Managing Credit Risk in Bank Loan Portfolio

Managing Credit Risk in Bank Loan Portfolio

DOI: 10.4018/978-1-5225-7280-0.ch002

Abstract

In this chapter, the Six Sigma DMAIC approach is applied to improve credit risk management in banking loan portfolio selection. The objective is to select the optimal loan portfolio which achieves the bank's investment objectives with an acceptable credit risk according to their predefined limits. Stochastic optimisation constructs an efficient frontier of optimal loan portfolios in banking with maximal profit and minimising loan losses, i.e. credit risk. Simulation stochastically calculates and measures mean gross profit, loan losses, variance, standard deviation and the Sharpe ratio. The Six Sigma capability metrics determines if the loan portfolio complies with the bank's limits regarding the gross profit; loan losses, which quantifies the credit risk; and Sharpe ratio, i.e. a risk adjusted measure. Also, the bank regulation limits are applied based on the bank's capital to control the maximum loan amount per loan investment grade. Analysis allows for selection of the best Efficient Frontier loan portfolio with the maximum Sharpe ratio.
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Introduction

This chapter specifically focuses on credit risk associated with the loan portfolio of a bank. Credit risk is the risk of losses due to borrowers’ default or deterioration of credit standing. Default is the event that borrowers fail to comply with their debt obligations. Default triggers a total or partial loss of the amount lent to the counterparty.

Wilson (1998) discussed how financial institutions are increasingly measuring and managing the risk from credit exposures at the portfolio level, in addition to the transaction level. This change in perspective has occurred for a number of reasons. First is the recognition that the traditional binary classification of credits into “good” credits and “bad” credits is not sufficient--a precondition for managing credit risk at the portfolio level is the recognition that all credits can potentially become “bad” over time given a particular economic scenario. The second reason is the declining profitability of traditional credit products, implying little room for error in terms of the selection and pricing of individual transactions, or for portfolio decisions, where diversification and timing effects increasingly mean the difference between profit and loss. Finally, management has more opportunities to manage exposure proactively after it has been originated, with the increased liquidity in the secondary loan market, the increased importance of syndicated lending, the availability of credit derivatives and third-party guarantees, and so on.

In order to take advantage of credit portfolio management opportunities, management must first answer several technical questions: What is the risk of a given portfolio? How do different macroeconomic scenarios, at both the regional and the industry sector level, affect the portfolio's risk profile? What is the effect of changing the portfolio mix? How might risk-based pricing at the individual contract and the portfolio level be influenced by the level of expected losses and credit risk capital? This paper describes a new and intuitive method for answering these technical questions by tabulating the exact loss distribution arising from correlated credit events for any arbitrary portfolio of counterparty exposures, down to the individual contract level, with the losses measured on a marked-to-market basis that explicitly recognises the potential impact of defaults and credit migrations.

Lending is the principal business activity for most commercial banks. The loan portfolio is typically the largest asset and the predominant source of revenue. As such, it is one of greets sources of risk to a banks safety and soundness. Whether due to lax credit standards, poor portfolio risk management, or weakness in the economy, loan portfolio problems have historically been the major cause of bank losses and failures (Comptroller of the Currency Administrato, 2015).

Credit Portfolio Management (Hünseler 2013) is a topical text on approaches to the active management of credit risks. The book is a valuable, up to date guide for portfolio management practitioners. Its content comprises of three main parts: The framework for managing credit risks, Active Credit Portfolio Management in practice and Hedging techniques and toolkits.

A paper by Crouhya, Galaib & Marka (2000) reviewed the proposed industry sponsored Credit Value-at-Risk methodologies. The credit migration approach, as proposed by JP Morgan with CreditMetrics, is based on the probability of moving from one credit quality to another, including default, within a given time horizon.

The option pricing or structural approach is based on the asset value model originally proposed by Merton (Merton 1974). In this model the default process is endogenous and relates to the capital structure of the firm. Default occurs when the value of the firm’s assets falls below some critical level. Third, the actuarial approach as proposed by Credit Suisse Financial Products (CSFP) with CreditRisk+ and which only focuses on default. Default for individual bonds or loans is assumed to follow an exogenous Poisson process. Finally, McKinsey Research proposes CreditPortfolioView which is a discrete time multi-period model where default probabilities are conditional on the macro-variables like unemployment, the level of interest rates, the growth rate in the economy, which to a large extent drive the credit cycle in the economy.

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