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Hi All,

The paperback and Kindle edition of the book "Risk-based Management in the World of Threats and Opportunities: A Project Controls Perspective" is now available in Amazon. Copy and paste the following on your browser. You can grab a copy now.

• http://www.amazon.com/RUFRAN-C.-FRAGO-PMI-RMP/e/B01055MPYI
• http://www.amazon.com/dp/B0104OFUDI/ref=rdr_kindle_ext_tmb
• https://www.linkedin.com/pulse/risk-based-management-world-threats-opportunities-rufran?trk=prof-post

Cheers,

RUFRAN C. FRAGO, P.Eng

The effort today is to develop a Monte Carlo simulation in Excel that works for operational risk losses rather than market or credit risk, where the technique is so often found. The objective is to build a fully working Microsoft Excel model which isn't just statistical theory but actually exemplifies the Monte Carlo simulation process in practice.

Click here to review and download the Monte Carlo example

By looking at a case study in Monte Carlo and Loss Data, we are able to see how important it is to model loss experience and to categorise operational risk loss events.

Recently I had a discussion on modelling risk with a fantastic and successful business person who said to me : "I have read about Monte Carlo, you even make mention to it on your blog but it doesn't make great sense to me. The maths in Monte Carlo is even worse because it seems to confuse the concept by taking it into an academic

Dimensioning Dependency, Correlation, Causality and Mutuality for multiple risk factors is an important modelling exercise risk analysts should entertain. Not doing so is one of the reasons why CAPM is busted.

Let’s look at a very straight forward method for measuring correlation in risk variables and for propagating a final outcome.

To continue reading, follow this link

In the world of operational risk, there are a lot of analysts who believe that they can dimension the impacts from uncertainty by counting the number of events they experience over a period of time and then multiply that count by the average loss amount for the total event horizon they observe.

This approach for quantifying the impacts from uncertainty is full of error and it should be avoided. In fact, let's be clear, it is so fundamentally wrong as a measure of exposure that it isn't even a goo