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2.6.1 Qualifying Criteria for Hedge Accounting

To qualify for hedge accounting, there are three requirements that a hedging relationship must meet (see Figure 2.6):

1. The hedging relationship consists only of eligible hedging instruments and eligible hedged items.

2. At the inception of the hedging relationship there is formal designation and documentation of the hedging relationship and the entity's risk management objective and strategy for undertaking the hedge. That documentation shall include identification of the hedging instrument, the hedged item, the nature of the risk being hedged and how the entity will assess whether the hedging relationship meets the hedge effectiveness requirements (including its analysis of the sources of hedge ineffectiveness and how it determines the hedge ratio).

3. The hedging relationship meets all three hedge effectiveness requirements.

Figure 2.6 Qualifying criteria for hedge accounting.

The three hedge effectiveness requirements are as follows:

1. There is an economic relationship between the hedged item and the hedging instrument.

2. The effect of credit risk does not dominate the value changes that result from that economic relationship.

3. The weightings of the hedged item and the hedging instrument (i.e., the hedge ratio of the hedging relationship) are the same as those resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. However, that designation shall not reflect an imbalance between the weightings of the hedged item and the hedging instrument that would create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting.

The first effectiveness requirement means that the hedging instrument and the hedged item must be expected to move in opposite directions as a result of a change in the hedged risk (i.e., there is an economic relationship and not just statistical correlation). For example, it would be possible to hedge a West Texas Intermediate (WTI) crude oil exposure using a Brent crude oil forward instrument. A perfect correlation between the hedged item and the hedging instrument is not required and, indeed, would not be sufficient on its own.

The second requirement indicates that the impact of changes in credit risk should not be of a magnitude such that it dominates the value changes, even if there is an economic relationship between the hedged item and hedging derivative. This implies that when the creditworthiness of the entity or the counterparty to the hedging instrument notably deteriorates, the hedging relationship may not qualify for hedge accounting going forward because the change in the credit risk may be the largest factor affecting the hedging instrument's fair value change.

The third requirement indicates that the actual hedge ratio used for accounting should be the same as that used for risk management purposes, unless the ratio is inconsistent with the purpose of hedge accounting. IFRS 9 tries to avoid deliberate underhedging, either to minimise recognition of ineffectiveness in cash flow hedges or the creation of additional fair value adjustments to the hedged item in fair value hedges.

2.6.2 Hedge Ratio

IFRS 9 does not define the term hedge ratio, but I have assumed throughout this book that it is the designated amount (i.e., notional) of the hedged item compared with the designated amount (i.e., notional) of the hedging instrument within the hedging relationship (alternatively, it may be defined the other way around).

In most simple hedges, where the underlyings of the hedging instrument and the hedged item match, the hedge ratio is 1:1. For example, a highly probable forecast sale denominated in USD of an entity whose functional currency is the EUR hedged with a EUR–USD FX forward will result in a 1:1 hedge ratio.

In other hedging relationships the hedge ratio may differ from 1:1, especially where the underlyings of the hedged item and the hedging instrument differ. This is the case where there is an underlying for which its market is notably more liquid than that of the hedged item underlying, and both underlyings are highly correlated (a “proxy hedge”). For example, an entity whose functional currency is the EUR may decide to hedge a highly probable forecast sale denominated in Norwegian krone (NOK) with a more liquid Swedish krona (SEK) proxy: a SEK–EUR FX option. The entity may decide that 1 NOK is best hedged with 0.94 SEK, and as a result, the hedge ratio is set at 1:0.94. Such an assessment is usually made by considering historical and current market data for the hedged item and hedging instrument where possible, taking into account their relative performance in the past.

2.6.3 Effectiveness Assessment

Periodically the entity shall assess whether the hedging relationship meets the hedge effectiveness requirements – hedge effectiveness assessment. This assessment is probably the most operationally challenging aspect of applying hedge accounting. At a minimum, whichever comes first, IFRS 9 requires that hedge effectiveness be evaluated (see Figure 2.7):

• at the inception of the hedge;

• at each reporting date, including interim financial statements; and

• upon a significant change in the circumstances affecting the hedge effectiveness requirements.

Figure 2.7 Frequency of hedge effectiveness assessments.

Each effectiveness assessment relates to future expectations about hedge effectiveness and is therefore only forward-looking.

2.6.4 Effectiveness Assessment Methods

One of the effectiveness requirements is that an economic relationship exists between the hedging instrument and the hedged item, or in other words, that the hedging instrument and the hedged item have values that will generally move in opposite directions. IFRS 9 does not specify a method for assessing whether an economic relationship exists between a hedging instrument and a hedged item. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship, including its sources of hedge ineffectiveness.

IFRS 9 states that an entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can constitute a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.

The effectiveness requirement of an existence of an economic relationship between the hedged item and the hedging instrument (the “economic relationship requirement”) is commonly assessed by applying one of the following methods:

• The critical terms method. This is a qualitative method (i.e., no numerical analysis is performed).

