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Dear S@R,

I am not interested in the use of stochastic models, and particularly Monte Carlo simulations.  I believe that these approaches too often lead to underestimating risks of extreme events, by failing to indentify correlated variables, first order or second order variables, and correlations in sample populations. I believe that the use of these models carries an important responsibility in the way banks failed to address risks correctly.
Best regards,

Dear NN,

We wholeheartedly agree on the errors you point out, especially for the banking sector. However this is per se not the fault of Monte Carlo simulation as a technique, but in the way some models has been implemented and later misused.

We also have read the stories about bank risk managers (and modellers) forced by higher management to change important risk parameters to make further loans possible.

We just do not relay only on normal variables with short slim tails and simple VaR calculations. For risk calculations we alternatively use shortfall and spectral risk, the latter to give progressively larger weights to losses that can be disastrous. This will be a topic in a future post on our Web site.

However I beg to differ with you on the question of correlations. In my experience large correlation matrixs is a part of the problem you describe. Such correlation matrixs will undoubtedly contain spurious correlations giving false estimates of important relations. This is why we model all important relations, using the unexplained variance as a part of the uncertainty describing the problem under study – the company’s operations.

Many claim that what killed Wall Street was uncritical use of David X. Li’s copula formula, where errors massively increase the risk of the whole equation blowing up (Salmon, 2009). We have therefore never used his work, relaying more on both B. Mandelbrot and Taleb Nasim’s views.

As we se it, the use of copula’s formua was done to avoid serious statistical analysis and simulation work – which is what we do.

If you should reconsider, we will be happy to meet with you to explain the nature of our work. To us nothing is better than a demanding customer.

Best regards



Salmon, Felix (2009,02,23). Recipe for Disaster: The Formula That Killed Wall Street. Wired Magazine, Retrieved 0702,2009, from

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S@R develops models for support of decision making under uncertainty. Taking advantage of recognized financial and economic theory, we customize simulation models to fit specific industries, situations and needs.

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