By Christian Meyer and Peter Quell
Senior administration are anticipated to make the most important enterprise judgements utilizing advanced chance types that, with no really good quantitative monetary wisdom, can result in unwell judged offerings. the hot arguable discussions about the use of danger types through the monetary main issue, and the hot regulatory framework, have highlighted the necessity for a constant method of solution the query What are hazard versions made for? and perhaps even extra importantly What are hazard types now not made for? . The file goals to give an explanation for: What danger version validation is; What possibility versions exist; How a hazard version can fail; Which elements of fact are incorporated, and which facets are excluded from a danger version; and How enterprise judgements may be according to a possibility versions output. In addressing those concerns, this file presents useful suggestion to the administration of monetary associations and a toolbox to elevate the major questions in terms of integrating the result of quantitative versions into company judgements.
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Extra info for Risk Model Validation
6â•‡ Risk measures for an exemplary P&L distribution Probability 7% Expectation value (average) 6% Value-at-risk (95% quantile) 5% Standard deviation Expected shortfall 4% (average over the tail) 3% Tail 2% (5% of mass ) 1% 0 Loss -80 -70 Profit -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 Note that there is an extensive (and still growing) mathematical theory of risk measures with ‘good’ properties, eg, coherence (Artzner et al 1999). Often it will also be required and useful to compute risk contributions, ie, to allocate the risk expressed via the risk measure to the single positions within the portfolio (see Tasche in Resti, 2008).
By evaluation of the portfolio considering the values of the risk factors and using risk-factor mapping, a scenario leads to a certain profit or loss. A huge number of scenarios can be evaluated this way. The collection of losses computed in the scenarios provides an approximation to the P&L distribution. At its heart, Monte Carlo simulation is a technique for numerical integration.
Multiplying probabilities of default with stress factors. In both cases, models having worked perfectly before were applied in situations they were not designed for. ’). The whole concept of Poisson approximation has been borrowed from insurance, where there is a much longer tradition of dealing with discrete events (deaths, car accidents, natural catastrophes) and their aggregation to a distribution of possible losses, ie, insurance claims. As with discretisation, approximation is not a bad thing in itself.
Risk Model Validation by Christian Meyer and Peter Quell