Scenario-based RORC Optimisation for a Bank Loan Book

Note available here.

More information on the software used in this study here.

William Perraudin, Susan Wu, Fang Yao

Efficient management of banks, in the current regulatory environment, requires economical use of regulatory capital. To achieve this, banks should evaluate, for each business activity, the net expected return and the consumption of regulatory capital. This note presents a methodology for making such comparisons and for assessing how strategic changes in lending volumes devoted to different asset classes and markets affect the bank’s overall return-to-capital ratios.

Many banks use return on capital (often termed Risk Adjusted Return on Capital or RAROC) as the basis for hurdle rates in pricing loans. Some banks also use RAROC to analyse the performance of business units. Few institutions, however, use the approach in a systematic manner to optimise the bank’s exposure to different asset classes. Currently, for many banks, the constraint on lending is regulatory rather than economic capital. It is, therefore, interesting to analyse how different lending book strategies affect the Return on Regulatory Capital or “RORC”.

This note shows how such an analysis may be performed in a top-down manner contributing significantly to strategic decision making. We focus on the loan book of a bank although the approach could be extended to cover other business activities and risks. We show how to forecast net returns and regulatory capital for loan exposures broken down by asset class both in a base case projection and subject to shocks to the macroeconomic environment.

We apply our approach to the case of a particular bank, Barclays Group plc. The calculations presented are based entirely on publicly disclosed data. We chose Barclays as the example because of its importance as a major UK bank with a strong international presence. To construct the data for this case study, we use data on the bank’s loan book from its Pillar III disclosure and information from its annual report.

The projections are made for a base case and for an adverse scenario involving a recession in the UK. The framework we employ is capable of providing dynamic views of a bank’s loan book risk (provision and regulatory capital) based on user-defined criteria at different aggregation levels. In other words, the RORC calculation can distinguish the risk character of a bank’s loan book in a completely bespoke manner. For simplicity’s sake, here we limit ourselves to reporting results at an asset class level.

The calculations reported here are performed using Risk Control’s application for conditional analysis of risk, Stress ControllerTM. This application consists of web-based software in which much of the logic of a financial modelling exercise may be included through readily editable equations. The software supports the conditional forecasting of default rates in loan pools at different levels of aggregation and permits the user to generate macroeconomic scenarios and to link these through to shocks to loan ratings and probabilities of default.