In the last two decades, risk management has expanded its role from "in-house police" to a role concentrating on overall portfolio management.

  • 27 Mar 2000
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In the last two decades, risk management has expanded its role from "in-house police" to a role concentrating on overall portfolio management. As part of this evolution, risk management teams now focus on two primary sources of risk--price risk and operational risk--to most effectively address hedging needs as well as portfolio optimization. A critical element of this focus on portfolio optimization is the unprecedented growth in the market for financial derivatives. Derivatives are a multi-trillion dollar market at least in part because futures, swaps, options and other more exotic financial tools play a significant risk mitigation and portfolio optimization function.

Futures contracts have become an integral part of many businesses risk management hedging strategies. However, there are real overhead costs to using futures in the context of price volatility. One such overhead cost is related to the capital required to maintain futures margin accounts (Kase). Maintenance or variation margin is the cash transfer that takes place in most futures markets after each trading day (and sometimes intra-day) to mark long and short positions to the market. Unlike a forward contract that settles only when the contract matures, most futures contracts are settled daily by the payment of variation margin from the party who has lost money that day to the party who has made money. Therefore in addition to outright hedging losses, there are other non-trivial costs to consider when hedging with futures contracts in an environment of market volatility.

The following example from the natural gas industry will reveal the potential margin costs related to hedging with futures. Consider the following example:

On September 23, Trader A buys Northwest Rockies fixed-price natural gas for January 1997 delivery. To hedge the fixed price obligation related to this transaction, the risk management desk sells 15 January futures contracts at a price of $2.316.1

Figure 1 shows the characteristics of the January 1997 natural gas contract across time. The contract shows considerable price volatility beginning after approximately March 26. In fact, the contract price history appears to have two very different "price periods." The first price period (from the contract inception through approximately March 26) shows a contract with fairly stable price movements, and a mean of $1.9962. The second price period (from March 27 through the contract termination) shows a contract with extremely volatile price movements, and a mean of $2.6189. One can statistically quantify the difference between the two groups or periods of price data. A paired-samples t test reveals that these price groups are seemingly from two different, unrelated price distributions.2 Stated differently, the two sets of prices are so disparate that they seemingly are unrelated to one another. This finding suggests that this price movement was exceptional, and probably unforeseen by Trader A or the risk manager. Perhaps most importantly, this price movement would have necessitated multiple payment of margin monies.3 Specifically, losses on this "out-of-the-money" hedge would have been realized immediately in the form of margin calls, creating a negative cash flow as well as the foundation for a funding crisis.

These results are important insofar as evidence from recent financial engineering literature suggests that volatility of a time horizon of longer than a few days is difficult to forecast (Christoffersen, Diebold, Schuermann). Indeed, volatility forecastability seems to decline quickly with horizon, and seems to disappear altogether beyond horizons of 10 or 15 trading days (Christoffersen, Diebold, Schuermann). Alternative forecasting methods, such as extreme value theory, are most appropriate when considering long-term volatility, and specifically, price events in the "tails" of the distribution.

However, in the absence of accurate forecasts, limits and checks on trading activity that protect a company from market moves are important in the context of an overall risk management program. For instance, price limits represent a "pain threshold," beyond which the merits of a transaction may come into question. As portfolio optimizers, risk managers must act as independent evaluators of a company's business activity by overseeing the risk profile of each trader, as well as trading activity in total, with particular attention paid to the potential risks and rewards related to any specific transaction. Thus guidelines and limits traditionally applicable to speculative trading positions are also applicable to hedges in the context of a volatile market. This is especially true to the extent that periods of price volatility are extremely difficult to forecast. In the absence of accurate forecasting methods, how can we hedge appropriately, and how can we choose the most appropriate tool with which to hedge? These questions are important insofar as traditional risk management tools do not accurately forecast certain volatile price events.


Christoffersen, Peter F., Diebold, Francis X., Schuermann, Til "Horizon Problems and Extreme Events in Financial Risk Management." Federal Reserve Bank of New York Economic Policy Review, October 1998, volume 4 number 3, page 109 (10).

Kase, Cynthia, "Hedging without Futures." National Petroleum News, October 1993, volume 85 number 11, page 86 (1).

1 To complete this hedge, Northwest Rockies Basis would have also been sold.

2 A paired-samples t test compares the means of two groups for a single variable. The test computes the differences between the values of the two groups, and tests if the average differs from zero. The "Data Split" Point on Figure 1 represents the point at which two groups of prices were established.

3Refco Group provided historical data.

This week's Learning Curve was written byCharles Tooman, a risk management associate at an energy marketing company in Denver.

  • 27 Mar 2000

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1 JPMorgan 227,365.29 1021 8.28%
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1 BNP Paribas 27,431.07 110 7.85%
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3 JPMorgan 21,834.93 53 6.25%
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Bookrunners of all EMEA ECM Issuance

Rank Lead Manager Amount $m No of issues Share %
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2 JPMorgan 7,363.27 46 9.48%
3 Goldman Sachs 6,842.44 35 8.81%
4 Citi 5,763.97 41 7.42%
5 UBS 4,691.07 23 6.04%