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Communicating Collections of Risks with Horizon Charts and Monte Carlo Simulations


Communicating risks with horizon charts and Monte Carlo simulations enriches communication with decision makers.
Communicating risks with horizon charts and Monte Carlo simulations enriches communication with decision makers.

 A risk horizon chart is produced using an underlying Monte Carlo simulation engine. This methodology generates thousands of possible scenarios by picking distribution function samples for frequencies and severities of displayed risks. Horizon charts provide richer communication of risks when communicating with decision makers.

 

Risk Heat Maps Are Limited

A significant limitation in risk heat maps is their inability to rank or prioritize risks. A risk located in a box with frequency row 1 and severity column 4 will “weigh” the same as a risk in its inverted 4-1 position. There is no way to know which of the two is a priority due to the very nature of the tool, which does not quantify its magnitude.

 

Three Dimensions Provide Richer Communication

Alternatively, I have developed a “risk horizon” chart where the risks are classified three-dimensionally. Just as we see stars of different intensity and spatial positions in the sky, we can capture the three-dimensionality of a risk: its frequency, impact, and “product”: its monetary magnitude.

 

An Example

In this horizon chart, the vertical axis measures frequency and the horizontal axis measures severity or impact. The magenta bubble (lower right-hand corner) is a relatively low-frequency risk – it happens on average about 6 times a year, but its average impact is $660. When thousands of severity and impact scenarios are simulated, the result obtained is a risk whose monetary impact is $4,000, measured by the relative size of the bubble. In contrast, the light green bubble on the upper left-hand side has a mean frequency of 30 times a year and a mean impact for each of those events of $300. This generates a compound simulated effect of $9,400, 4.5X higher, on average, than the magenta risk.

An example of a horizon chart utilizing Monte Carlo simulations.
An example of a horizon chart utilizing Monte Carlo simulations.

 

Overcoming the previous two, the purple-centered bubble would generate an average impact of just under $15,000 by combining an average frequency of 33 annual events with an average severity of $450. It is 1.6 times higher than the second risk on the horizon, the light green bubble, and concentrates 16% of all the universe of risks, which are 20 in total.

 

The Power of Horizon Charts

This risk horizon chart was produced using an underlying Monte Carlo simulation engine. This methodology generates thousands of possible scenarios by picking distribution function samples for frequencies and severities of displayed risks. There is so much more information that can be conveyed on it when compared to the limited colorful experience of a risk matrix.


 

Fernando Hernandez’s international experience is geared towards training, consulting, developing, and communicating quantitative risk, financial applications, and decision-making models. As a consultant and trainer since 2003, Fernando has helped organizations set up their decision-making models and quantify and understand their risks. Before becoming a consultant and trainer, he worked for 10 years in corporate finance as CFO in various industries.

 

Fernando lives and works out of San Jose, Costa Rica. He has delivered over 430 trainings, workshops, and conferences, in three languages (English, Spanish, Portuguese), delivering close to 10,000 hours of consulting and training, on 37 countries and 78 cities for the past 17 years.

 

Fernando Hernandez can be reached at fhernandez@izirisk.com.

 


 

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