Volume 11, Issue 41 (10-2020)
jemr 2020, 11(41): 145-196 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Naseri S A, Jabal Ameli F, Barkhordary Dorbash S. Investigating the Correlation of Selected Banks with Dynamic Conditional Correlation (DCC) Model and Identifying Systemically Important Banks with Conditional Value at Risk and Shapley Value Method. jemr 2020; 11 (41) :145-196
URL: http://jemr.khu.ac.ir/article-1-2043-en.html
URL: http://jemr.khu.ac.ir/article-1-2043-en.html
1- University of Tehran , salinaseri@yahoo.com
2- University of Tehran
2- University of Tehran
Abstract: (4141 Views)
Systemic risk arises from simultaneous movement or correlations between market segments; Thus, systemic risk occurs when there is a high correlation between the risks and crises of different market segments or institutions operating in the economy, or when the risks of different segments in a market segment or a country are related to other segments and other countries. This paper presents a measure of systemic risk calculation to effectively describe the systemic importance of each financial institution in a system. The DCC-GARCH methodology with normal and t-student distributions has been used to examine the correlation of time-varying banks. The results of this section show that the application of DCC-GARCH-student-t model is preferable to DCC-GARCH-normal model. In order to investigate the presence of leverage effect, GJR-GARCH model was used and the results of estimation showed the presence of asymmetry and the absence of leverage effect in the data. In the study of dynamic conditional correlation between selected banks, it is also observed that α_C ,β_C are not significant for both estimation cases. Therefore, in both cases, it is estimated based on the normal distribution and t-student α_C=β_C=0 and the conditional correlation becomes constant. Based on the results of shapley value and in order to allocate the total risk between the banks in the sample, Parsian, Mellat, EN, Tejarat and Saderat banks have the most systemic importance for the period of June 17, 2009 to May 7, 2019.
Type of Study: Applicable |
Subject:
سایر
Received: 2020/06/1 | Accepted: 2020/12/2 | Published: 2021/01/10
Received: 2020/06/1 | Accepted: 2020/12/2 | Published: 2021/01/10
References
1. Acharya, V. & Pedersen, L. H. & Philippon, T. & Richardson, M. P. (2010). measuring systemic risk, FRB of Cleveland Working Paper. [DOI:10.26509/wp-201002]
2. Acharya, V. & Engle, R.,& Richardson, M. (2012). Capital shortfall: A new approach to ranking and regulating systemic risks, The American Economic Review, 102 (3). [DOI:10.1257/aer.102.3.59]
3. Adrian, T. & Brunnermeier, M.K. (2009). CoV aR, Staff Reports 348, Federal Reserve Bank of New York.
4. Adrian, T & Brunnermeier, M.K. (2014). CoVaR, Staff Reports, (106)7, 1705-1741. [DOI:10.1257/aer.20120555]
5. Ali, R. & Vause, N. & Zikes, F. (2016). Systemic Risk in Derivatives Markets: A Pilot Study Using CDS Data, Bank of England Financial Stability Papers, 8, 3-21. [DOI:10.2139/ssrn.2809667]
6. Andersson, S.T. & Lindskog, J. (2019). A study on the DCC-GARCH model's forcasting ability with value-at-risk applications on the Scandinavian foreign exchange market, Independent thesis Basic level (degree of Bachelor).
7. Arias, M. & Mendoza, J. & Pérez-Reyna, D. (2010). Applying CoVaR to Measure systemic market risk: the Colombian case, Working Paper, Bogotá: Banco de la Republica. [DOI:10.32468/tef.47]
8. Baillie, R. & Bollerslev, T. (1989). The Message in Daily Exchange Rates: A Conditional-Variance Tale, Journal of Business & Economic Statistics, (7)3, 297-305. [DOI:10.1080/07350015.1989.10509739]
9. Banulescu, G.D. & Dumitrescu, E.I. (2015). Which are the SIFIs? A Component Expected Shortfall approach to systemic risk,. Journal of Banking & Finance, 50, 575-588. [DOI:10.1016/j.jbankfin.2014.01.037]
10. Benoit, S. & Colletaz, G. & Hurlin, C. & Pérignon, C. (2013). A Theoretical and Empirical Comparison of Systemic Risk Measures, HEC Paris Research Paper No. FIN-2014-1030. Available at SSRN: https://ssrn.com/abstract=1973950 or http://dx.doi.org/10.2139/ssrn.1973950 [DOI:10.2139/ssrn.1973950]
11. Billio, M. & Caporin, M. & Gobbo, M. (2005). Flexible Dynamic Conditional correlation Multivariate GARCH models for Asset Allocation.,Ca'Foscari University of Venice Department of Economics. [DOI:10.1080/17446540500428843]
12. Bollerslev, T. (1987). A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return, the Review of Economics and Statistics, (69)3, 542-547. [DOI:10.2307/1925546]
13. Bollerslev, T. (1990). Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model, The Review of Economics and Statistics, (72)3, 498-505. [DOI:10.2307/2109358]
14. Bollerslev, T. & Wooldridge, J.M. (1992). Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances,. Econometric Reviews, 11, 143-172. [DOI:10.1080/07474939208800229]
15. Brownlees, C.T., & Engle, R. (2012). Volatility, correlation and tails for systemic risk measurement. Working paper. [DOI:10.2139/ssrn.1611229]
16. Brownlees, C. & Engle, R. (2016). SRISK: A Conditional Capital Shortfall Measure of Systemic Risk, Esrb Working Paper, (30)1, 48-79. [DOI:10.1093/rfs/hhw060]
