Measuring the Operational Component of Catastrophic Risk: Computational Framework. Paper no.48. Abstract

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1 Measuring the Operational Component of Catastrophic Risk: Computational Framework Paper no.48 Abstract Quantifying operational risk has important policy implications as Basel II introduces a new regulatory capital charge for banks internal modelling of operational losses. This research incorporates applying computational intelligence techniques onto operational risk measurement approaches to enhance estimation models and develop simulation tools. Increasing processing power of computer systems combining with the enormous amount of data becoming available in finance brings about good opportunities to carry out further research works. The software will be written in Matlab and will be based on object-oriented design concept. Story-based implementation development model as in Agile approach will be utilized. What is the research? Basel II defines operational risk as the risk of loss resulting from inadequate or failed internal process, people and systems or from external events [1]. Although this definition is generally accepted by banking industry, certain risk analysts believe the definition to be flawed. Therefore there is still no standardized definition of operational risk. However, with the introduction of Basel II, all definitions converge around the one suggested by the new accord [2]. Another important implementation of Basel II is that it requires banks to collect data on operational losses. This mandatory data collection progress allows risk analysts to scrutinize the behaviour of operational losses systematically. The accord also has given guidance to three broad

2 approaches of measurement of operational risk. The ability to quantify operational risk has important policy implications as Basel II introduces a new regulatory capital charge for banks internal modelling of operational losses. This leads the interest in quantifying operational risks to grow considerably. The two income-based approaches assume that there is a stable linear relationship between the income generated by a business or business line and the exposure to operational losses. Unfortunately these assumptions do not conform well to reality of financial markets. However, many financial institutions express preference towards the income statement based measures since the requirements of advanced measurement approaches are deemed too demanding and requiring for longer term. Irrespective of choice to quantify the capital charge for operational risk, Basel II undoubtedly requires an operational risk management function that corresponds to the degree of business sophistication. In this research, it is intended to build on Allen and Bali [2] who study stock returns in order to isolate extreme and operational risk premium and investigate its co-movement with macroeconomic variables. Inferring catastrophic risk from the cross-sectional distribution of monthly stock returns is also in the scope of this study. The contribution of this study is composed of two parts; first enhancing model specification and estimation procedure, and second providing a powerful tool to simulate certain scenarios. The context of the Research This study is more focused on computational framework of measuring operational component of catastrophic risk. The research incorporates applying computational intelligence techniques onto operational risk measurement approaches to enhance estimation models and develop simulation tools.

3 The principal goal of computational intelligence [3] is to understand the principles that make intelligent behaviour possible in natural or artificial systems whereas the engineering of Computational Intelligence is designing and implementing systems to solve certain problems. Therefore the discipline of Computational Intelligence does not only enable scientists to observe the external behaviour of intelligent systems, but also gives opportunities to scientists to experiment by designing executable models with the assistance of more powerful means which was not available previously. Computational intelligence (CI) has been progressively gaining presence for the last two decades [4]. CI was initially utilized in physical engineering with the aim of integrating the flexibility of judgement and response into computational applications that handles huge amount of data and has ability to learn and adapt. Following its success in physical engineering in pattern recognition and nonlinear forecasting, CI drew attention of economists and CI experiments in financial applications began. Established Computational Intelligence methods include fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning. Furthermore new methods mimicking nature in problem solving are being continuously introduced as well as hybrid approaches being constructed. Increasing processing power of computer systems combining with the enormous amount of data becoming available in finance brings about good opportunities to carry out further research works. The interest in this research domain is considerably growing as can be seen from availability of the increasing amount of books and articles published [5]. Compared to the past, there is less need to simplify assumptions

4 and the share of data-driven modelling is more promoted. As a result of these novel developments, researchers currently are capable of obtaining, processing and analyzing high frequency financial data, price complex financial products or analyze market behaviour using powerful computational resources. In this context, computational intelligence techniques propose a solid framework which is well suited for certain tasks in financial applications. Upon applying computational intelligence methods to finance and economics, there are certain challenges that need to be addressed. Many of the problems in this domain stem from nonlinearities [4]. Since there is no standardized and optimized method to develop models for non linear problems, a model for a particular of problem is considered as ad-hoc solution and is inferred from heuristic approach. Another related issue is the assumption in many finance and economics problems that there are important nonlinearities both between the independent variables (observables) and the dependent variables. Another concern in this field is arising around datasets. In order to generate better results, more accurate dataset should be utilized. In other sense, the corrupted and missing data will result in producing flawed outcomes. By combining domain knowledge with empirical data, computational intelligence techniques allow us to develop powerful computing tools to solve complex, multiobjective problems, that are tough enough to be solved using traditional methods. Determining behavioural change in financial time-series data stands as another issue. Studies around this issue suggest that the next status of an individual does not only depend on the current status but also its status changes over time.

5 There are also certain issues with relate to software engineering with computational intelligence. Handling these challenges appropriately is a crucial part of this study. Fuzziness resided in the requirements for software application should be managed in the early stage of research [6]. Imprecise knowledge and fuzzy objects should be handled well. Otherwise this will surely affect the productivity of the software application. Lastly, a software quality model that predicts a statistical measure of reliability for each module enables researchers to find out most fault-prone modules early in the software life cycle. How will the study be undertaken-methodology? This study will estimate the operational losses for each institution in the collected dataset and then use the panel to estimate the loss distribution for financial intermediaries. Thus, by pooling together the loss experience of financial institutions data availability issue will be addressed to some extent. It also brings about new evidences on the applicability of extreme value theory. And conclusion will answer the question of relationship between stock market data and characteristics of operational loss distribution. Within computational framework, a database that presumably stores a couple of million data records will be designed taking optimized data handling mechanism issue into account. The software required to design and implement as a part of this project will be written in Matlab. The application will be based on object-oriented design concept. However, as efficiency in linear algebra and matrix operations is of the core strength of the Matlab, design approach would not be purely object-oriented.

6 Software development model will be similar to Agile development approach. Storybased implementation will be utilized. At the end of each story, a bug-free and working application will be achieved. The crucial benefit of this approach will be that the application will allow any change at any time.

7 References [1] Basel Committee for Banking Supervision, International Convergence of Capital Measurement and Capital Standards: A Revised Framework, [2] A. Scurr, S. Bozhkov, A. Serguieva, K. Yu, O. Dolutas: Quantifying operational risk of financial institutions, International Federation of Operational Research Societies Conference, South Africa, 2008 [3] L. Allen, T. Bali: Cyclicality in catastrophic and operational risk measurements, Journal of Banking and Finance, vol. 31, pp , [4] Poole D, Mackworth A, Goebel R: Computational Intelligence: A Logical Approach. Oxford University Press: New York; 2004 [5] Chen S H, Wang P P: Computational Intelligence in Economics and Finance. Springer: Germany; 2004 [6] Isasi P, Quintana D, Saez Y: Applied Computational Intelligence for Finance and Economics. Computational Intelligence Vol: 23 No: 2; 2007 [7] Lee J: Software Engineering with Computational Intelligence. Springer: Germany; 2003