دوره 11، شماره 41 - ( 7-1399 )
سال11 شماره 41 صفحات 196-145 |
برگشت به فهرست نسخه ها
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-fa.html
URL: http://jemr.khu.ac.ir/article-1-2043-fa.html
ناصری سید علی، جبل عاملی فرخنده، برخورداری دورباش سجاد. بررسی همبستگی بانک‌های منتخب با مدل همبستگی شرطی پویا (DCC) و شناسایی بانک‌های دارای اهمیت سیستمیک با روش ارزش در معرض خطر شرطی و ارزش شیپلی. تحقیقات مدلسازی اقتصادی. 1399; 11 (41) :145-196
1- دانشگاه تهران ، salinaseri@yahoo.com
2- دانشگاه تهران
2- دانشگاه تهران
چکیده: (3970 مشاهده)
ریسک سیستمیک در اثر حرکت هم‌زمان یا همبستگی بین بخش‌های بازار ایجاد می‌شود؛ بنابراین ریسک سیستمیک زمانی اتفاق می‌افتد که همبستگی بالایی بین ریسک‌ها و بحران‌های بخش‌های مختلف بازار یا موسسات فعال در اقتصاد وجود داشته باشد یا زمانی که ریسک‌های بخش‌های مختلف در یک بخش از بازار یا یک کشور با سایر بخش‌ها و کشورهای دیگر مرتبط و همبسته باشد. در این مقاله یک سنجه برای محاسبه ریسک سیستمیک به منظور توصیف کارای اهمیت سیستمیک هر موسسه مالی در یک سیستم ارایه شده است. برای بررسی همبستگی متغیر در زمان بانک¬های مختلف به یکدیگر از متدولوژی DCC-GARCH با توزیع‌های نرمال و t-استیودنت استفاده شده است. نتایج این بخش نشان می‌دهد که کاربست مدلDCC-GARCH-student-t نسبت به مدل DCC-GARCH-normalارجحیت دارد. به منظور بررسی وجود اثر اهرمی از مدل GJR-GARCH استفاده گردید و نتایج حاصل از تخمین، وجود عدم تقارن و عدم وجود اثر اهرمی را در داده¬ها نشان داد. در بررسی همبستگی شرطی پویای بین بانک¬های منتخب نیز ملاحظه می‌شود که α_C .β_C برای هر دو حالت تخمین معنادار نیستند در نتیجه در هر دو حالت برآورد شده بر اساس توزیع نرمال و t-استیودنتα_C=β_C=0 بوده و مدل به همبستگی شرطی ثابت تبدیل می‌شود.
بر اساس نتایج حاصل از ارزش شیپلی و به منظور تخصیص ریسک کل بین بانک¬های موجود در نمونه، به ترتیب بانک¬های پارسیان، ملت، اقتصاد نوین، تجارت و صادرات دارای بیشترین اهمیت سیستمیکی برای دوره زمانی مورد بررسی از 27/03/1388 تا 17/02/1398 هستند
فهرست منابع
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]
ارسال پیام به نویسنده مسئول
| بازنشر اطلاعات | |
 | این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
