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A Regressive Approach to Hedge Accounting at Novelis One of the more important decisions to be made during the life of a hedge is how to assess its effectiveness. Louis W. Edwards, Director of Derivative Accounting and Reporting at Novelis, explains how Novelis assess their hedge accounting and relationships, paying particular attention to their use of regression analysis, and the challenges they face when making these decisions.

A Regressive Approach to Hedge Accounting at Novelis

by Louis W. Edwards, Director, Derivative Accounting and Reporting, Novelis

One of the more important decisions to be made during the life of a hedge is how to assess its effectiveness. Both ASC 815 (FAS 133) and IAS 39 require companies to assess hedge effectiveness at the inception of the relationship and on a periodic basis throughout its life. This requirement includes a forward-looking prospective assessment and a backward-looking retrospective assessment. Choosing the right method is important because if your test results fail to meet the criteria you establish for them, you must discontinue hedge accounting for that hedge and changes in fair value must be recognised in earnings. Moreover, once you have selected a method, you cannot change it without de-designating the hedge relationship.

Companies may elect to forgo periodic effectiveness testing by asserting that the critical terms of the exposure match those of the derivative. The problem with this method is that auditors and regulators have taken a very narrow definition of the term ‘match’. A payment date that is different by as little as one day may make the critical terms match method inappropriate. Many companies have been burned by using critical terms match and then being told by their auditors or the SEC that the method was inappropriate. This has led to more than a few financial restatements. Given the risks of the critical terms match, many companies now use the dollar offset method as their default method for assessing hedge effectiveness. The popularity of this method arises from its ease of use. The change in the value of the derivative is compared to the change in the value of the hedged item. If the ratio of the two changes lies within a predetermined range - say 80% to 125% - the hedge may be deemed to be highly effective.

Companies not wanting to bear the risks associated with critical terms match or the dollar offset method are increasingly turning to regression analysis to assess hedge effectiveness.

Small changes, big problems

The risk of the dollar offset method is that a seemingly good hedge can fail this test without warning, especially if markets are relatively stable. Assume you have $500m in variable-rate debt, hedged with an interest rate swap. If the derivative changes in value by $15,000 and the hedged item changes by $10,000, the hedge will fail because the ratio of the two changes falls outside of the 80-125 range. It does not matter that both changes are small relative to the notional amount. This can be frustrating when you know that most of the time, the hedge would have been effective, but dollar offset is not a ‘most of the time’ method of testing.

Companies not wanting to bear the risks associated with critical terms match or the dollar offset method are increasingly turning to regression analysis to assess hedge effectiveness. Regression analysis is a statistical method where changes in the derivative and changes in the hedged item are measured at regular intervals over time and a line is mathematically drawn through the measurements. The slope of that line is an important output; it represents the overall ratio of derivative to hedged item. It is like doing a series of dollar offset tests and then averaging the results. Therefore, a few measurements may fall outside the range without causing the overall hedge relationship to fail. Regression analysis is also useful when there is basis difference between the derivative and the exposure, as is often the case with commodity hedges.

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