Accounting for Derivatives. Ramirez Juan

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