PRIIPs Market Risk Measures and the Limits of Reverse Engineering

Summary

This discussion explains that a PRIIP’s market risk measure (MRM) is determined through its volatility equivalent (VEV), which in turn depends on a value-at-risk calculation using standard deviation, skewness, excess kurtosis, and the recommended holding period. The question is whether changing those inputs could reduce an MRM from 5 to 2, and whether Excel Solver or invented statistics could achieve the desired result.

The replies say that the calculation uses at least two years of historical prices for daily funds, including dividends where applicable. Reverse engineering the formula can produce simulated prices, for example using Black–Scholes, but those prices would not represent the fund’s or share class’s actual time series. One suggestion is to examine less volatile periods or a shorter history, while the response also warns that choosing data to obtain a lower risk measure may misrepresent risk. The discussion offers no worked calculation, regulatory guidance, or validation that a particular data window is appropriate; it emphasizes that risk inputs should reflect the product rather than a target classification.

Key ideas

• MRM is derived from VEV, which is derived from a VaR calculation.
• The VaR inputs described include standard deviation, skewness, excess kurtosis, and the recommended holding period.
• The response identifies at least two years of historical daily prices and applicable dividends as inputs for daily funds.
• Simulated prices can be reverse engineered from the formula but may not represent the real fund or share class history.
• Selecting a less volatile period solely to lower the reported measure risks misrepresenting product risk.

Tags

# PRIIPs Kid MRM Calculations


# PRIIPs Kid MRM Calculations












I am currently modelling a category 2 PRIIP in Excel based on some share price datas provided by a client. The calculations yield a MRM (Market Risk Measure) of 5. Now, the client wants us to conduct an analysis on how they could get a MRM of 2. The question is, how would you do that?

MRM is determined by VeV, which is determined by VaR. The calculation of VaR includes the standard deviation, skewness, excess kurtosis and the number of months in the recommended holding period. So, we want VeV to be in an interval between 0,5% and 5,0% (currently 30%). That would mean we need new values for standard deivation, skewness and excess kurtosis. Is that even possible with the current dataset? Do we even need a dataset or do we just throw in some fictional numbers of standard deviation, skewness and excess kurtosis in order to hit a VeV between 0,5% and 5,05? How would you guys approach this task? Maybe use Solver in Excel?

I really look forward to your answers :)

/Pablo

## Answer by Lahcen Oula (score 0)

https://quant.stackexchange.com/a/60217

Indeed, the calculation of market risk measure is based on the VEV by using at least 2 years historical prices for daily funds and dividends if any. you can do the reverse engineering of the formula in order to define the price simulated based on Black-Scholes, however, this will not reflect the real-time series of your fund/or share class.

## Answer by KaiSqDist (score 0)

https://quant.stackexchange.com/a/79708

I am not an expert on modelling MRM, but if you need "lower" SD, SKEW or KURT, it might make more sense to use less volatile periods or a shorter time span. But IMO, what your client seems to be suggesting is highly misrepresentative and is not the right way to calculate risk analytics.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.