Operational Risk: Measurement and Modelling


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A How-to Guide to Quantifying Model Risk

This support by arbitrage-trading operations is what gives this quantity its power, as a measure of model risk.


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This model risk measure, the standard deviation of the cost of hedging, derives its power from the fact that it takes advantage of the properties of the models that are specific to option pricing, instead of treating the model as a generic unknown function. Use of these very problem-specific properties makes the test tighter and more data-efficient. Note also that this measure of model risk incorporates all of the modeling assumptions, including calibration choices, data choices, hedge instrument choices, etc.

We should pause to note that this method requires the generation of thousands of historical paths, similar to those used in historical VaR simulations. Generating many historical paths from the one true historical path we have, requires the path construction algorithm to make some form of a stationarity assumption. This means that the test must to be run with a variety of different assumptions in order to prove that our conclusions do not depend heavily on this assumption. A by-product of the computation of the cost of hedging distribution that we may easily compute is the daily hedge error distribution.

Extremely small probabilities found from this calculation suggest that the model may be losing its validity.

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Click to contact the Numerix Marketing Team and we'll put you in touch with the right Numerix expert or team member for the job. Contact Us. Home Blogs. Sep 9, Seeking a Definition of Model Risk Model Risk needs to be quantifiable to be useful, so we have to think about what kind of quantity we need Model Risk to be.

Model Risk Definition

Measuring Model Risk: Leveraging the Standard Deviation of the Cost of Hedging There is a better method with a rationale that can be defended through arbitrage. A Measure of Model Risk: Take a Closer Look This model risk measure, the standard deviation of the cost of hedging, derives its power from the fact that it takes advantage of the properties of the models that are specific to option pricing, instead of treating the model as a generic unknown function. Related Content:. Blog Post - Jan 18, Need Assistance?

Bibliographic Information

Mistakes, it is reasonable to suppose, happen at random: therefore they can be treated statistically. If we have operational records, and can identify mistakes, then determining the operational risk due to stupidity is essentially an exercise in data-mining. Formally, that is to say, operational risk from stupidity is very much like normal financial risk, though we need more real facts, and fewer stylized ones.

Malice is more interesting, and harder to deal with. Unlike mistakes and bull-headedness, corruption and deception are the stuff of which interesting stories are made, and sometimes truly spectacular sums of money are involved vide Barings, Enron. The problem, from our point of view, is that these cases belong more to the realm of game theory than that of statistics the strategic, interdependent actions of intelligent creatures, trying to do each other wrong.


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Perhaps there are statistical regularities to malicious operational risk there are, after all, statistical regularities to homicide but it is hard to get good data, for four reasons. First, successful malice, almost by definition, goes unnoticed until it's too late. Second, few institutions survive many really bad acts of malice, so the sample size is intrinsically small. Third, institutions are unlikely to boast of being swindled by their own employees or managers; many cases are thus unavailable to those seeking to augment their own databases.

An Integrated Approach

Fourth, there is every reason to think that malicious people help assess operational risk. The book under review is intended to be a practical guide to measuring, dealing with, and perhaps even reducing, operational risk. King's strategy basically has two parts.

Iconoclastic

King doesn't put it quite that way, but that's what it comes to. Small losses are supposed to have more or less assignable causes: operational losses on bank loans, for instance, happen because you assigned the customer the wrong credit score, or because collateral isn't worth what you thought it was, etc. We shall return to them presently. The delta method, it turns out, is just error propagation, familiar from introductory lab courses in physics, chemistry, etc.

Operational Risk

Assume that the errors in your measurements have Gaussian distributions, and that they are small enough that errors in derived quantities are linear functions of measurement errors i. In doing so, this research employs Cornish-Fisher expansion upon which the confidence interval of operational variables is an explicit function of the skewness and kurtosis as well as the volatility. However, people risk is enormous and plays a dominant role in affecting the level of operational risk exposures in Islamic banks in investment activities.

gribtadonse.tk Social bookmarking:. A-Z Index Accessibility. You are in: Home Research Durham e-Theses. Depositor Login Administrator Login. Abstract With the emergence and development of Islamic banking industry, the need to cater operational risks issues has attracted the attention of academics in recent years. Quick links Latest additions Search Browse by year Browse by department.

Good practice guide to setting inputs for operational risk models. 21 March 2016
Operational Risk: Measurement and Modelling Operational Risk: Measurement and Modelling
Operational Risk: Measurement and Modelling Operational Risk: Measurement and Modelling
Operational Risk: Measurement and Modelling Operational Risk: Measurement and Modelling
Operational Risk: Measurement and Modelling Operational Risk: Measurement and Modelling
Operational Risk: Measurement and Modelling Operational Risk: Measurement and Modelling
Operational Risk: Measurement and Modelling Operational Risk: Measurement and Modelling

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