Energy price volatility may require new solutions

The extreme price and supply volatility in global energy markets raises the prospect that the traditional risk management exposures and limits inherent in energy trading and risk management (ETRM) software may not be adequate. Basic principles for position and P&L swing limits can sometimes be moved to deal with short-term changes. There is an emerging consensus that ETRM systems should avoid setting static limits that may be restrictive on a trading floor’s ability to squeeze out margin when opportunity presents itself or to be creative in how the market is being approached.

Exposing financial risk when the P&L is flat is a major focus in this change. To accomplish it effectively will require correlating traders’ cash flow, credit, delivery, currency, and tolerance exposure to changes in pricing, quantity of movements, or inventory.

The challenge for ETRM systems is to provide actionable intelligence in the event of a trading spike that breaches basic trading limits by a combination of detailed P&L attribution and splitting out the components of the model used to calculate the measure. A dynamic set of limits that react to changes in market price levels, changes to volatilities, and other factors such as currency rates, interest rates, or credit ratings potentially provides the most acceptable solution.

STP and cash management

This requirement takes ETRM beyond the already complex parameters of straight-through processing (STP) trading systems that contain automated workflows with checks and balances that are custom-designed for each trading company’s front-, middle- and back-office functional requirements. Such systems automatically provide audit trails, time stamps, and peer reviews in a seamlessly documented format that reduces the compliance burden and improves profit potential. In doing so, they become central to ERTM strategy by meeting the needs of heightened regulatory scrutiny and intense market demands.

Additional multi-platform capabilities include detailed invoice generation and tracking, intelligent support of prior period adjustments, and the capture and management of complex transportation and storage entitlements.

Common STP interactions include sending settlement and month-end accounting records to the GL system and feeding payment information back into the ETRM system from GL. Movement actuals and inventory levels from terminals, racks, tankage, or inspections at the ports are generally automated as part of calculations. Document creation such as contracts, confirms, nomination documents, invoices, and LOIs can also be automated.

However, events such as counterparty bankruptcy or a system-wide problem such as the credit crunch do not lend themselves to easy ETRM modeling. When a counterparty’s credit rating drops or the rating of the bank providing the securitization for a counterparty drops, does that require a change in the limits on them or closer scrutiny until the exposure is liquidated?

Ultimately, it can be argued that a good ETRM implementation should provide actionable intelligence in the event of a trading spike that breaches basic trading limits by a combination of detailed P&L attribution and exposing the model internals used to calculate the P&L measure. Using a dynamic set of limits that react to credit ratings as well as to changes in market price levels and currency rates is essential under today’s trading conditions.

Counterparty risk management

Trading volatility takes the challenge to the next level. ETRM systems commonly support position limits, P&L swing limits, VaR limits, credit limits, PFE (potential future exposure), or credit VaR limits and will notify on breaches of these limits. However, the business processes to manage these limits may be too slow for today’s marketplace.

When oil prices move 10% to 20% in one day, setting static limits on outright or spread positions may restrict a trading floor’s ability to adopt creative trading strategies that squeeze out margin. Statistical models that rely largely on volatility to represent the encapsulated risk due to a variety of market factors may be insufficient.

One school of thought is that all political, delivery, and price risk is effectively captured with the single measure of volatility and that the distributions spread over the time to liquidation present an effective way to capture the risk. But there is a difference in the risk represented by a million-dollar VaR on a position on, for example, the BFOE market versus the Nigerian crude market.

Rapid changes in the commodity price of oil or the credit rating of counterparties, compared to, as an example, the 25 days needed to move a shipload of oil across the Atlantic Ocean, requires that trading floors have the ability to rapidly react to market changes without giving up the controls to regulate the operations of asset transactions.

Country political risk factors, the quality of risk in a counterparty’s credit rating, and the rating of the bank offering credit security for a movement are examples of sophisticated intelligence factors that need to be accounted for in the ETRM model, just like real-time prices and actuals for position and P&L updates.

Margin call management

Margin call management is another growing ETRM requirement, particularly given trading developments since mid-2008. The financial sector has been hit by regulatory limits on short selling in the stock markets and limits on credit extended to businesses. The impact of current events on the energy trading world makes it essential that risk managers proactively use ETRM systems to engage limits around their trading practices that can effectively handle margin calls.

There are two types of relevant concerns. For cleared instruments like exchange futures and options, and clearport swaps, there is an initial margin requirement for all participants based on their volume and then a variation margin based on the position and how the market moved each day.

For OTC and bilateral trades, counterparties can call margin as security for credit that has been extended. These margin calls are to make credit available to do the next trades between counterparties when credit limits are reached or breached. Exchanges and clearing houses use complicated margin calculation algorithms to calculate how much cash needs to be posted by a trading entity — the Nymex’s SPAN system is one approach. ETRM systems will increasingly be expected to integrate with these margin calculation formulas as part of managing overall trading risks and limits.

Sophistication and flexibility

As market players adopt more sophisticated risk management strategies, ETRM systems must similarly adapt through tighter trading and technology integration. Given the price volatility and high trading volume in the liquid hydrocarbons market, both traders and ETRM providers must continue to adapt to new realities.

It is essential that this involve the ability to accurately attribute P&L changes to changes in curves and their impact on trade cost or trade value, as well as P&L changes due to delivered specifications, outturn losses, timing or other factors (changes in additional costs, changes due to new trades, changes in hedges or pricing events, and so on). That requires balancing trading, risk, scheduling, accounting, and credit, and the specific requirements of each of those roles from a position, P&L or cost basis.

The demands of today’s energy trading risk management requirements cannot be underestimated. Much was learned in the unwinding of Enron’s trading positions after their bankruptcy. Still, there is no standard methodology or measure that would predict the impacts of every potential market disaster.

The most practical response is flexibility from a sophisticated ETRM system that can provide actionable intelligence about what has caused the breach of trading limits through a combination of detailed P&L attribution, counterparty risk assessment, and margin call management. Using a dynamic set of limits with the proper combination of control and flexibility is the ETRM model for today and tomorrow. OGFJ

About the author

Rana Basu is vice president of the Center of Expertise at TradeCapture Inc., a global provider of commodity trading and transaction management solutions since 1997. TradeCapture provides enterprise-wide trading and risk management systems for multi-commodity markets worldwide. The Houston-based firm has offices in London, Rome, and Hyderabad, India.