17. Cao, Z. (2013). Multi-CoVaR and Shapley Value: A Systemic Risk Measure., Working Paper, Banque de France.
18. Clemente, AD. (2018). Estimating the Marginal Contribution to Systemic Risk by A CoVaR‐model Based on Copula Functions and Extreme Value Theory, Economic Notes, (47)4, 1-44. [DOI:10.1111/ecno.12095]
19. Dajčman, S. & Festić, M. (2012). Interdependence between the Slovenian and European Stock Markets - A DCC-Garch Analysis, Economic Research, (25)2, 379-395. [DOI:10.1080/1331677X.2012.11517513]
20. Derbali, A. & Hallara, S. (2015). Systemic risk of European financial institutions: Estimation and ranking by the Marginal Expected Shortfall, Research in International Business & Finance, (37)2, 32-40. [DOI:10.1016/j.ribaf.2015.10.013]
21. Derbali, A. & Hallara, S. (2015). Dependence of Default Probability and Recovery Rate in Structural Credit Risk Models: Empirical Evidence from Greece, International Journal of Management and Business Research , (5)2, 141-158.
22. Dias, D. & Fernando, L. & Lucena Aiube, A. & Baidya, T. & Nanda, K. (2016). Hedging stocks through commodity indexes: a DCC-GARCH approach, Journal of Financial and Quantitative Analysis,(51) 5 , 1545-1574.
23. Drakos, A.A. & Kouretas, G.P. (2014). Measuring Systemic Risk in Emerging Markets Using CoVaR. In Emerging Markets and the Global Economy. M. Arouri, S. Boubaker, dan D. K. Nguyen (eds). Oxford: United Kingdom. [DOI:10.1016/B978-0-12-411549-1.00012-0]
24. Drehman, M. & Tarashev, N. (2011). Systemic importance: Some simple indicators, BIS Quarterly Review, 25-37.
25. Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models, Journal of Business & Economic Statistics, (20) 3, 339-50. [DOI:10.1198/073500102288618487]
26. Engle, R, (2009). Anticipating Correlations: A New Paradigm for Risk Management, Princeton University Press. [DOI:10.1515/9781400830190]
27. Engle, R. & Gonzalez-Rivera, G. (1991). Semi parametric ARCH Models, Journal of Business & Economic Statistics, (9) 4, 345-59. [DOI:10.1080/07350015.1991.10509863]
28. Engle, R.F. & Sheppard, K. (2001). Correlation Multivariate GARCH:Theoretical and Empirical properties of Dynamic Conditional, NBER Working Paper no. 8554 Issued in October 2001 [DOI:10.3386/w8554]
29. Girardi, G. & Ergün, AT. (2013). Systemic risk measurement: Multivariate GARCH estimation of CoVaR, Social Science Electronic Publishing, (37) 8, 3169-3180. [DOI:10.1016/j.jbankfin.2013.02.027]
30. Glosten, L.R. & Jagannathan, R. & Runkle, D.E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance, (48) 5, 1779-1801. [DOI:10.1111/j.1540-6261.1993.tb05128.x]
31. Grieb, F. (2015). Systemic Risk and Hedge Funds, Social Science Electronic Publishing, (49) 2, 235-340. [DOI:10.2139/ssrn.2592639]
32. Hsieh, D.A. (1989). Testing for Nonlinear Dependence in Daily Foreign Exchange Rates, The Journal of Business, (62) 3, 339-68. [DOI:10.1086/296466]
33. Huang, W.Q. & Uryasev, S. (2018). The CoCVaR approach: systemic risk contribution measurement, Journal of Risk, 20(4), 75-93. [DOI:10.21314/JOR.2018.383]
34. Karimalis, EN. & Nomikos, NK. (2017). Measuring Systemic Risk in the European Banking Sector: A Copula CoVaR Approach, European Journal of Finance, 1, 1-38.
