Efficiency, Profitability and Quality of Banking Services

This paper develops a general framework for combining strategic benchmarking with efficiency benchmarking of the services offered by bank branches. In particular, the service-profit chain is cast as a cascade of efficiency benchmarking models. Three models-based on Data Envelopment Analysis (DEA)-are developed in order to implement the framework in the practical setting of a bank's branches: an operational efficiency mode, a quality efficiency model and a profitability efficiency model. The use of the models is illustrated using data for the branches of a commercial Bank. Empirical results indicate that superior insights can be obtained by analyzing operations, service quality, and profitability simultaneously than the information obtained from benchmarking studies of these three dimensions separately. Some relations between operational efficiency and profitability, and between operational efficiency and service quality are investigated. This paper was presented at the Financial Institutions Center's conference on Performance of Financial Institutions, May 8-10, 1997.

Dynamic asset (and liability) management under market and credit risk

We introduce a modelling paradigm which integrates credit risk and market risk in describing the random dynamical behaviour of the underlying fixed income assets. We then consider an asset and liability management (ALM) problem and develop a mul- tistage stochastic programming model which focuses on optimum risk decisions. These models exploit the dynamical multiperiod structure of credit risk and provide insight into the corrective recourse decisions whereby issues such as the timing risk of default is appropriately taken into consideration. We also present a index tracking model in which risk is measured (and optimised) by the CVaR of the tracking portfolio in relation to the index. Both in- and out-of-sample (backtesting) experiments are undertaken to validate our approach. In this way we are able to demonstrate the feasibility and flexibility of the chosen framework

Extending Credit Risk (Pricing) Models for the Simulation of Portfolios of Interest Rate and Credit Risk Sensitive Securities

We discuss extensions of intensity based models for pricing credit risk and derivative securities to the simulation and valuation of portfolios. The stochasticity in interest rates, credit spreads (default intensities) and rating migrations are incorporated in a unified framework. Scenarios of future prices of all securities are calculated in a risk-neutral world. The calculated prices are consistent with observed prices and the term structure of default free and defaultable interest rates. Three applications are discussed: (i) study of the inter-temporal price sensitivity of credit bonds to changes in interest rates, default probabilities, recovery rates and rating migration, (ii) portfolio simulations with attribution of changes to credit events and interest rates and, (iii) tracking of corporate bond indices. Key words: credit risk, default risk, simulation, integrated product management

The Tail that Wags the Dog: Integrating Credit Risk in Asset Portfolios

Tails are of paramount importance in shaping the risk profile of portfolios with credit risk sensitive securities. In this context risk management tools require simulations that accurately capture the tails, and optimization models that limit tail effects. Ignoring the tails in the simulation or using inadequate optimization metrics can have significant effects and destroy portfolio efficiency. The resulting portfolio risk profile can be grossly misrepresented when long run performance is optimized without consideration of the short term tail effects. This paper illustrates the pitfalls and suggests models for avoiding them.

What Drives the Performance of Financial Institutions?

