M&SOM Review

Consider the following events. In 2008, Dow Chemical Company imposed an unprecedented 20% increase in prices on consumers—the biggest one-time hike in prices in the company’s history till that point in time. Many sugar mills in India and Brazil, the top two sugar producing countries in the world, were forced to shut down in 2014. What is the common link in these (and many other such) examples? The severe distress these companies faced were brought on by fluctuations in commodity prices—increased energy prices in Dow’s case and depressed sugar prices in the case of the sugar mills.

Commodity price fluctuations and the attendant distress due to unmanaged uncertainty is a reality that commodity processing firms must deal with. Not surprisingly, firms often use financial instruments such as options and future contracts to manage the price uncertainty. While trading these instruments can reduce risk, it often cannot eliminate it because local commodity spot prices might be affected by firm-specific and local market-specific factors such as quality, timing, and location. Equally important, the amount of “exposure” that a firm has to commodity price uncertainty and the consequences of adverse price movements also depends on its operational decisions; for example, inventory of the commodity it holds, the capacity available to process and/or sell it in the market. Thus, efficient management of the commodity price risk requires an integrated approach that considers operational and financial hedging decisions jointly.

We explore this issue of joint operational and financial hedging decisions to manage risk in Devalkar et al. (2017). The motivating context for our problem was the soybean processing division of a large agribusiness company in India that had to deal with commodity price fluctuations on both the procurement and sales sides. The firm purchases soybeans directly from farmers at the prevailing spot market prices in each period during the soybean harvest period. The soybean is processed to produce soybean oil and soybean meal, both of which are traded through various channels with uncertain prices. While the firm uses exchange traded contracts to manage price risk, these instruments are not perfectly correlated with the spot market prices leaving the cash flows from operations uncertain. We model the multi-period operational and financial hedging decisions to manage risk and maximize the market based-value of cash flows generated from operations for such processors, where market based-value takes into account the cost of financial distress faced by the firm.

A common practice, both in the financial industry and outside, is to use measures such as value-at-risk (VaR) and conditional value-at-risk (CVaR) to measure riskiness of assets and cash flows. Measures such as VaR and CVaR quantify the value of cash flows in the “worst cases”; broadly speaking, VaR is the threshold value such that the probability of cash flows being less than this threshold is no more than a pre-defined value, say 5%, while CVaR is the expected value of cash flows conditional on the cash flow being less than or equal to the VaR. Such measures are useful when considering risk management decisions in single-period contexts, or multi-period settings which can be decomposed into single-period decisions. For example, it works well for financial institutions as these institutions can rebalance their portfolios by trading each day and ensure the riskiness of investment on a daily basis is below the prescribed threshold. However, these measures are not suitable when decisions in one period cannot be completely reversed and decisions over time are interlinked. For instance, commodity processing firms make procurement, processing, and trading decisions over multiple periods in the face of capacity constraints; for example, not being able to process all the available input even if the prices are favorable because of lack of machines, labor, and so forth. As a result, firms often carry inventory of the commodities from one period to the next, leading to a natural dependence of decisions over time.

A natural option is to adapt measures such as CVaR (or VaR) to a multi-period setting by computing the CVaR of total cash flows accumulated over the entire horizon of operations, while accounting for how the total cash flows are affected by decisions in each period and evaluate risk accordingly. Unfortunately, doing so can lead to inconsistent evaluation of risk over time. Without getting technical, consider the following hypothetical situation to understand the implications of time inconsistency. Using a time-inconsistent risk measure, a commodity processing firm may conclude that irrespective of what price the output commodity has in the next period, it is too risky to process and carry inventory of the output to the next period. Such an evaluation, in and of itself, is not a problem. However, in the next period once the price of the output commodity is known, irrespective of what the price is, the firm actually might end up concluding that it would have been beneficial to have had inventory of the output available! Such a situation seems absurd, and rightfully so. However, it is exactly such paradoxes that a time-inconsistent risk evaluation can lead to. So, in addition to dealing with joint operational and financial hedging decisions, managing risk in a dynamic setting also requires care in terms of ensuring the risk evaluation is consistent over periods and does not lead to situations such as the one previously described.

In Devalkar et al. (2017), we use an idea developed in the mathematical finance literature—conditional risk mappings—to propose a time-consistent, dynamic risk measure, constructed from single-period risk measures such as CVaR, that does not suffer this drawback and is a suitable objective function to model a firm’s mutli-period risk management problem. Using this risk measure, we characterize the optimal procurement and processing policy that maximizes the value of operational cash flows. We show that input procurement and processing decisions are governed by thresholds on the input inventory. From a computational stand-point, these policies are quite tractable and can be implemented in practice.

Firms often use heuristics such as limits on the maximum and/or minimum amount of inventory of the input and output commodity they will carry in any period as a way of managing risk. Such heuristics can potentially ignore, or underestimate the amount of risk the firm is exposed to. Our results show that ignoring risk or overestimating it, by using the optimal risk-neutral or myopic policy, respectively, can lead to significant loss (up to 25%) in market-based value for firms. We also find that while trading financial instruments may not eliminate risk, they help to reduce the variability of total cash flows considerably and reduce the chances of significant negative cash flows being realized in any given period. The reduced uncertainty in cash flows from operations can help the firm improve its forecast of cash flows in the future and better plan its fixed investments. 

Reference

Devalkar SK, Anupindi R, Sinha A (2017) Dynamic risk management of commodity operations: Model and analysis. Manufacturing Service Operations Management 20(2):317–332.

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