• Zaafri Ananto Husodo Universitas Indonesia, Department of Management, Faculty of Economics and Business
  • Sigit Sulistyo Wibowo Universitas Indonesia, Department of Management, Faculty of Economics and Business
  • Muhammad Budi Prasetyo Universitas Indonesia, Department of Management, Faculty of Economics and Business
  • Usman Arief
  • Maulana Harris Muhajir Bank Indonesia, Department of Macroprudential Policy
Keywords: Copula, Pair copula construction, Systemic risk, Financial system


We develop a joint default probability index to signal potential systemic risks in the highly concentrated Indonesian banking industry. To build the index, we estimate bank-level tail risks using monthly bank financial reports. We use the copula approach to derive the joint multivariate dependencies at the bank level, as reflected in the monthly financial reports. Our results, which are based on a sample of 104 banks fromDecember 2003 to April 2020, show joint multivariate dependencies at the bank level suggesting that the standard univariate normal distribution is unsuitable for capturing tail risks of individual banks. Our index accurately captures the global financial crisis of 2007-2008 indicating that it is a valid joint default probability index. Further, our index also signaled a higher degree of joint default before the COVID-19 outbreak in2020, suggesting that it is a good indicator of potential systemic risk in the economy.


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Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44, 182–198.

Acharya, V., Engle, R., & Pierret, D. (2014). Testing Macroprudential Stress Tests: The Risk of Regulatory Risk Weights. Journal of Monetary Economics, 65, 36–53.

Aini, M., & Koesrindartoto, D. P. (2020). The Determinants of Systemic Risk: Evidence from Indonesian Commercial Banks. Buletin Ekonomi Moneter Dan Perbankan, 23, 101–120.

Basel Committee on Banking Supervision. (2018). Global systemically Important Banks: Revised Assessment Methodology and the Higher Loss Absorbency Requirement.

Bedford, T., & Cooke, R. M. (2001). Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines. Annals of Mathematics and Artificial Intelligence, 32, 245–268.

Bedford, T., & Cooke, R. M. (2002). Vines: A new graphical model for dependent random variables. The Annals of Statistics, 30, 1031–1068.

Benoit, S., Colliard, J.-E., Hurlin, C., & Pérignon, C. (2017). Where the risks lie: A survey on systemic risk. Review of Finance, 21, 109–152.

Bisias, D., Flood, M., Lo, A. W., & Valavanis, S. (2012). A survey of systemic risk analytics. Annual Review of Financial Economics, 4, 255–296.

Black, F. (1986). Noise. The Journal of Finance, 41, 528–543.

Brechmann, E. C., Hendrich, K., & Czado, C. (2013). Conditional copula simulation for systemic risk stress testing. Insurance: Mathematics and Economics, 53, 722–732.

Cooke, R. M. (1997). Markov and Entropy Properties of Tree- and Vine-Dependent Variables. Proceedings of the Section on Bayesian Statistical Science (Vol. 27). American Statistical Association.

Cooke, R. M., Joe, H., & Aas, K. (2011). Vines Arise. In D. Kurowicka (Ed.), Dependence Modeling: Vine Copula Handbook (pp. 38–71). World Scientific Publishing Co Pte Ltd.

Devpura, N., & Narayan, P.K., (2020) Hourly Oil Price Volatility: The role of COVID-19. Energy Research Letters, 1(2), 13683.

Drehmann, M., & Tsatsaronis, K. (2014). The Credit-To-GDP Gap and Countercyclical Capital Buffers: Questions and Answers. BIS Quarterly Review. March, 55-73.

Greenwood, R., Landier, A., & Thesmar, D. (2015). Vulnerable banks. Journal of Financial Economics, 115, 471–485.

Hansen, L. P. (2014). Challenges in Identifying and Measuring Systemic Risk. In M. K. Brunnermeier & A. Krishnamurthy (Eds.), Risk Topography: Systemic Risk and Macro Modeling. The University of Chicago Press.

Haroon, O., and Rizvi, S.A.R. (2020). Flatten the Curve and Stock Market Liquidity—An Inquiry Into Emerging Economies. Emerging Markets Finance and Trade, 56, 2151-2161.

