.
  • Kim, T.H. and Muller, H. (2004): “Two-stage Quantile Regression when the First Stage is based on Quantile Regression”, The Econometrics Journal, vol. 7(1), pp. 218-231.
  • King, M. and Wadhwani, S. (1990): “Transmission of Volatility between Stock Markets”, The Review of Financial Studies, vol. 3(1), pp. 5-33.
  • Koenker, R (2005): “Quantile Regression”, Econometric Society Monograph Series, Cambridge University Press, Cambridge.
  • Kuan, C.M., Yeh, J. and Hsu, Y.C. (2009): “Assessing Value at Risk with CARE, the Conditional Autoregressive Expectile Models”, Journal of Econometrics, vol. 150(2), pp. 261-270.
  • Kupiec, P.H. (1995): “Techniques for Verifying the Accuracy of Risk Measurement Models”, Journal of Derivatives, vol. 3(2), pp. 73-84.
  • Longstaff, F.A. (2010): “The Subprime Credit Crisis and Contagion in Financial Markets”, Journal of Financial Economics, vol. 97(3), pp. 436-450.
  • López-Espinosa, G., Moreno, A., Rubia, A. and Valderrama, L. (2012): “Short-term Wholesale Funding and Systemic risk: A Global Covar approach”, Journal of Banking and Finance, 36(12), pp. 3150-3162.
  • López-Espinosa, G., Moreno, A., Rubia, A. and Valderrama, L. (2015): “Systemic Risk and Asymmetric Responses in the Financial Industry”, Journal of Banking and Finance, vol. 58, pp. 471-485.
  • Ludwig, A. and Sobański, K. (2014): “Banking Sector Fragility Linkages in the Euro Area: Evidence for Crisis years 2007-2010", Economics Letters, vol. 125(3), pp. 451-454.
  • McGuire, P. and Von Peter, G (2009): “The US dollar Shortage in Global Banking”, Working Paper, BIS.
  • McNeil, A.J., Frey, R. and Embrechts, P (2005): “Quantitative Risk Management: Concepts, Techniques, and Tools”, Princeton University Press, Princeton.
  • Newey, W.K. and Powell, J. L. (1987): “Asymmetric Least Squares Estimation and Testing”, Econometrica, 55(4), pp. 819-847.
  • Panopoulou, E. and Pantelidis, T. (2009): “Integration at a Cost: Evidence from Volatility Impulse Response Functions”, Applied Financial Economics, vol. 9(11), pp. 917-933.
  • Poon, S.H. and Granger, C.W.J. (2003): “Forecasting Volatility in Financial Markets: A Review”, Journal of Economic Literature, vol. 41(2), pp. 478-539.
  • Powell, J.L. (1983): “The Asymptotic Normality of Two-Stage Least Absolute Deviations Estimators”, Econometrica, vol. 51(5), pp. 1569-1575.
  • Rodríguez-Moreno, M. and Peña, J.A. (2013): “Systemic Risk Measures: The Simpler the Better?", Journal of Banking and Finance, vol. 37(6), pp. 1817-1831.
  • Segoviano, M. and Goodhart, C (2009): “Banking Stability Measures”, Working Paper, IMF.
  • Shiller, R.J. (1989): “Comovements in Stock Prices and Comovements in Dividends”, Journal of Finance, vol. 44(3), pp. 719-729.
  • Susmel, R. and Engle, R.F. (1994): “Hourly Volatility Spillovers between International Equity Markets”, Journal of International Money and Finance, vol. 13(1), pp. 3-25.
  • Taylor, J.W. (2008a): “Estimating Value at Risk and Expected Shortfall using Expectiles”, Journal of Financial Econometrics, vol. 6(2), pp. 231-252.
  • Taylor, J.W. (2008b): “Using Exponentially Weighted Quantile Regression to estimate Value at Risk and Expected Shortfall”, Journal of Financial Econometrics, vol. 6(3), pp. 382-406.
  • Taylor, S (1986): “Modelling Financial Time Series”, Wiley, New York.
  • Theodossiou, P. and Lee, U. (1993): “Mean and Volatility Spillovers Across Major National Stock Markets: Further Empirical Evidence”, Journal of Financial Research, 16(4), pp. 337-350.
  • Tressel, H (2010): “Financial Contagion through Bank Deleveraging: Stylized Facts and Simulations Applied to the Financial Crisis”, Working Paper, IMF.
  • Vinod, H.D. and López-de-Lacalle, J. (2009): “Maximum Entropy Bootstrap for Time Series: The meboot R Package”, Journal of Statistical Software, vol. 29(5), pp. 1-19.
  • Weistroffer, C. and Möbert, J (2010): “Monitoring Cross Border Exposure”, Deutsche Bank Research.
  • Yao, Q. and Tong, H. (1996): “Asymmetric Least Squares Regression Estimation: a Nonpara -metric Approach”, Journal of Nonparametric Statistics, vol. 6(2-3), pp. 273-292.