35. Kuhe, D.A. (2018). Modeling Volatility Persistence and Asymmetry with Exogenous Breaks in the Nigerian Stock Returns, CBN Journal of Applied Statistics ,(9) 1, 167-196.
36. Lin, E. & Sun, EW. & Yu, MT. (2016). Systemic Risk, Interconnectedness, and Non-core Activities in Taiwan Insurance Industry, Annals of Operations Research, 12, 1-25.
37. Liu, J. & Song, Q. & Qi, Y. & Rahman, S. & Sriboonchitta, S. (2020). Measurment of systemic risk in Global Financial Markets and Its Application in Forecasting Trading Decisions, Sustainability, MDPI, Open Access Journal, 12(10), 1-15. [DOI:10.3390/su12104000]
38. Longin, F. & Solnik, B. (1995). Is the Correlation in International Equity Returns Constant?, Journal of International Money and Finance, 14, 3-26. [DOI:10.1016/0261-5606(94)00001-H]
39. Mighri, Z. & Mansouri, F. (2013). Dynamic Conditional Correlation Analysis of Stock Market Contagion: Evidence from the 2007-2010 Financial Crises, International Journal of Economics and Financial Issues, (3) 3, 637-661.
40. Minović, J.Z. (2009).Modeling Multivariate Volatility Processes: Theory and Evidence, Theoretical and Applied Economics, (534) 5, 21-44.
41. Muharam, H. (2017). Measuring Systemic Risk of Banking in Indonesia: Conditional Value at Risk Model Application, Jurnal Ilmu Ekonomi, (6) 2, 301 - 318. [DOI:10.15408/sjie.v6i2.5296]
42. Orskaug, E. (2009). Multivariate DCC-GARCH Model, Norwegian University of Science and Technology, Department of Mathematical Sciences.
43. Palm, F. & Vlaar, P. (1997). Inflation differentials and excess returns in the European Monetary System, Journal of International Financial Markets, Institutions and Money, (7) 1, 1-20. [DOI:10.1016/S1042-4431(97)00008-5]
44. Pascual, L. & Nieto, M. & Ruiz, E. (2006). Bootstrap prediction intervals for VaR and ES in the context of GARCH models, Computational Statistics & Data Analysis, 50, 2293-2312. [DOI:10.1016/j.csda.2004.12.008]
45. Reboredo, JC. & Ugolini, A. (2015). Systemic risk in European sovereign debt markets: A CoVaR-copula approach, Journal of International Money & Finance, 51, 214-244. [DOI:10.1016/j.jimonfin.2014.12.002]
46. Restrepo, M. I. (2012). Estimating Portfolio Value at Risk with GARCH and MGARCH models, Perfil de Coyuntura Económica, 19, 77-92.
47. Sakti, M.R.P. & Masih, M. & Saiti, B. & Tareq, M.A. (2018). Unveiling the diversification benefits of Islamic equities and commodities: Evidence from multivariate-GARCH and continuous wavelet analysis, Managerial Financ, 44, 830-850. [DOI:10.1108/MF-08-2017-0278]
48. Smaga, P. (2014). The concept of systemic risk, LSE Research Online Documents on Economics 61214, London School of Economics and Political Science, LSE Library.
49. Espinosa, G. L. & Moreno, A. & Rubia, A. & Valderrama, L. (2015). Systemic risk and asymmetric responses in the financial industry, Journal of Banking & Finance,(58), 471-485. [DOI:10.1016/j.jbankfin.2015.05.004]
50. Tse, Y. K. (2000). A Test for Constant Correlations in a Multivariate GARCH Model, Journal of Econometrics, 98, 107-127. [DOI:10.1016/S0304-4076(99)00080-9]
51. Tse, Y. K. & Tsui, K. C. (2002). A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations, Journal of Business & Economic Statistics, (20) 3, 351-362 [DOI:10.1198/073500102288618496]
52. Wanat, S. & Denkowska, A. (2018). Dependencies and systemic risk in the European insurance sector: Some new evidence based on copula-DCC-GARCH model and selected clustering methods. Xiv:1905.03273.
53. Weiss, A. A. (1986). Asymptotic Theory for ARCH Models: Estimation and Testing, Econometric, (2) 1, 107-131. [DOI:10.1017/S0266466600011397]
54. Yu, I. W. & Fung, K. P. & Tam, Ch. S. (2010). Assessing financial market integration in Asia - Equity markets, Journal of Banking & Finance, (34) 12, 2874-2885. [DOI:10.1016/j.jbankfin.2010.02.010]
55. Zakoian, J. M. (1994). Threshold heteroskedastic models, Journal of Economic Dynamics and Control, (18) 5, 931-955. [DOI:10.1016/0165-1889(94)90039-6]
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
