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HEAT™ or Hedge Effectiveness Analysis Toolkit is JPMorgan's latest addition to a long list of innovative and cutting-edge risk management solutions. HEAT is destined to help corporations navigate the complexities of hedge effectiveness testing under IAS 39 and FAS 133. HEAT comprises a consistent framework incorporating alternative methodologies for understanding and implementing hedge effectiveness testing. It is unique because it enables corporations to assess the effectiveness of hedges in both economic and accounting terms and also enables corporations to estimate the potential impact on earnings if hedge accounting is not obtained. While HEAT provides corporations with a consistent framework incorporating many alternative methodologies for hedge effectiveness testing, auditors will ultimately determine the appropriateness of any given methodology from a regulatory and accounting perspective, and as such accounting advice should be sought before implementing a particular methodology. In practice, even relatively simple hedge effectiveness methodologies can give surprising and sometimes counterintuitive results. HEAT helps to address the pitfalls that need to be negotiated in developing a consistent and intuitive approach to evaluating hedge effectiveness.

According to IAS 39 or FAS 133 an a posteriori test for hedge effectiveness has to be implemented when using hedge accounting. Both standards do not regulate which numerical method has to be used.

A number of hedge effectiveness tests have been published recently. Such tests are of different quality; for example, not all of them can deal with the problem of small numbers. This means a test might determine an effective hedge to be ineffective, a scenario which would increase the volatility in earnings. Therefore, it seems useful to have criteria at hand to discriminate and assess hedge effectiveness tests.

In this paper, we introduce such objective criteria, which we develop according to our understanding of miminum economic requirements. They are applicable to tests based on market values of two points in time as well as tests based on time series of market values.

According to our criteria we compare common tests like the dollar offset ratio, regression analysis or volatility reduction, showing strengths and weaknesses. Finally, we develop a new Adjusted Hedge Interval test based on our previous one (Hailer, AC and SM Rump (2003). Zeitschrift für das gesamte kreditwesen, 56(11), 599–603). Our test does not show weaknesses of other effectiveness tests.

The IFRS 9 on Financial Instruments has made an important contribution to the credit loss recognition process and financial reporting by replacing the existing Incurred Credit Loss (ICL) model with the Expected Credit Losses (ECL) model. The ECL model applies to all financial instruments whether they are recognized at the amortized cost or at fair value. Firms are required to estimate and recognize loan loss allowances based either on the 12-month or lifetime ECL, depending on whether there has been a significant increase in the credit risk since initial recognition. In this chapter, we first briefly explain the scope of IFRS 9 and then discuss the main characteristics of ECL model and also present mathematical models that can be used to estimate credit loan losses. The mathematical models can be based either on the capital market, discounted cash flow, or weighted losses approach. Finally, we discuss ECL disclosures that are expected to provide greater transparency on credit risk and loan loss provisions, and also present economic implications of the ECL model on firm performance.