Harun, C. A., Rachmanira, S., & Nattan, R. R. (2015). Kerangka Pengukuran Risiko Sistemik (Bank Indonesia Occasional Paper, OP/4/2015). Bank Indonesia.

Henker, T., & Husodo, Z. A. (2010). Noise and efficient variance in the Indonesia Stock Exchange. Pacific-Basin Finance Journal, 18, 199–216.

Iyke, B. (2020a). COVID-19: The Reaction of US Oil and Gas Producers to the Pandemic. Energy Research Letters, 1(2), 13912.

Iyke, B.N. (2020b). The Disease Outbreak Channel Of Exchange Rate Return Predictability: Evidence from COVID-19. Emerging Markets Finance and Trade, 56, 2277-2297.

Jacobs, H. (2016). Market Maturity and Mispricing. Journal of Financial Economics, 122, 270–287.

Joe, H. (1993). Parametric Families of Multivariate Distributions with Given Margins. Journal of Multivariate Analysis, 46, 262–282.

Joe, H. (1996). Families of M-Variate Distributions with Given Margins and M(M-1)/2 Bivariate Dependence Parameters. Institute of Mathematical Statistics Lecture Notes - Monograph Series, 120–141.

Juhro, S. M., & Iyke, B. N. (2019). Monetary Policy and Financial Conditions in Indonesia. Buletin Ekonomi Moneter dan Perbankan, 21, 283-302.

Kurowicka, D., & Cooke, R. M. (2006). Uncertainty Analysis with High Dimensional Dependence Modelling. John Wiley & Sons, Ltd.

Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. The Journal of Finance, 29, 449–470.

Mishra, A.K., Rath, B.N., & Dash, A.K., (2020) Does the Indian financial market nosedive because of the COVID-19 outbreak, in comparison to after demonetisation and the GST? Emerging Markets Finance and Trade, 56, 2162-2180.

Narayan, P., K. (2020). Oil price news and COVID-19—Is there any connection?. Energy Research Letter, 1, 1-5, 13176.

Patton, A. J. (2006). Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47, 527–556.

Phan, D.H.B., & Narayan, P.K., (2020) Country Responses and the Reaction of the Stock Market to COVID-19—a Preliminary Exposition, Emerging Markets Finance and Trade, 56, 2138-2150.

Pourkhanali, A., Kim, J.-M., Tafakori, L., & Fard, F. A. (2016). Measuring Systemic Risk Using Vine-Copula. Economic Modelling, 53, 63–74.

Prabheesh, K.P., Padhan, R., & Garg, B. (2020) COVID-19 and the oil price—stock market nexus: Evidence from net oil-importing countries, Energy Research Letters, 1, 13745.

Ramelli, S., & Wagner, A. F. (2020). Feverish Stock Price Reactions to the Novel Coronavirus. SSRN Electronic Journal.

Rosenberg, J. V., & Schuermann, T. (2006). A General Approach to Integrated Risk Management with Skewed, Fat-Tailed Risks. Journal of Financial Economics, 79, 569–614.

Salisu, A., A & Akanni, L., O. (2020). Constructing a Global Fear Index for the COVID-19 Pandemic. Emerging Markets Finance and Trade, 56, 2310-2331.

Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Université de Paris, 8, 229–231.

Valle, D. L., De Giuli, M. E., Tarantola, C., & Manelli, C. (2016). Default probability estimation via pair copula constructions. European Journal of Operational Research, 249, 298–311.

Vidya, C. T., and Prabheesh, K.P. (2020). Implications of COVID-19 Pandemic On The Global Trade Networks. Emerging Markets Finance and Trade, 56, 2408-2421.

Zhang, D. (2014). Vine Copulas And Applications To The European Union Sovereign Debt Analysis. International Review of Financial Analysis, 36, 46–56.

How to Cite
Husodo, Z., Wibowo, S., Prasetyo, M., Arief, U., & Muhajir, M. (2020). ESTIMATING A JOINT PROBABILITY OF DEFAULT INDEX FOR INDONESIAN BANKS: A COPULA APPROACH. Buletin Ekonomi Moneter Dan Perbankan, 23(3), 389 - 412.