• The simple scenario analysis method: assessing how the hedging relationship would behave under various future scenarios. This is a quantitative method.

• The linear regression method: assessing, using historical information, how the hedging relationship would have behaved if it had been entered into in the past. This is a quantitative method.

• The Monte Carlo simulation method: assessing how the hedging relationship would behave under a large number of future scenarios. This is a quantitative method.

IFRS 9 requires an entity to specify at hedge inception, in the hedge documentation, the method it will apply to assess the hedge effectiveness requirements and to apply that method consistently during the life of the hedging relationship. The method chosen by the entity has to be applied consistently to all similar hedges unless different methods are explicitly justified.

If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.

A quantitative method may also be used to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements. An entity can use the same method as that used to assess the economic relationship requirement, or a different method.

2.6.5 The Critical Terms Method

The critical terms method is the simplest way to assess whether the economic relationship requirement is met. Under IFRS 9, an entity may conclude that there is an economic relationship between the hedged item and the hedging instrument if the critical terms of the hedged item and hedging instrument match or are closely aligned. At a minimum, the following critical terms must be the same or closely aligned:

• the notional amounts;

• the maturity and interim periods (e.g., interest periods); and

• the underlying (e.g., Euribor 3-month rate).

This conclusion is valid while the credit risk associated with the entity or the counterparty to the hedging instrument is considered to be very low.

2.6.6 The Simple Scenario Analysis Method

The simple scenario analysis method is the simplest quantitative method to assess whether a hedging relationship meets the economic relationship requirement. The goal of this method is to reveal the behaviour of changes in fair value of both the hedging item and the hedging instrument under specific scenarios.

Normally a few scenarios (e.g., four) are simulated. Each scenario assumes that the underlying risk being hedged will move in a specific way over a certain period of time. The main drawback of the scenario analysis method is the subjectivity in selecting the scenarios. The scenarios chosen may not be followed by the underlying hedged risk once the hedge is in place, and therefore the conclusions of the analysis may not depict the realistically expected behaviour of the hedge. As a result, this method is used to assess hedging relationships in which the critical terms method cannot be used but it is quite clear that the changes in fair value of the hedge item and hedging instrument will almost fully offset each other.

For example, assume that an entity, with the EUR as its functional currency, enters into a 12-month GBP–EUR FX forward with a forward rate of 0.8015 to hedge a highly expected GBP-denominated sale expected to occur in 15 months. The spot rate was 0.8000 at the time. The significantly different maturities of the hedged item (15 months) and the hedging instrument (12 months) make the use of the critical terms method inappropriate. However, the entity concludes that a scenario analysis captures the relevant characteristics of the hedging relationship. The economic relationship requirement can be assessed under the following three scenarios:

1. a two-standard deviation depreciation of the GBP relative to the EUR during the next 12 months;

2. an unchanged 0.80 spot rate in 12 months' time;

3. a two-standard-deviation appreciation of the GBP relative to the EUR during the next 12 months.

Establishing the FX Rate of a Scenario

At the moment of the analysis, a currency pair is trading at its spot rate. However, it is impossible to know with certainty what would be the FX spot rate at the end of the analysis horizon. Assuming a normal distribution of FX rate, it is possible to calculate a range in which, with a specific probability, the FX rate is expected to be on a specific date in the future. The boundaries of the range can be calculated according to the following formula:

where:

σ is the standard deviation. Normally, σ is set at the volatility of an option with strike at-the-money forward with term coinciding with the analysis horizon and a currency pair coinciding with that of the hedge item.

N is the number of standard deviations. Figures based on a 95 % confidence interval of require N = 1 and N = –1. For a 99 % confidence interval, N = 2 and N = –2 are used.

T is the number of years elapsed from the current date to the end of the analysis horizon.

In our example, assuming a 12 % volatility of the GBP–EUR FX rate, the FX spot rates at the end of the 12-month period would be:

• under the first scenario, 1.0170 (=0.8000 × exp(2 × 12 % × 1));

• under the second scenario, 0.8000;

• under the third scenario, 0.6293 (=0.8000 × exp(–2 × 12 % × 1)).

The movements under the first and third scenario are very large. The entity expected the GBP–EUR FX rate to be between 0.8293 and 1.0170 with a 99 % probability.

2.6.7 The Regression Analysis Method

The regression analysis method is typically applied when a proxy hedge is used (i.e., when the underlying of the hedged item and that of the hedging instrument differ). The idea is to analyse the behaviour of the hedging relationship using historical market rates. Regression analysis is a statistical technique that assesses the level of correlation between one variable (the dependent variable) and one or more other variables (known as the independent variables). In the context of hedge effectiveness testing, the primary objective is to determine whether changes in the fair value of the hedged item and the hedging instrument attributable to a particular risk were highly correlated in the past, and thus supportive of the assertion that there will be a high degree of offset in changes in the fair value of the hedged item and the hedging instrument in the future. The regression analysis is a process that can be divided into three major steps, as shown in Figure 2.8.

Figure 2.8 Phases in the regression analysis method.

