Accounting for Derivatives. Ramirez Juan

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      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.

      • 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.

      • 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).