“How can you be better than us understand our business risk?”

This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can use your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.

The main reason for this lies in how we can treat uncertainties1 in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.

Risk is usually compartmentalized in “silos” and regarded as proprietary to the department and – not as a risk correlated or co-moving with other risks in the company caused by common underlying events influencing their outcome:

When Queen Elizabeth visited the London School of Economics in autumn 2008 she asked why no one had foreseen the crisis. The British Academy Forum replied to the Queen in a letter six months later. Included in the letter was the following:

One of our major banks, now mainly in public ownership, reputedly had 4000 risk managers. But the difficulty was seeing the risk to the system as a whole rather than to any specific financial instrument or loan (…) they frequently lost sight of the bigger picture2.

To be precise we are actually not simulating risk in and of itself, risk just is a bi-product from simulation of a company’s financial and operational (economic) activities. Since the variables describing these activities is of stochastic nature, which is to say contains uncertainty, all variables in the P&L and Balance sheet will contain uncertainty. They can as such best be described by the shape of their frequency distribution – found after thousands of simulations. And it is the shape of these distributions that describes the uncertainty in the variables.

Most ERM activities are focused on changing the left or downside tail – the tail that describes what normally is called risk.

We however are also interested in the right tail or upside tail, the tail that describes possible outcomes increasing company value. Together they depict the uncertainty the company faces:

S@R thus treats company risk holistic by modeling risks (uncertainty) as parts of the overall operational and financial activities. We are thus able to “add up” the risks – to a consolidated level.

Having the probability distribution for e.g. the company’s equity value gives us the opportunity to apply risk measures to describe the risk facing the shareholders or the risk added or subtracted by different strategies like investments or risk mitigation tools.

Since this can’t be done with ordinary addition3 (or subtraction) we have to use Monte Carlo simulation.

The value added by this are:

  1.  A method for assessing changes in strategy; investments, new markets, new products etc.
  2. A heightening of risk awareness in management across an organization’s diverse businesses.
  3. A consistent measure of risk allowing executive management and board reporting and response across a diverse organization.
  4. A measure of risk (including credit and market risk) for the organization that can be compared with capital required by regulators, rating agencies and investors.
  5. A measure of risk by organization unit, product, channel and customer segment which allows risk adjusted returns to be assessed, and scarce capital to be rationally allocated.
  6.  A framework from which the organization can decide its risk mitigation requirements rationally.
  7. A measure of risk versus return that allows businesses and in particular new businesses (including mergers and acquisitions) to be assessed in terms of contribution to growth in shareholder value.

The independent risk experts are often essential for consistency and integrity. They can also add value to the process by sharing risk and risk management knowledge gained both externally and elsewhere in the organization. This is not just a measurement exercise, but an investment in risk management culture.


All business planning are built on forecasts of market sizes, market shares, prices and costs. They are usually given as low, mean and high scenarios without specifying the relationship between the variables. It is easy to show that when you combine such forecasts you can end up very wrong4. However the 5 %, 50 % and 95 % values from the scenarios can be used to produce a probability distribution for the variable and the simultaneous effect of these distributions can be calculated using Monte Carlo simulation, giving for instance the probability distribution for profit or cash flow from that market. This can again be used to consolidate the company’s cash flow or profit etc.

Controls and Mitigation

Controls and mitigation play a significant part in reducing the likelihood of a risk event or the amount of loss should one occur. They however have a material cost. One of the drivers of measuring risk is to support a more rational analysis of the costs and benefits of controls and.
The result after controls and mitigation becomes the final or residual risk distribution for the company.

Distributing Diversification Benefits

At each level of aggregation within a business diversification benefits accrue, representing the capacity to leverage the risk capital against a larger range of non-perfectly correlated risks. How should these diversification benefits be distributed to the various businesses?

This is not an academic matter, as the residual risk capital5  attributed to each business segment is critical in determining its shareholder value creation and thus its strategic worth to the enterprise. Getting this wrong could lead the organization to discourage its better value creating segments and encourage ones that dissipate shareholder value.