The financial sector is one of the most, if not the most significant economic sector in modern societies. In advanced countries, it employs more people than major manufacturing industries combined and accounts for a high percentage of the Gross Domestic Product. But the financial services sector also plays a large indirect role in national economies. The financial sector mobilizes savings and allocates credit across space and time, and enables firms and households to cope with uncertainties by hedging, pooling, sharing and pricing risks. This ultimately improves the quantity and quality of real investments and increases income per capita and raises standards of living. Today financial institutions are experiencing unprecedented change in a competitive global environment. Economies of scale that lead to more integrated automation lead to further economies of scope. Technological innovation adds competitive pressures, and provides opportunities for new non-traditional providers. Consumers themselves may be the strongest force of change in the financial services industry since their needs are evolving quickly. Banking institutions are being transformed from financial intermediaries to retail service providers - they now formulate products for clients or intermediaries, sell and service a range of products to customers and also provide the support functions needed for the successful execution of its primary activities. The authors ask how they can measure performance of a financial institution in this changing landscape? Traditional productivity measures are difficult to compute and tell us less than they used to. The authors also seek to identify what drives performance and identify three dimensions of performance drivers to explore: strategy; execution of strategy; and the environment. To begin, the authors define performance to mean economic performance as measured by financial indicators. Quality of services and effective risk management help explain strong performance levels. Strategic decisions that affect an institution's success involve product mix, client mix, geographical location, and distribution channels. In terms of strategy execution, the authors cite x-efficiency, human resource management, use of technology, process design, and the successful alignment of all these components as important factors in successful firm performance. Creating the right technology environment also has a significant impact on performance. The remainder of this paper summarizes the fifteen chapters of a book edited by the authors on performance and its drivers. The book is divided into three section: General; Drivers of Performance; and Environmental Drivers of Performance. There are also two chapters at the end that discuss performance and risk management. The authors then briefly discuss possible future research directions, including the relationship between operational efficiency and quality of services, the balance between risk management and retail services in bank activities, and study of interorganizational issues.

Integrated risk/cost planning models for the US Air Traffic system

A prototype network planning model for the U.S. Air Traffic control system is described. The model encompasses the dual objectives of managing collision risks and transportation costs where traffic flows can be related to these objectives. The underlying structure is a network graph with nonseparable convex costs; the model is solved efficiently by capitalizing on its intrinsic characteristics. Two specialized algorithms for solving the resulting problems are described: (1) truncated Newton, and (2) simplicial decomposition. The feasibility of the approach is demonstrated using data collected from a control center in the Midwest. Computational results with different computer systems are presented, including a vector supercomputer (CRAY-XMP). The risk/cost model has two primary uses: (1) as a strategic planning tool using aggregate flight information, and (2) as an integrated operational system for forecasting congestion and monitoring (controlling) flow throughout the U.S. In the latter case, access to a supercomputer is required due to the model's enormous size

EU borders: walking backwards from Northern Ireland to Cyprus

The Good Friday agreement put to rest age-old conflicts on Ireland. It also offered hope that the reunification of Cyprus might be possible within the European Union. But lately, as Stavros Zenios writes, the “Green Line” that divides the easternmost island of the EU is viewed as a template for a soft border at the westernmost island of the Union after Brexit

Scenario Modeling for the Management of International Bond Portfolios

We address the problem of portfolio management in the international bond markets. Interest rate risk in the local market, exchange rate volatility across markets, and decisions for hedging currency risk are integral parts of this problem. The paper develops a stochastic programming optimization model for integrating these decisions in a common framework. Monte Carlo simulation procedures, calibrated using historical observations of volatility and correlation data, generate jointly scenarios of interest and exchange rates. The decision maker's risk tolerance is incorporated through a utility function, and additional views on market outlook can also be incorporated in the form of user specified scenarios. The model prescribes optimal asset allocation among the different markets and determines bond-picking decisions and appropriate hedging ratios. Therefore several interrelated decisions are cast in a common framework, while in the past these issues were addressed separately. Empirical results illustrate the efficacy of the simulation models in capturing the uncertainties of the Salomon Brothers international bond market index.

There are few good solutions from a divided Cyprus for Northern Ireland

The Good Friday agreement put to rest age-old conflicts in Ireland. It also offered hope that the reunification of Cyprus might be possible within the European Union. But lately, as Stavros Zenios writes, the "Green Line" that divides the easternmost island of the EU is viewed as a template for a soft border at the westernmost island of the Union after Brexit. ..

Risk Factor Analysis and Portfolio Immunization in the Corporate Bond Market

In this paper we develop a multi-factor model for the yields of corporate bonds. The model allows the analysis of factors which influence the changes in the term structure of corporate bonds. More than 98% of the variability in the corporate bond market is captured by the model, which is then used to develop credit risk immunization strategies. Empirical results are given for the U.S. market using data for the period 1992-1999.