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The effectiveness of TARP-CPP on the US banking industry: a new copula-based approach. (English) Zbl 1395.91484

Summary: Following the 2008 financial crisis, regulatory authorities and governments provided distressed banks with equity infusions in order to strengthen national banking systems. However, the effectiveness of these interventions for financial stability has not been extensively researched in the literature. In order to understand the effectiveness of these bailouts for the solvency of banks, this paper proposes a new model: the longitudinal binary generalised extreme value (LOBGEV) model. Differing from the existing models, the LOBGEV model allows us to analyse the temporal structure of the probability of failure for banks, for both those that received a bailout and for those that did not. In particular, it encompasses both the flexibility of the D-vine copula and the accuracy of the generalised extreme value model in estimating the probability of bank failure and of banks receiving approval for capital injection. We apply this new model to the US banking system from 2008 to 2013 in order to investigate how and to what extent the troubled asset relief program (TARP)-capital purchase program (CPP) reduced the probability of the failure of commercial banks. We specifically identify a set of macroeconomic and bank-specific factors that affect the probability of bank failure for TARP-CCP recipients and for those that did not receive capital under TARP-CCP. Our results suggest that TARP-CPP provided only short-term relief for US commercial banks.

MSC:

91G50 Corporate finance (dividends, real options, etc.)
62P05 Applications of statistics to actuarial sciences and financial mathematics
62H05 Characterization and structure theory for multivariate probability distributions; copulas
91G70 Statistical methods; risk measures

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References:

