And Now, Spread-at-Risk

Gabriel Bousbib, head of risk management at Reuters America, explains why Spread-at-Risk is a better risk measure for non-leveraged financial institutions.

The simplicity of the Value-at-Risk concept has led many to recommend that it become a standard risk measure, not only for financial institutions involved in large-scale trading operations, but also for retail banks, insurance companies, institutional investors and nonfinancial concerns. The hope is that VAR could be used by investors and analysts to compare one firm's equity capital with that of another, the same way more traditional financial ratios are used.

To be relevant in risk management, however, VAR depends on two key elements-the ability or need to mark-to-market the firm's assets and liabilities, whether they be on or off the balance sheet, and the need for the firm to meet short-term large negative variations in the market value of its assets through its equity capital. One or both of these elements cannot easily be applied to firms such as insurance companies, retail banks, institutional investors and nonfinancial corporations.

The reluctance shown by these firms to adopt FASB 107 and FASB 115, which recommend the adoption of a mark-to-market approach for all assets and liabilities, goes beyond the issue of the volatility induced by marking-to-market assets and liabilities. It also reflects their belief that mark-to-market does not provide an accurate economic picture of their companies' financial status.

Let us take the case of a typical retail bank in the United States whose simplified balance sheet could look as follows:

With the probable exception of its mortgage loan portfolio, it will be impossible for this retail bank to mark-to-market its assets and liabilities accurately. For example, one could model demand deposits as a zero coupon perpetual debt. In practice, however, the behavior of demand deposits will depend on alternative deposit products offered by the marketplace, together with the necessity of some businesses and individuals to keep demand deposits.

Similarly, credit card receivables could be analyzed as a series of fixed or floating cash flows, taking into account the various caps and floors imbedded in the credit card (for example, maximum or minimum rate on a floating rate card, option to switch from a fixed-rate card to a floating-rate card and vice-versa). In practice, however, the behavior of credit card receivables depends not only on the current level of interest rates, but also on exogenous factors such as customer convenience; seasonality; the bank's pricing and fee policy; the bank's ability to modify the terms of the transaction by modifying, for example, the maximum rate charged on a credit card; and so on. Similar conclusions could be drawn for insurance products.

Contrary to the Treasury world, in which the value of a transaction is based solely on the current level of the relevant market variables, a retail bank or an insurance company must then incorporate additional dimensions, including customer behavior, pricing policies and so on. These differences are illustrated in the table below.

Marking-to-market doesn't provide actionable information to a retail bank or the insurance company's management. The need for marking-to-market transactions in the wholesale and treasury worlds is quite obvious. The organization depends mostly on "bought" money, which can be shifted very quickly. The organization's leverage via this professionally managed "bought" is significant and the organization must thus ensure on a daily basis, or even intraday, that it has sufficient equity capital to meet unexpected losses, leading to the concept of Value- at-Risk.

But at a retail bank or an insurance company, the source of funds is much more stable and "stickier," originating from a large number of small depositors. The organization's objective is to maximize its net margin, that is, the yield generated by its assets over the cost of its liabilities, over a fairly long time period (such as one quarter or one year). The potential variations of the organization's net margin as a function of market variables, as well as exogenous factors discussed earlier, is the relevant actionable information that must be provided to management.

Daily mark-to-market and VAR analyses do not provide any indication of these variations. In the Treasury world, market risk management should aim at measuring and managing the "instantaneous" (that is, daily) changes in mark-to-market. But market risk for a retail bank or an insurance company should focus on measuring and managing the volatility of the firm's net returns (asset return minus liability yield) over a given time horizon (such as monthly or annual).

To its credit, VAR, as currently defined, does a good job of addressing the needs of a highly leveraged institution interested in potential losses resulting from large market movements. The VAR measure provides debt and equity holders with a measure of the equity capital required to sustain market losses for which the market may not be willing to provide short-term funding. But this measure is clearly of limited value to the stakeholders of a non-leveraged financial institution.

A better and more actionable statistic for the non-leveraged financial institution could be developed around the concept of "Spread-at-Risk," which would measure an institution's anticipated net spread (assets minus liabilities) and its expected distribution over a reporting period (quarter, semester, year). The Spread-at-Risk would need to include the institution's projected pricing policies and incorporate the behavior of its clients. In addition to presenting senior management with an integrated risk profile of the institution, such a measure can provide a non-leveraged institution with multiple benefits, including improved pricing policies, better product design and optimized client targeting.

Let us show how Spread-At-Risk would be implemented for a retail bank. As interest rates vary and the bank's pricing levels relative to the market rates are modified, the yield or the cost for each bank product (expressed either in percentage rate or in dollar amounts) could vary for a number of reasons:

Market movements. Rates could decrease, thus decreasing the cost of a CD portfolio by a factor proportional to the change in market rates.

Product "contractual features." Rates could rise and a cap imbedded in revolving credit lines could go in the money, thus capping the yield of the asset at the cap rate.

Retail bank pricing policies. Rates could rise and prices on super saving accounts might not be adjusted accordingly, thus triggering a wave of withdrawals; the balances lost must then be funded at the current market rates.

Customer behavior. During the holiday season, retail card balances are likely to increase, thus increasing the dollar amounts earned by the retail card business.

Clearly, in practice, a retail bank may have hundreds of "products," and the concept of product in this discussion might represent groupings of products directly available to the bank's customers. The level of product aggregation would depend on a number of practical considerations, including data availability, modeling complexities and the level of granularity required.

For each product, over a given time horizon (say a month or a quarter) and for given market scenarios (generated via a historical simulation, a covariance or Monte Carlo analysis), a modeling function would then determine two sets of data. The first would be the expected yield or cost of the product ("Yield Matrix"), and the second would be the expected revenue/cost of the product, after taking into account the outstanding dollar balance of the product ("Dollar Matrix").

This information would be determined based on interest rate levels; pricing relative to the market; and forecasted balances based on expected new business, redemptions, renewal rates and so on.

Contrary to a traditional Value-at- Risk analysis, the Spread-at-Risk in the case of a retail bank must take into account pricing policies relative to the market, which in turn can impact the outstanding balances on given products.

Each box in the first table represents the expected yield or cost of the product for a combined change in market rates and pricing levels. The second table translates these yields or costs in dollars, based on expected balances, which will reflect balances as forecasted by the business units and adjusted for market movements and price changes.

Generating these matrices for each product and then aggregating them would provide the retail bank with a dollar matrix representing the dollar increase or decrease in income over the period considered. Dividing the dollar figure by the average net asset size would provide the retail bank with a Spread-at-Risk distribution. In other words, the Spread-at-Risk represents the increase or decrease in the bank's net margin as a function of market scenarios and the bank's pricing policies.

There are clear overlaps between strategic risk calculations for leveraged and non-leveraged financial institutions. Both require a sophisticated engine to generate a large number of market scenarios efficiently, using one of several methodologies that have gained acceptance in the market. Both require the ability to model the "value" of a product or a transaction as a function of market variables.

Yet, in the case of a non-leveraged financial institution, "value" designates the increase or decrease in the product's revenue or cost over the period considered, as opposed to change in mark-to-market in the traditional capital markets world. The concept of Spread-at-Risk thus provides non-leveraged financial institutions with a more meaningful measure of its market risk, as it integrates how both market movements and pricing policies can impact an institution's net income over the horizon period.