Estimation of capital requirements in downturn conditions via the CBV model: Evidence from the Greek banking sector

• Main Authors: Konstantinos Papalamprou, Paschalis Antoniou
• Format: Article
• Language: English
• Published: Elsevier 2019-01-01
• Series: Operations Research Perspectives
• Online Access: http://www.sciencedirect.com/science/article/pii/S2214716017301847

One of the main drawbacks of the original CreditRisk+ methodology is that it models the default rates of the sectors (e.g. industry) as independently distributed random variables. Such an assumption has been considered as unrealistic and various approaches have been proposed in order to overcome this issue. To the best of our knowledge, such approaches have not been applied to portfolios associated with periods characterized by severe downturn economic conditions. In our work, apart from the standard CreditRisk+ model, we have also implemented two recent approaches that allow the dependence between sector default rates and can account for macroeconomic factors and have fed each model with portfolio data from a major Greek bank spanning the period 2008–2015. Based on our empirical analysis, it became evident that among the three models only the CBV model, incorporating a nonlinear (and nonconvex) mathematical programming procedure, could follow the pace of the crisis and provided realistic estimations regarding the credit risk capital required. Finally, it is shown that the economic capital estimates derived by that model could have been used as an early warning indicator for the banking crisis (at least for the case of Greece) that may begin within the next couple of years, since there is a clear correlation between the model estimations and the values of well-established early warning indicators for banking crises. Keywords: Economic capital, Nonlinear programming, CreditRisk+, Sector correlation, JEL classification: G2, G32, C61, C44, C69