An optimal portfolio and capital management strategy for basel III compliant commercial banks

We model a Basel III compliant commercial bank that operates in a financial market consisting of a treasury security, a marketable security, and a loan and we regard the interest rate in the market as being stochastic. We find the investment strategy that maximizes an expected utility of the bank’s asset portfolio at a future date. This entails obtaining formulas for the optimal amounts of bank capital invested in different assets. Based on the optimal investment strategy, we derive a model for the Capital Adequacy Ratio (CAR), which the Basel Committee on Banking Supervision (BCBS) introduced as a measure against banks’ susceptibility to failure. Furthermore, we consider the optimal investment strategy subject to a constant CAR at the minimum prescribed level. We derive a formula for the bank’s asset portfolio at constant (minimum) CAR value and present numerical simulations on different scenarios. Under the optimal investment strategy, the CAR is above the minimum prescribed level. The value of the asset portfolio is improved if the CAR is at its (constant) minimum value

Optimal asset allocation and capital adequacy management strategies for Basel III compliant banks

Philosophiae Doctor - PhDIn this thesis we study a range of related commercial banking problems in discrete and continuous time settings. The first problem is about a capital allocation strategy that optimizes the expected future value of a commercial bank’s total non-risk-weighted assets (TNRWAs) in terms of terminal time utility maximization. This entails finding optimal amounts of Total capital for investment in different bank assets. Based on the optimal capital allocation strategy derived for the first problem, we derive stochastic models for respectively the bank’s capital adequacy and liquidity ratios in the second and third problems. The Basel Committee on Banking Supervision (BCBS) introduced these ratios in an attempt to improve the regulation of the international banking industry in terms of capital adequacy and liquidity management. As a fourth problem we derive a multi-period deposit insurance pricing model which incorporates the optimal capital allocation strategy, the BCBS’ latest capital standard, capital forbearance and moral hazard. In the fifth and final problem we show how the values of LIBOR-in-arrears and vanilla interest rate swaps, typically used by commercial banks and other financial institutions to reduce risk, can be derived under a specialized version of the affine interest rate model originally considered by the bank in question. More specifically, in the first problem we assume that the bank invests its Total capital in a stochastic interest rate financial market consisting of three assets, viz., a treasury security, a marketable security and a loan. We assume that the interest rate in the market is described by an affine model, and that the value of the loan follows a jump-diffusion process. We wish to find the optimal capital allocation strategy that maximizes an expected logarithmic utility of the bank’s TNRWAs at a future date. Generally, analytical solutions to stochastic optimal control problems in the jump setting are very difficult to obtain. We propose an approximation method that exploits a similarity between the forms of the control problems of the jump-diffusion model and the diffusion model obtained by removing the jump. With the jump assumed sufficiently small, the analytical solution of the diffusion model then serves as a proxy to the solution of the control problem with the jump. In the second problem we construct models for the bank’s capital adequacy ratios in terms of the proxy. We present numerical simulations to characterize the behaviour of the capital adequacy ratios. Furthermore, in this chapter, we consider the approximate optimal capital allocation strategy subject to a constant Leverage Ratio, which is a specific non-risk-based capital adequacy ratio, at the minimum prescribed level. We derive a formula for the bank’s TNRWAs at constant (minimum) Leverage Ratio value and present numerical simulations based on the modified TNRWAs formula. In the third problem we model the bank’s liquidity ratios and we monitor the levels of the liquidity ratios under the proxy numerically. In the fourth problem we derive a multi-period deposit insurance pricing model, the latest capital standard a la Basel III, capital forbearance and moral hazard behaviour. The deposit insurance pricing method utilizes an asset value reset rule comparable to the typical practice of insolvency resolution by insuring agencies. We perform numerical computations with our model to study its implications. In the final problem, we specialize the affine interest rate model considered previously to the Cox-Ingersoll-Ross (CIR) interest rate dynamic. We consider fixed-for-floating interest rate swaps under the CIR model. We show how analytical expressions for the values of both a LIBOR-in-arrears swap and a vanilla swap can be derived using a Green’s function approach. We employ Monte Carlo simulation methods to compute the values of the swaps for different scenarios. We wish to make explicit the contributions of this project to the literature. A research article titled “An Optimal Portfolio and Capital Management Strategy for Basel III Compliant Commercial Banks” by Grant E. Muller and Peter J. Witbooi [1] has been published in an accredited scientific journal. In the aforementioned paper we solve an optimal capital allocation problem for diffusion banking models. We propose using the solution of the Brownian motions control problem of [1] as the proxy in problems two to four of this thesis. Furthermore, we wish to note that the methodology employed on the final problem of this study is actually from the paper [2] of Mallier and Alobaidi. In the paper [2] the authors did not present simulation studies to characterize their pricing models. We contribute a simulation study in which the values of the swaps are computed via Monte Carlo simulation methods

Predicting Bank Failure Using Regulatory Accounting Data

A liquidity shortfall in the United States triggered the bankruptcy of several large commercial banks, and bank failures continue to occur, with 50 banks failing between 2013 and 2015. Therefore, it is critical banking regulators understand the correlates of financial performance measures and the potential for banks to fail. In this study, binary logistic regression was employed to assess the theoretical proposition that banks with higher nonperforming loans, lower Tier 1 leverage capital, and higher noncore funding dependence are more likely to fail. Archival data ranging from 2012-2015 were collected from 250 commercial banks listed on the Federal Deposit Insurance Corporation\u27s website. The results of the logistic regression analyses indicated the model was able to predict bank failure, X2(3, N = 250) = 218.86, p \u3c .001. Nonperforming loans, Tier 1 leverage capital, and noncore funding were all statistically significant, with Tier 1 leverage capital (β = -1.485), p \u3c .001) accounting for a higher contribution to the model than nonperforming loans (β = .354, p \u3c .001) and noncore funding dependence (β = -.057, p = .015). The implication for positive social change of this study includes the potential for bank regulators to enhance job security, wealth creation, and lending within the community by working with bank managers to develop more timely corrective action plans to alleviate the risk of bank failure

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

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordIn 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.NSFCChina Postdoctoral Science FoundationZhejiang UniversityFundamental Research Funds for the Central Universitie