Under both International Financial Reporting Standards (IFRS) and US Generally Accepted Accounting Principles (US GAAP) there is a wide range of financial instruments that are measured at fair value. Fair value measurement is the process of reporting positions based on the current market price or using other relevant information to determine a proxy to such prices.

Traditionally, fair valuation has had differing interpretations based upon the usage and context. For the purpose of financial statement reporting, IFRS 13 defines fair value as “the price that would be received to sell an asset, or paid to transfer a liability in an orderly market transaction at the measurement date”. Some fair value measurements are more robust than other fair value measurements. The IFRS 13 fair value hierarchy categorises price inputs into three levels – Levels 1, 2 and 3. While level 1 and level 2 inputs include mainly observable market information, level 3 includes unobservable inputs that are significant to the fair value outcome, essentially management's estimates, assumptions and inputs that cannot be corroborated with observable market data. Therefore, in the absence of high-quality market data, a trading entity may not be able to estimate the fair values of the trading assets as reliably.

In recent years, companies have been applying fair value measures to increasingly complex and less liquid financial instruments. This is especially the case in the area of commodity trading. In this sector there have recently been several cases of companies realising much lower market values for their assets, compared to their estimated fair values. These instances have highlighted the shortcomings of fair value accounting for "Level 3" financial assets and also brought into question the quality of financial instruments disclosures. 

This matter is being discussed on investor calls with increased regularity. Lenders to the commodity trading sector have also noted that it is not straightforward to understand how easily certain assets can be converted into cash given the lack of market price data, and that this is exacerbated during times of market stress.

In general quoted prices are available for short term contracts in most commodities.

However, for fair valuing or “marking-to-market” (MTM) long term contracts, which have now become commonplace, quoted prices are generally not available. This necessitates the use of a valuation model. A key input to such a model is estimated commodity prices for the future periods and from which the “forward curves” are derived, often 15-20 years into the future. It is important to note that while the short end of the curve reflects an active market, modelling the far end of the curve is based upon assumptions and inputs.

Episodes of market stress bring to the fore challenges in estimating the fair value of trading portfolios. These are often a combination of lack of market liquidity, complex derivatives and a reliance on bespoke valuation models. Most trading companies that have long term purchase and supply contracts use proprietary models to fair value commodity trading assets and liabilities on their balance sheets. It is important to note that in the absence of readily available market data, a trader's biases and expectations can influence the modelling process. Since every trading entity is likely to have a different expectation about future commodity prices, forward prices for a particular commodity can differ significantly between entities. These limitations have led critics to label Level 3 MTM as mark-to-make-believe. Hence, clear disclosure of valuation assumptions becomes important, not only for the purpose of comparability, but also to get meaningful insight into trading business models.

Commodities are among the most volatile of asset classes, and therefore it is necessary for management to challenge the robustness of valuation assumptions. Whether commodity risks are reasonably estimated depends upon how reliably the market values of the commodities can be determined. Uncertainty in risk quantification is generally higher for products that are less standardised, traded less often or their pricing more complex to understand. Companies should proactively engage in conversation around valuation approaches, quality of disclosures on pricing assumptions and consequent risks to the trading portfolios. These factors can help us avoid past mistakes.

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