A robust bank asset allocation model integrating credit-rating migration risk and capital adequacy ratio regulations

Dawen Yan (), Xiaohui Zhang () and Mingzheng Wang ()
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Dawen Yan: Dalian University of Technology
Xiaohui Zhang: University of Exeter
Mingzheng Wang: Zhejiang University

Annals of Operations Research, 2021, vol. 299, issue 1, No 28, 659-710

Abstract: Abstract In this paper, we consider a bank asset allocation problem with uncertain migration risk of credit ratings and capital adequacy ratio (CAR) regulations. In the practical scenarios, the future market values of each risky asset are largely affected by outer complex environments. We only observe the information about their first-moment and marginal second-moment of year-ahead market value of each loan asset. Based on these scenarios, we propose a new distributionally robust optimization model with the chance constraint characterized by uncertain CAR. Following the duality theory in infinite-dimensional optimization problem and the theory of conic linear optimization model, we can reformulate the original problem into a tractable linear deterministic semi-definite programming (SDP) model. By using this tractable linear SDP model, we can provide a robust asset allocation policy to bank managers. Further, we conduct a simulation study to illustrate the application of our method under two different economic conditions, a downward condition and an upward condition. Then a series of sensitivity tests is applied to examine the impacts of various factors, including safety probability, target CAR and recovery rate, on the optimal asset allocations. We also compare the performance of our model and the CVaR model. We demonstrate our model provides an efficient way to deal with the trade-off between expected return and CAR.

Keywords: Asset allocation; Credit rating migration; Capital adequacy ratio; Distributionally robust optimization; Chance constraint (search for similar items in EconPapers)
JEL-codes: C61 G21 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-020-03571-2

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