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Risk management involves ‘uncertain’ outcomes. In financial analysis, the greater the variability in financial outcomes, the greater the risk.

The Efficient Frontier

One of the most general strategies in financial analysis is determining relative financial value by finding the greatest yield (or rate of return) for a given level of risk (or variability in cash flows). The yield is often referred to as alpha and the variability in return is referred to as beta. For any given level of beta, investors tend to prefer assets with the greatest alpha. The set of assets that provide this maximum alpha for each level of beta is called the ‘Efficient Frontier’ – and these are the candidates from which investors usually select investments.

It is essential that any financial analysis and all risk analyses include both ‘point’ estimates of expected return and ‘distribution’ estimates of return for given scenarios to allow the comparing of variability in outcomes (beta) of assets.


A simplified risk-management matrix that illustrates the four major strategies of risk management.

General Principles of Risk Management

Another general principle of risk management is to assess both the relative ‘impact’ of a risk and the relative ‘likelihood’ of that risk. Some risks are very frequent but have a very small impact. Some risks are relatively rare but are catastrophic. The management of risk requires that a specific strategy be developed for each category of risk.

Some risk managers classify their management strategies as having SARA characteristics – which stands for Share, Avoid, Reduce, Accept. These strategies are illustrated in the accompanying risk matrix.

Model Risk Management

One category of risk that has become increasingly important (as financial markets and instruments have become much more volatile lately) is that of Model Risk Management. This relates to the risk that arises from developing and relying upon quantitative models for estimating other risks.

As financial managers begin to rely upon the output of a financial model, for example, they might become blind to the limitations of those very same financial models. Sometimes the volatility of certain markets far exceeds the ‘normal’ limits within which those models have been developed. The ‘fat tails’ of distributions discovered in the 2008 market disruptions proved that sometimes entire markets are disrupted in ways that are wholly unanticipated… Events and correlations among variables that seemed impossible in ‘normal’ times actually occur. This is like a string of ‘100 year floods’ that occur in multiple back-to-back years. Model risk management is designed to identify the underlying weaknesses of the models that provide us with this false sense of security.

Download a sample chapter from Risk-Based Pricing book by Craig M. Allen.

See other posts on risk management.

Financial modeling is the process of converting data and assumptions into a projection of future economic outcome.

Sample Expected Cash Flow

For Debt Modeling

Typically, a financial model for debt instruments utilizes the parameters of the financial instrument – the debt balance, the term and the loan coupon – to generate a ‘pristine’ model of what cash flows will result in a ‘pristine’ situation… that is if the contract terms are fulfilled completely with no options exercised.

Once the pristine cash flows of a debt instrument are understood, then various scenarios are overlaid the pristine flows – to estimate what would happen if certain options are exercised. For example, with many debt instruments, the borrower is able to repay the loan balance early with little or no repayment penalty. The likelihood of the early repayment option being exercised increases if interest rates fall and the borrower can refinance at a reduced cost of borrowing. Consequently, most investors evaluate the value of a debt instrument by utilizing some expected distribution of future interest rates. For each scenario of future interest rates, a separate estimate of cash flows will result – with the probability of repayment in each period corresponding to some comparison of the relative cost of borrowing in that future period.

In addition to early debt repayment, many financial models incorporate some expected distribution of future borrower defaults. The distribution of defaults may incorporate economic conditions that might be expected. The distribution of defaults will also most certainly incorporate the historical record of defaults by similar borrowers. When loan defaults (and the corresponding payment delinquencies) are incorporated into financial models, the models often become quite complex, with not only estimates of delayed and missing payments, but also estimates of asset recoveries when the loans are collateralized – with corresponding costs estimated which might be associated with the recovery process (e.g., legal costs, or the cost of repossessing an automobile and shipping it off to auction).

The major categories of risks that are incorporated into debt-type cash flow models include market risks (such as with the future interest rates that might become available), credit risks (that is, the frequency of defaults and delinquencies for the borrower population), and operational risks (the operating risks associated with the lender and associated service providers).

For Business Cash Flow Modeling

Many businesses are valued based upon the net cash flow after expenses. These business models are often dependent upon estimates of customer growth, of new products introduced, the estimated future costs associated with producing a product and with other business risks.

The business cash flow projections can be as varied as the business type. Often, financial models and business analysis rely upon ‘common-sized’ financial statements that are collected and shared among business-financing institutions. With these common-sized statements, businesses are grouped by type of business and by size (in terms of turn over), and financial statements are presented with each type of income and expense as an expected relationship with sales or income.

Download a sample chapter from Risk-Based Pricing book by Craig M. Allen.

See other posts on finance and risk.

Cornerstone Publication – MANN

On certain occasions, a single publication opens up an enormous field of engagement. Such was the case with the publication of the MANN model papers. The ‘Multidimensional Analysis of Nearest Neighbors’ was characterized as a novel way of aggregating cohorts of borrowers into neighborhoods – or into collections that shared similar attributes. The initial paper was published as: Allen, Craig M., “Credit scoring and risk-adjusted pricing: a review of techniques,”… Read More »Cornerstone Publication – MANN

Statistical Process Control & Management

Obviously Delphi started its focus in the financial markets. But, it was fortunate to have been able to engage in some fairly innovative forays into very different spheres of work. One of the areas that was particularly enjoyable for Delphi involved the solving of problems with very physical characteristics – process control or process management. In the manufacture of certain products – say machined parts in the aerospace industry –… Read More »Statistical Process Control & Management