[1] Aas, K.; Berg, D., Models for construction of multivariate dependence: A comparison study, The European Journal of Finance, 15, 7, 639-659 (2009)
[2] Aas, K.; Czado, C.; Frigessi, A.; Bakken, H., Pair-copula constructions of multiple dependence, Insurance: Mathematics \(\&\) Economics, 44, 2, 182-198 (2009) · Zbl 1165.60009
[3] Andreeva, G.; Calabrese, R.; Osmetti, S., A comparative analysis of the UK and Italian small businesses using generalised extreme value models, European Journal of Operational Research, 249, 2, 506-516 (2016)
[4] Arellano, M.; Bo, H., Panels data models:some recent developments, (Heckman, J. J.; Learner, E., Handbook of econometrics, 5 (2001), Elsevier Science B V), 3229-3296
[5] Arena, M., Bank failures and bank fundamentals: A comparative analysis of Latin America and east asia during the nineties using bank-level data, Journal of Banking \(\&\) Finance, 32, 2, 299-310 (2008)
[6] Bayazitova, D.; Shivdasani, A., Assessing tarp, Review of Financial Studies, 25, 2, 377-407 (2012)
[7] Bedford, T.; Cooke, R., Vines: A new graphical model for dependent random variables, Annals of Statistics, 30, 4, 1031-1068 (2002) · Zbl 1101.62339
[8] Berger, A. N.; Bouwman, C. H., How does capital affect bank performance during financial crises?, Journal of Financial Economics, 109, 1, 146-176 (2013)
[10] Berger, A. N.; Roman, R. A., Did TARP Banks get competitive advantages?, Journal of Financial and Quantitative Analysis, 50, 6, 1199-1236 (2015)
[11] Black, L. K.; Hazelwood, L. N., The effect of tarp on bank risk-taking, Journal of Financial Stability, 9, 4, 790-803 (2013)
[12] Calabrese, R.; Giudici, P., Estimating bank default with generalised extreme value regression models, Journal of the Operational Research Society, 66, 11, 1783-1792 (2015)
[13] Calabrese, R.; Marra, G.; Osmetti, S. A., Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model, Journal of the Operational Research Society, 67, 4, 604-615 (2016)
[14] Calabrese, R.; Osmetti, S., Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model, Journal of Applied Statistics, 40, 6, 1172-1188 (2013) · Zbl 1514.62453
[15] Calabrese, R.; Osmetti, S., Improving forecast of binary rare events data: A gam-based approach, Journal of Forecasting, 34, 3, 230-239 (2015) · Zbl 1397.62334
[16] Calderon, C.; Schaeck, K., The effects of government interventions in the financial sector on banking competition and the evolution of zombie banks, Journal of Financial and Quantitative Analysis (2016)
[17] Cole, R. A.; White, L. J., Déjà vu all over again: The causes of us commercial bank failures this time around, Journal of Financial Services Research, 42, 1-2, 5-29 (2012)
[18] Croci, E.; Hertig, G.; Nowak, E., Decision-making during the crisis: Why did the treasury let commercial banks fail?, Proceedings of European corporate governance institute (ECGI) (2015)
[19] Czado, C., Pair-copula constructions of multivariate copulas, Copula theory and its applications, 93-109 (2010), Springer
[20] Daeyoung, K.; Jong-Min, K.; Shu-Min, L.; Yoon-Sung, J., Mixture of d-vine copulas for modeling dependence, Computational Statistics \(\&\) Data Analysis, 64, 2, 1-19 (2013) · Zbl 1468.62099
[21] Dam, L.; Koetter, M., Bank bailouts and moral hazard: Evidence from germany, Review of Financial Studies, 25, 8, 2343-2380 (2012)
[22] Demyanyk, Y.; Hasan, I., Financial crises and bank failures: A review of prediction methods, Omega, 38, 5, 315-324 (2010)
[23] DeYoung, R.; Torna, G., Nontraditional banking activities and bank failures during the financial crisis, Journal of Financial Intermediation, 22, 3, 397-421 (2013)
[24] Duchin, R.; Sosyura, D., Safer ratios, riskier portfolios: Banks response to government aid, Journal of Financial Economics, 113, 1, 1-28 (2014)
[25] Falk, M.; Husler, J.; Reiss, R.-D., Laws of small numbers: Extremes and rare events (2010), Springer
[26] Fethi, M. D.; Pasiouras, F., Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey, European Journal of Operational Research, 204, 2, 189-198 (2010) · Zbl 1178.90228
[27] Gropp, R.; Hakenes, H.; Schnabel, I., Competition, risk-shifting, and public bail-out policies, Review of Financial Studies, 24, 6, 2084-2120 (2011)
[28] Hakenes, H.; Schnabel, I., Banks without parachutes: Competitive effects of government bail-out policies, Journal of Financial Stability, 6, 3, 156-168 (2010)
[29] Ioannidis, C.; Pasiouras, F.; Zopounidis, C., Assessing bank soundness with classification techniques, Omega, 38, 5, 345-357 (2010)
[30] Joe, H., Multivariate models and multivariate dependence concepts (1997), CRC Press · Zbl 0990.62517
[31] King, G.; Zeng, L., Explaining rare events in international relations, International Organization, 55, 3, 693-715 (2001)
[32] Koetter, M.; Bos, J. W.; Heid, F.; Kolari, J. W.; Kool, C. J.; Porath, D., Accounting for distress in bank mergers, Journal of Banking \(\&\) Finance, 31, 10, 3200-3217 (2007)
[33] Koetter, M.; Noth, F., Did tarp distort competition among sound unsupported banks?, Economic Inquiry (2015)
[34] Koleva, N.; Paivab, D., Copula-based regression models: A survey, Journal of Statistical Planning and Inference, 139, 11, 3847-3856 (2009) · Zbl 1169.62328
[35] Kraemer, N.; Brechmann, E. C.; Silvestrini, D.; Czado, C., Total loss estimation using copula-based regression models, Insurance: Mathematics and Economics, 53, 3, 829-839 (2013) · Zbl 1290.91092
[36] Kumar, P. R.; Ravi, V., Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review, European Journal of Operational Research, 180, 1, 1-28 (2007) · Zbl 1114.91305
[37] Laeven, L.; Valencia, F., Resolution of banking crises: The good, the bad, and the ugly (2010), International Monetary Fund
[38] Liu, W.; Kolari, J. W.; Tippens, T. K.; Fraser, D. R., Did capital infusions enhance bank recovery from the great recession?, Journal of Banking \(\&\) Finance, 37, 12, 5048-5061 (2013)
[40] Meade, N.; Islam, T., Using copulas to model repeat purchase behaviour an exploratory analysis via a case study, European Journal of Operational Research, 200, 3, 908-917 (2010) · Zbl 1177.90233
[41] Panagiotelis, A.; Czado, C.; Harry, J., Pair copula constructions for multivariate discrete data, Journal of the American Statistical Association, 499, 107, 1063-1072 (2012) · Zbl 1395.62114
[42] Radice, R.; Marra, G.; Wojtyś, M., Copula regression spline models for binary outcomes, Statistics and Computing (2015) · Zbl 1505.62328
[44] Savu, C.; Trede, M., Hierarchies of archimedean copulas, Quantitative Finance, 10, 3, 295-304 (2010) · Zbl 1270.91086
[45] Smith, M., Copula modelling of dependence in multivariate time series, International Journal of Forecasting, 31, 815-833 (2015)
[46] Smith, M.; Khaled, M., Estimation of copula models with discrete margins via bayesian data augmentation, Journal of the American Statistical Association, 107, 497, 290-303 (2012) · Zbl 1261.62051
[47] Smith, M.; Min, A.; Almeida, C.; Czado, C., Modeling longitudinal data using a pair-copula decomposition of serial dependence, Journal of the American Statistical Association, 402, 105, 1467-1479 (2010) · Zbl 1388.62171
[48] Wang, X.; Dey, D. K., Generalized extreme value regression for binary response data: An application to b2b electronic payments system adoption, The Annals of Applied Statistics, 4, 4, 2000-2023 (2010) · Zbl 1220.62165
[49] Wheelock, D. C.; Wilson, P. W., Why do banks disappear? The determinants of us bank failures and acquisitions, Review of Economics and Statistics, 82, 1, 127-138 (2000)
[50] Whelan, N., Sampling from Archimedean copulas, Quantitative finance, 4, 3, 339-352 (2004) · Zbl 1409.62108
[51] Wilson, L., Tarp’s deadbeat banks, Review of Quantitative Finance and Accounting, 41, 4, 651-674 (2013)
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