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Riskiness Leverage Models. Riskiness Leverage Models. AKA RMK algorithm Capital can be allocated to any level of detail in a completely additive fashion. Riskiness only needs to be defined on the total, and can be done so intuitively. Many functional forms of risk aversion are possible.
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Riskiness Leverage Models • AKA RMK algorithm • Capital can be allocated to any level of detail in a completely additive fashion. • Riskiness only needs to be defined on the total, and can be done so intuitively. • Many functional forms of risk aversion are possible. • All the usual forms can be expressed, allowing comparisons on a common basis. • Simple to do in simulation situation.
Riskiness Leverage Models • Start with N random variables (think underwriting and other cash flows) and their total • Riskiness be expressed as the mean value of a linear functional in the total times an arbitrary function depending only on the total. • Simple example: functional is variable minus constant times the mean of the variable.
Riskiness Leverage Models • The allocation of the riskiness to an individual variable is • Surplus, risk load or whatever can be allocated proportionally and everything will add no matter what the dependency structure. • These are referred to a co-measures, in analogy with the simple examples of covariance, co-skewness, and so on. • Covariance and higher powers have a = 1 and
TVAR as Riskiness Leverage • TVAR has (1) functional = variable and (2) leverage zero below some value corresponding to a percentile , and constant above it: • This can be re-framed as • and individual riskiness as
EPD as Riskiness Leverage • Expected Policyholder Deficit has (1) functional = variable - some value and (2) leverage zero below the value and 1 above it: • This is • and individual riskiness as
L x L x L m x Riskiness Leverage Examples VaR: TVaR: Semi-variance:
Generic Riskiness Leveragefor management should • be a down side measure (the accountant’s point of view); • be more or less constant for excess that is small compared to capital (risk of not making plan, but also not a disaster); • become much larger for excess significantly impacting capital; and • go to zero (or at least not increase) for excess significantly exceeding capital – once you are buried it doesn’t matter how much dirt is on top.
How to choose measures? • Try out various measures on simulation to see how different they are. • Try out various measures on past history to see what would have guided you well. • Try out various measures on different levels of management to see what kind of buy-in you can get. • Run candidates in parallel with current processes for a while to see what they suggest.
A miniature companyportfolio example • ABC Mini-DFA.xls is a spreadsheet representation of a company with two lines of business. • How do we as company management look at the business? • “For the x percent of possibilities of net income that are less than $BAD we want the surplus to be a prudent multiple of the average value so that we can go on in business.” • Looking at the numbers quantifies x% as 2% and prudent as 1.5. • Two lessons from the model: • Returns on allocated surplus can be VERY misleading and need careful interpretation. • that we do not need to know what the reinsurer’s rate of return is on a contract to know how good or bad it is for us.