As we all know, the energy market is seasonal in nature and regularly experiences wide variations in market action, brought about largely by demand and supply imbalances. Given such market volatility, stress testing energy market participants’ exposures to price spikes and other key performance factors, like delivery quantities, makes a whole lot of sense to adequately handicap the potential for catastrophic losses and to assist in formulating a company’s risk appetite. This will mirror the current practice in the financial sector where banks routinely stress test their liquidity requirements in compliance with Dodd-Frank. How can energy companies structure an effective stress testing program within the confines of a Commodity and Trading Risk Management (CTRM) solution and do it in a way that is meaningful and attains the objectives outlined above?
 
For starters, the company must have a fundamentally sound underlying financial model that links its key risk factors to financial performance measures—typically revenue, cost, cash flow, and earnings. To establish a viable analytical foundation for credible risk assessment, care must be taken to ensure that the company’s entire business footprint is captured and reasonably represented in the quantitative model. This is a must-have, because without it, meaningful stress testing that captures the complex interaction of risk factors and informs a company’s financial vulnerabilities cannot be performed. Second, time-defined stress factors must be developed for each identified risk factor. This assumes the company already knows a relevant time period, such as a budget period, over which to perform the stress analysis.
 
Two types of stress factors would be beneficial: one for normal stress and another for extreme stress. For normal stress, one can follow the guidance provided by Standard & Poor’s (S&P) in its market and credit event liquidity adequacy study of energy companies. S&P recommends a 15 percent stretch for the first 12 months and 20 percent for all other months. For extreme stress, one can derive stress factors from analysis of daily historical movement in the risk factor. In the case of price for example, energy spot price movements for years like 1998, 2002, 2003, and 2013 could be used in conjunction with intelligence data derived from Value-at-Risk (VaR) analysis performed according to the Basel Committee on Banking Supervision’s guidance on capital adequacy standards for financial institutions. This standard stipulates a 10-Day, 99 percent VaR multiplied by a factor of three. The trick would be to imply, through backward calculations, energy price levels that yield this VaR scenario and use them in the extreme stress analysis.

Current technology trends make it possible for next-generation energy trading software systems to be powered by user-friendly solutions for effective configuration and implementation of stress testing programs, as broadly outlined above. In terms of specifics, the quantitative software must have a feature that enables stress factors to be set up on a time bucket basis, with time buckets tied to an observation date. In this way, the date composition of time buckets is automatically updated as time elapses or rolls over, which will obviate the need for burdensome daily manual stress data setup. In addition, the CTRM software must have the capability to allow different stress factors to be applied to different forward time periods. This, in effect, will capture differential inter-temporal shifts in the risk factor curve(s). Lastly, the software solution must have the capability to present stress test visualization reports in imaginative ways that effectively communicate the big picture message to company decision-makers. 

The benefits of stress testing cannot be underestimated or overlooked and energy and commodity trading platforms should not be implemented without stress testing functionality. Companies will eliminate adverse financial surprises and avoid jeopardizing the sustainability of business operations by regularly stress testing company exposures to market and credit events. By adopting stress testing analytics, a company gives itself a realistic chance to successfully prepare for—and put in place—contingency remedial plans for very low probability, company-killing events.    

About the Author:
Geoff Anato-Mensah has more than 35 years of corporate business experience, with the last fifteen focused on energy risk management. Mr. Anato-Mensah is currently Managing Director of Risk at OATI where he has directed, managed, and implemented financial valuation and risk management programs for energy companies. He has strong quantitative skills in asset valuation, credit risk modeling, market risk modeling, derivatives pricing, options trading, strategic planning, and risk policy development. Mr. Anato-Mensah has a Bachelors in Economics and a Masters in Applied Math from MIT, as well as a Master’s in Management from MIT’s Sloan School of Management. He has also been certified as an Energy Risk Professional (ERP) by the Global Association of Risk Professionals (GARP).