The first step in the regression analysis is to obtain the inputs to the analysis: the X and Y observations. Figure 2.9 outlines this process. This step is quite complex and requires a computer program (e.g., Microsoft Excel) to perform it. The idea is to go back to a specific date (the simulation period start date), assume that the hedging relationship started on that date and observe the behaviour of the hedging relationship using the historical market data of the simulation period. The simulation period ends on a date such that the term of the simulation is equal to the term of the actual hedge. This process is repeated several times.

Figure 2.9 Process to obtain X and Y observations.

The second step of the regression analysis is to plot the values of the X and Y variables and to estimate a line of best fit. A pictorial representation of the variables in the standard regression equation is shown in Figure 2.10.

Figure 2.10 Regression line of best fit.

Regression analysis uses the “least squares” method to fit a line through the set of X and Y observations. This technique determines the slope and intercept of the line that minimises the size of the squared differences between the actual Y observations and the predicted Y values. The linear equation estimated is commonly expressed as:

where

X is the change in the fair value (or cash flow) of the hedging instrument attributable to the risk being hedged;

Y is the change in the fair value (or cash flow) of the hedged item attributable to the risk being hedged;

α is the intercept (where the line crosses the Y axis);

β is the slope of the line;

ε is the random error term.

The third step of the regression process is to interpret the statistical results of the regression and determine whether the regression suggests that there is an economic relationship between the hedged item and the hedging instrument. The following three statistics must achieve acceptable levels to provide sufficient evidence for such a conclusion:

• R-squared, or the coefficient of determination, measures the degree of explanatory power or correlation between the dependent and the independent variables in a regression. R-squared indicates the proportion of variability in the dependent variable that can be explained by variation in the independent variable. By way of illustration, an R-squared of 95 % indicates that 95 % of the movement in the dependent variable is “explained” by variation in the independent variable. R-squared can never exceed 100 % as it is not possible to explain more than 100 % of the movement in the independent variable. IFRS 9 does not provide a minimum reference R-squared level, but an R-squared greater than or equal to 80 % may probably indicate a high correlation between the hedged item and the hedging instrument. In my view, and this is notably subjective opinion, an R-squared below 70 % is likely to imply an absence of economic relationship between the hedged item and the hedging instrument. In any case, it is important to remember that a pure high correlation is not sufficient; there also has to be an economic justification for such a high correlation. Moreover, from a statistical perspective, R-squared by itself is an insufficient indicator of hedge performance.

• The slope β of the regression line. There is no bright line for the slope. Under the previous financial instruments accounting standard (IAS 39) the slope was required to be between –0.80 and –1.25. Judgement is required to decide whether a given slope means that the economic relationship requirement has been met. The slope can provide an indication of the appropriate hedge ratio.

• The t-statistic or F-statistic. These two statistics measure whether the regression results are statistically significant. The t-statistic or F-statistic must be compared to the relevant tables to determine statistical significance. A 95 % or higher confidence level is generally accepted as appropriate for evaluating the statistical validity of the regression.

2.6.8 The Monte Carlo Simulation Method

One way to draw meaningful conclusions about an economic relationship assessment is to test the behaviour of the changes in fair value of both the hedging item and the hedging instrument under a very large number of scenarios of the underlying risk being hedged. For some highly structured products, the use of the scenario analysis method may miss a potential scenario that has a substantial effect in the hedging instrument's payout. Monte Carlo simulation is a tool that provides multiple scenarios by repeatedly estimating hundreds of different paths of the risk being hedged, based on the probability distribution of the risk. In my view, a well-performed Monte Carlo simulation can be very effective in assessing hedge effectiveness when the payout of the hedging instrument is highly dependent on the behaviour of the underlying risk during the life of the instrument.

2.6.9 Suggestions Regarding the Assessment Methods

The entity shall use the method that captures the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness. What follows is just my own personal recommendation (remember that an entity's external auditors always have the last word) regarding which method to use (see Figure 2.11):

• Use the critical terms method when the critical terms of the hedged item and the hedging instrument perfectly match. Remember, the critical term method is a qualitative assessment and therefore relatively simple to document.

• Use the critical terms method coupled with a single scenario analysis when there is a slight mismatch between the critical terms of the hedged item and the hedging instrument – for example, where there is a relatively short time lag between the interest periods of a swap and those of a hedged loan.

• Use the scenario analysis method when there is a mismatch in dates or notionals of the hedged item and the hedging instrument, and the latter is a vanilla hedging instrument (e.g., a swap, a forward, a standard option).

• Use the regression analysis method when there is a mismatch in underlyings of the hedged item and the hedging instrument (i.e., a proxy hedge has been used), and this instrument is a vanilla hedging instrument (e.g., a swap, a forward, a standard option).

• Use the Monte Carlo simulation method when the hedging instrument is complex and/or when its payout is highly dependent on the behaviour of the underlying risk during the life of the instrument (e.g., a range accrual with knock-out barriers).

Figure 2.11 Recommended decision tree of hedge effectiveness assessment methods.