Objectivity and the Measurement of Operational Risk. Dr. Lasse B. Andersen
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1 Objectivity and the Measurement of Operational Risk Dr. Lasse B. Andersen
2 Background - The OpRisk Project Societal Safety & Risk Mng. Research group: 18 professors, 15 assoc. professors, 25 Ph.D students, MSc stud. Acknowledged as one of the world wide leading research groups on operational risk management Create a forum for innovation, experience transfer and learning regarding management of operational risk
3 OpRisk Research Agenda Contribute to an improved framework for OpRisk Management in the Finance Industry Establish quantitative models under the AMA framework that supports day to day OpRisk Management Study the impact of Organisational Culture on Operational Risk Contribute to optimizing organisational learning through proper systems for loss event reporting Develop methodology for pro-active loss event identification in banks business processes
4 Features of a useful AMA Model Generates a quantitative measure of risk exposure in terms of economic capital Handles correlations and dependencies between loss events and business processes Provides a structured framework for combining information from several sources (data, expert knowledge etc.) Reflects risk reducing measures and enable cost benefit Knowledge based rather than data driven studies Visualises an individual bank specific risk picture incl. all causal factors as a basis for sound discussions on risk and risk exposure Forms a sound basis for day to day management of operational risk
5 AMA Modelling Challenges Modelling Approach Probability theory Respond to changes in business environment and control structure (both risk mitigation and quality improvement efforts) Modelling of correlations and dependencies Quantitative modelling of soft causal mechanisms, e.g., ethics, values, policies, competence, training etc Detailed modelling of causal mechanisms and control structures related to a large number of business areas and products Lack of relevant hard data Combine expert judgment and hard data Objective risk figures are preferred (and expected) by decision makers
6 Knowledge based modeling approach using Bayesian Networks
7 All models are wrong, but some are useful (G.E.P Box) The knowledge based BN model is useful because: It visualizes a bank specific risk picture that forms the basis for constructive discussions about risk It creates awareness and forms a sound basis for decision making It quantifies economic capital and forms a sound basis for day to day operational risk management The model output should be interpreted as: an expression of the involved experts uncertainty related to whether future loss events will occur or not However, most decision makers would prefer the results to be interpreted as: a revelation of the objective risk related to future losses, where the uncertainty in the results is insignificant
8 Objectivity, what is it? Based on facts that can be proved Undistorted by emotion, interpretation, or personal bias Objectivity is thus associated with correctness and truth
9 Objectivity Law (Can Empirical Legal Enquiry be Objective? Robert J Lipkin, USA 2008) Inquiry X is more objective than Y, if X requires fewer conceptual and methodological choices than Y assuming that more choices will include more contestable assumptions some level of contestable assumptions exists, below which we can say the enquire is objective Enquiry X is objective if it s contestable assumptions is fewer than the designated level Objective risk figures must be anchored in objective probabilities
10 Objective risk figures are presented as a real alternative Named "one of the 100 most influential people in finance" by Treasury & Risk magazine in 2006 The COSO method is highly subjective overly simplistic and conceptually flawed major discrepancy between perception and reality. Real risks can only be identified by studying historical loss data In the nineties, banks began collecting historical loss data and we entered the dawn of a new age. Analysis of these data lead to the development of modern operational risk management as an objective discipline
11 Why are we seeking objective risk figures? Education system failure Many professionals in the industry still believe in the existence of objective risk figures When presented as an alternative of course decision makers would prefer objective risk figures
12 The search for objective risk figures create significant problems: Creates a false sense of security, and decision makers may feel too comfortable Renders risk professionals incapable of taking action Uncertainty in risk figures is impossible to quantify Wrong basis for fruitful discussions about risk Important knowledge is lost (or excluded) in the risk analysis process Useful knowledge on causal mechanisms and influencing factors is substituted by acrobatic mathematical exercises on too few data points
13 The problem of inductive knowledge how do we know what we know? Bertrand Russell, variable catastrophy Best in the world on risk mng Risk Magazine AMA approval time What we learn from the past may turn out to be at best irrelevant or false, or at worst viciously misleading
14 A risk model should incorporate all available knowledge Observations/ hard data Relevant knowledge Relevant experience Systematic analysis of risk Potential loss events with corresponding uncertainties
15 True values and probabilities We can measure the average hight height of of the swedish Swedish population Statistical theory provides an estimate on the basis of measuring approx people. The measurement error can be expressed by standard deviation and confidence intervals Jacob Bernoulli ( ) wanted to find the true probabilities related to future events. He was only partially successful True probabilities exists in gambling since there are no dependency between trials
16 The history of probability 1450: Luca Paciolli presents The Puzzle : A and B play balla and the game goes on until one of the players have won 6 times the game is interrupted when A has won 5 and B has won 3 times how should the stakes be divided? 1654: Chevalier hired Pascal and Fermat to solve the puzzle : Bernoulli establish the law of large numbers Nineteen century: the application of probability theory increases within physics, biology, social science and economics Alternative theories of probabilities emerges 1932: Bruno de Finitti presents a subjective probability theory, and predicts we are all Bayesians by year : Bruno de Finitti publishes "The Theory of Probability
17 Acknowledged theories of probability The Frequency theory Defines the probability of an outcome as the limiting frequency with which that outcome appears in a long series of similar events. Classical Relative freq. The Propensity theory Probability is a propensity inherent in a set of repeatable conditions The Logical theory Identifies probability with degree of rational belief. Assuming that given the same evidence, Subjective all rational / human beings will entertain the same degree of belief in a prediction Predictive Bayesian The Subjective theory Identifies probability with the degree of belief of a particular individual. Note that all probability theories apply the same sets of axioms and theorems
18 The pluralist view Classical probability theory is anchored in the law of large numbers We assume the existence of true non-observable To be applied when we probabilities have full knowledge The result must be interpreted as our best of the estimate phenomenon of the true probability of the outcome of a future e.g. gambling, event Uncertainty is defined as the gap between laboratory our estimate or very and the true value large populations Predictive Bayesian/Subjective probability theory No true values exist only degree of belief The result must be interpreted as To our be uncertainty applied when we related do to the outcome of a future event not have full knowledge Uncertainty the outcome of future events of the phenomenon is uncertain e.g. operational risk
19 Objective risk figures do not exist, and the search for them derails the risk analysis process! There is no acknowledged scientific theory that supports objective risk figures in an OpRisk context Risk Management deals with uncertainties related to future loss events and should be based on all available knowledge
20 Objective risk results are not an option 0, , , , , , , , , , Loss severity 0,06 Event frequency 0,05 0,04 0,03 0,02 0,01 0 Subjective risk results Subjective risk results N~(μ*,σ*) μ* =25000 σ* =9105 0,12 0,1 0,08 0,06 0,04 Po~(λ*) λ* =50 0,
21 Summary Risk emerges as a result of systematic analysis, carried out by someone, on the basis of certain knowledge Objective risk figures can not be scientifically justified Naive positivists (Schrader-Frechette 1991) We should stop searching for objective risk figures and start incorporating all available knowledge in our models for measuring operational risk.
22 Thank you for your attention
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