Analyzing the impact of the credit crisis on ETRM

Rana Basu,Amphora, Houston

In the wake of the credit crisis, the energy trading community had to provide quick answers to questions such as "What is our exposure to Lehman?"; "What does this change in FX rate mean to our P&L?"; and "How does the cost of storage compare to shipping across the Atlantic?."

In the months that have followed, trading groups have recognized that energy trading and risk management (ETRM) systems that are well implemented can be the source of the "quick" answers that support the decision-making process. Consequently, ETRM providers are being asked to compile the "facts" in databases, and apply business rules, scenarios, simulations, and statistics to provide outputs that can aid in the "quick" communication of decision imperatives to enable effective problem solving.

A decision matrix

The ETRM holds a valuable history of transactions and their lifecycle impacts and outcomes. Engaging these "facts" through business rules and scenarios can enable users to test their hypotheses and get buy in on their views. Varying role-based information needs (i.e., marketers/traders, schedulers, risk managers, etc.) represented by these "facts" create challenges for a uniform functional set that could address both common and specialized needs.

Constructing a matrix of decision processes becomes a building block for developing such capabilities in the ETRM system. One such matrix can be to break out decision levels and functional dimensions. For example, a typical trading organization would have needs in some or all of the following decision levels:

1. Investigative – audit/accounting;
2. Trade lifecycle - transaction management and controls;
3. Trading desk management level - planning, hedging, and performance management;
4. Group/Division - Capital utilization and group performance;
5. Board – Capital allocation and risk adjusted rates of return.

Along each of the levels, the decision imperatives are broken down and looked at from the dimensions of:

1. Position and P&L controls (e.g. "what if" analysis on market prices);
2. Operations controls;
3. Risk characteristics (e.g. VaR, CFaR, PFE, Credit, etc.);
4. Accounting controls (e.g. cash flow, valuations, cost of capital, etc.);
5. Performance management (e.g. PL attribution, risk adjusted rates of return);
6. Planning and forecasting.

Accuracy, transparency lead to confidence

For any model, user confidence and transparency are the keys to successful adoption. The ETRM can build greater confidence in the decision-making process by combining the "facts" with projections based on current market conditions and forward curves or projections based on statistical simulations or distributions of possible outcomes. Such a tool could present functions tailored to each of the decision points along the position lifecycle that could be engaged easily, rapidly and accurately.

Consider crude that is available for a particular refining asset. Based on the history of transfers from the source to the asset, the 'facts' related to the costs (yields along with market curves for the period that the crude would be obtained and processed) could populate the available scenarios. Expressing costs as a percentage of the market price of the crude allows scenarios to be run for the worst, best and median cases.

Applying a set of scenarios (e.g., best/worst cost case as a percentage of market price) and business rules (e.g., valuation of the yield of the crude) would present a set of price points and shared understanding of PL performance.

Laying out the cost/value characteristics for each of these scenarios in a visual context will highlight the inherent value of this possible crude movement in a format that is easy to understand and manipulate. Hedges could be overlaid on the scenario to seek opportunity and mitigate risks. At the same time, running through statistical analysis to check against VaR or credit risk scenarios can help build the business case for the hypothesis that this crude represents a valuable business opportunity.

To build confidence in the decision process requires that the model be accurate under all scenarios and transparent in the assumptions and steps used to implement the business rules. It is this transparency feature of spreadsheets that has led to their overwhelming use in validating points of views and models. By embracing this capability while applying better business logic and validation of the "facts," ETRM systems can deliver superior results.

Implementation – from 'gut feel' to profits

The view from the trading floor steps through the execution, liquidation, settlement, and audit phases as each step becomes part of the "facts" upon which the next set of "what ifs" are tested. Building a framework on which a multitude of diverse hypotheses and assumptions can be easily tested and evaluated is the challenge for the ETRM vendor. Layering a collaboration platform with clear visualization metaphors, such as Google Wave, should enable a rapid grasp of the dimensions of the business case.

As ETRM implementations mature and greater discipline is applied into the setup of scenarios, the database becomes more valuable as it captures the history of events and their impacts on the final outcomes. Keeping the framework for decision support flexible and generic, so that users can configure their unique scenarios and viewpoints, while providing transparency and accuracy, is the Holy Grail for assuring broad user adoption.

In conclusion, the convergence of business drivers and technological capabilities is making the conditions ripe for ETRM providers to support higher levels of decision making. Implementers and integrators need to think through the needs for dynamic statistical simulation, in addition to static reporting, to create user-friendly, highly configurable, and collaborative solutions to enable better decisions.

If the foundations of a realistic set of position lifecycle events, their impacts, business rules and a flexible decision matrix are properly implemented in ETRM systems, the improvements to the quality of decision making and problem solving will be a significant value driver within the organizations that embrace them.

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