The simplest is the pro-rata approach which distributes the diversification benefits on a pro-rata basis down the various segment hierarchies (organizational unit, product, customer segment etc.).

A more right approach that can be built into the Monte Carlo simulation is the contributory method which takes into account the extent to which a segment of the organization’s business is correlated with or contrary to the major risks that make up the company’s overall risk. This rewards counter cyclical businesses and others that diversify the company’s risk profile.

Aggregation with market & credit risk

For many parts of an organization there may be no market or credit risk – for areas, such as sales and manufacturing, operational and business risk covers all of their risks.

But at the company level the operational and business risk needs to be integrated with market and credit risk to establish the overall measure of risk being run by the company. And it is this combined risk capital measure that needs to be apportioned out to the various businesses or segments to form the basis for risk adjusted performance measures.

It is not enough just to add the operational, credit and market risks together. This would over count the risk – the risk domains are by no means perfectly correlated, which a simple addition would imply. A sharp hit in one risk domain does not imply equally sharp hits in the others.

Yet they are not independent either. A sharp economic downturn will affect credit and many operational risks and probably a number of market risks as well.

The combination of these domains can be handled in a similar way to correlations within operational risk, provided aggregate risk distributions and correlation factors can be estimated for both credit and market risk.

Correlation risk

Markets that are part of the same sector or group are usually very highly correlated or move together. Correlation risk is the risk associated with having several positions in too many similar markets. By using Monte Carlo simulation as described above this risk can be calculated and added to the company’s risks distribution that will take part in forming the company’s yearly profit or equity value distribution. And this is the information that the management and board will need.

Decision making

The distribution for equity value (see above) can then be used for decision purposes. By making changes to the assumptions about the variables distributions (low, medium and high values) or production capacities etc. this new equity distribution can be compared with the old to find the changes created by the changes in assumptions etc.:

A versatile tool

This is not only a tool for C-level decision-making but also for controllers, treasury, budgeting etc.:

The results from these analyses can be presented in form of B/S and P&L looking at the coming one to five (short-term) or five to fifteen years (long-term); showing the impacts to e.g. equity value, company value, operating income etc. With the purpose of:

  • Improve predictability in operating earnings and its’ expected volatility
  • Improve budgeting processes, predicting budget deviations and its’ Evaluate alternative strategic investment options
  • Identify and benchmark investment portfolios and their uncertainty
  • Identify and benchmark individual business units’ risk profiles
  • Evaluate equity values and enterprise values and their uncertainty in M&A processes, etc.

If you always have a picture of what really can happen you are forewarned and thus forearmed to adverse events and better prepared to take advantage of favorable events.go-on-look-behind-the-curtainFrom Indexed: Go-on-look-behind-the-curtain6


Print Friendly, PDF & Email
  1. Variance is used as measure of uncertainty or risk. []
  2. The letter from the British Academy to the Queen is available at: http://media.ft.com/cms/3e3b6ca8-7a08-11de-b86f-00144feabdc0.pdf []
  3. The variance of the sum of two stochastic variables is the sum of their variance plus the covariance between them. []
  4. https://www.strategy-at-risk.com/2009/05/04/the-fallacies-of-scenario-analysis/ []
  5. Bodoff, N. M.,  Capital Allocation by Percentile Layer VOLUME 3/ISSUE 1 CASUALTY ACTUARIAL SOCIETY, pp 13-30, http://www.variancejournal.org/issues/03-01/13.pdf

    Erel, Isil, Myers, Stewart C. and Read, James, Capital Allocation (May 28, 2009). Fisher College of Business Working Paper No. 2009-03-010. Available at SSRN: http://ssrn.com/abstract=1411190 or fttp://dx.doi.org/10.2139/ssrn.1411190 []

  6. From Indexed: http://thisisindexed.com/2012/02/go-on-look-behind-the-curtain/ []


About the Author

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.

Post a Reply