Guest Post from Chitro Majumdar
Economic capital models can be complex, embodying many component parts and it may not be immediately obvious that a complex model works satisfactorily. Moreover, a model may embody assumptions about relationships between variables or about their behaviour that may not hold in all circumstances (e.g under periods of stress). We have developed an algorithm for Dynamic Financial Analysis (DFA) that enables the creation of a comprehensive framework to manage Enterprise Risk’s Economic Risk Capital. DFA is used in the capital budgeting decision process of a company to launch a new invention and predict the impact of the strategic decision on the balance sheet in the horizon. DFA gives strategy for Enterprise Risk Management in order to avoid undesirable outcomes, which could be disastrous.
“The Quants know better than anyone how their models can fail. The surest way to replicate this adversity is to trust the models blindly while taking large-scale advantage of situations where they seem to provide ERM strategies that would yield results too superior to be true”
Dynamic Financial Analysis (DFA) is the most advance modelling process in today’s property and casualty industry-allowing us to develop financial forecasts that integrate the variability and interrelationships of critical factors affecting our results. Through the modeling of DFA, we see the company’s relevant random variables is based on the categorization of risks which is generated solvency testing where the financial position of the company is evaluated from the perspective of the customers. The central idea is to quantify in probabilistic terms whether the company will be able to meet its commitments in the future. DFA is in the capital budgeting decision process of a company launching a new invention and predicting the impact of the strategic decision on the balance sheet in a horizon of few years.
The validation of economic capital models is at a very preliminary stage. There exists a wide range of validation techniques, each of which provides corroboration for (or against) only some of the desirable properties of a model. Moreover, validation techniques are powerful in some areas such as risk sensitivity but not in other areas such as overall absolute accuracy or accuracy in the tail of the loss distribution. It is advisable that validation processes are designed alongside development of the models rather than chronologically following the model building process. There is a wide range of validation processes and each one provides evidence for only some of the desirable properties of a model. Certain industry validation practices are weak with improvements needed in benchmarking, industry wide exercises, back-testing, profit and loss analysis and stress testing and followed by other advanced simulation model. For validation we adhere to the below mentioned method to calculate.
Calculation of risk measures
In their internal use of risk measures, banks need to determine an appropriate confidence level for their economic capital models. It generally does not coincide with the 99.9% confidence level used for credit and operational risk under Pillar 1 of Basel II or with the 99% confidence level for general and specific market risk. Frequently, the link between a bank’s target rating and the choice of confidence level is interpreted as the amount of economic capital necessary to prevent the bank from eroding its capital buffer at a given confidence level. According to this view, which can be interpreted as a going concern view, capital planning is seen more as a dynamic exercise than a static one, in which banks want to hold a capital buffer “on top” of their regulatory capital and where it is the probability of eroding such a buffer (rather than all available capital) that is linked to the target rating. This would reflect the expectation (by analysts, rating agencies and the market) that the bank operates with capital that exceeds the regulatory minimum requirement. Apart from considerations about the link to a target rating, the choice of a confidence level might differ based on the question to be addressed. On the one hand, high confidence levels reflect the perspective of creditors, rating agencies and regulators in that they are used to determine the amount of capital required to minimise bankruptcy risk. On the other hand, use of lower confidence levels for management purposes in order to allocate capital to business lines and/or individual exposures and to identify those exposures that are critical for profit objectives in a normal business environment. Another interesting aspect of the internal use of different risk measures is that the choice of risk measure and confidence level heavily influences relative capital allocations to individual exposures or portfolios. In short, the farther out in the tail of a loss distribution, the more relative capital gets allocated to concentrated exposures. As such, the choice of the risk measure as well as the confidence level can have a strategic impact since some portfolios might look relatively better or worse under risk-adjusted performance measures than they would based on an alternative risk measure.
Chitro Majumdar CSO – R-square RiskLab
More details: http://www.riskreturncorp.com