Seven Types of Risk Analysis Every Energy Trader Should Know
For most energy traders, thinking about risk is the exact opposite of fun. Dwelling on potential bad results is about as enjoyable as enduring an IRS audit; but understanding how to accurately assess your trading risk can help ensure that you never have to go through that pain in real life. Energy traders should care about market risk, credit risk, and regulatory risk, and management should ensure that appropriate risk control measures are in place to rein in trader behavior. With these goals in mind, here are seven types of risk analysis executive management must demand and every trader needs to know to protect against trading losses, while sparing employers the ultimate business heartache—financial ruin.
By assuming traders care about the odds of a really big loss, Value-at-Risk (VaR) answers the question, “What is my worst-case scenario?” or “How much could I lose if things go really bad with price movements?” VaR is a risk metric used to statistically estimate the likelihood that a trading portfolio will incur a maximum potential loss, within a specified period, and at given probability confidence level. The “specified period” is usually the period over which a trader can liquidate or offset a losing position and 95% tends to be the primary confidence level used in practice.
There are three primary methods to estimate VaR: Analytical VaR, Historical Simulation VaR, and Monte Carlo VaR. At a practical level, these are characterized by the tradeoff between accuracy and computational speed. Analytical VaR is the fastest to compute, but the least accurate. It assumes the price risk factor is lognormally distributed, but market experience tells us energy prices have fat tails—meaning more extreme price levels are actually realized than predicted by the normal distribution. Monte Carlo VaR is by far the slowest to compute, but it is the most accurate. Historical VaR is somewhere in between with respect to this tradeoff. Ultimately, the size, complexity, and length of the trading portfolio dictate computational speed. In line with best practices, it makes sense to calculate analytical VaR on a daily basis, while calculating Monte Carlo VaR on a biweekly or monthly basis (for comparison purposes), and using any resulting variances as intelligence data to adjust the daily analytical VaR results.
Mark-to-Market (MtM) refers to a valuation process where open positions are marked to forward market prices, thereby establishing the unrealized profit and loss potential of the opportunity created by the trader. Used in conjunction with a VaR measurement, it allows managers to determine the risk-return characteristics of trader positions. This provides a viable means to objectively assess trader performance and to allocate scarce trading capital among trading desks.
3. Counterparty Credit Exposure
Recent low energy prices have worsened the credit risk exposures of energy market participants, and it is prudent for trading partners to understand, control, and mitigate the credit risks coming from market activity. Toward that end, it is customary to estimate counterparty current credit exposure, subject to netting provisions, on a daily basis and track progression on counterparty credit limit usage for risk control purposes.
Due to market volatility, it is best practice to complement counterparty current credit exposure estimates with corresponding Potential Future Exposure (PFE) estimates, in order to arrive at aggregate credit exposure estimates. PFE is an estimate of counterparty credit exposure under changing market conditions captured by price volatility. It is defined as the maximum future exposure that results from change in market prices over various future periods. It is often estimated using Monte Carlo simulation methods, which are complex in nature, but can be estimated as well with simpler, well-designed Analytical VaR methods that cover several future time horizons with their corresponding forward price scenarios.
4. Counterparty Collateral Requirements
As a credit risk mitigation tool for bilateral agreements, counterparty collateral requirements must be assessed in a bi-directional manner, so as to adequately and simultaneously track both inbound and outbound counterparty margin calls. For purposes of the collateral calculations, composition of the recognized collateral must be known upfront—cash, letters of credit, guarantees, other collateral types, or any combination thereof. Also, care must be taken to ensure that, at the time of the analysis, only unused portions of the collateral are included in the calculations. Counterparty exposures, net of outstanding collateral posted and/or collateral received, are compared against ratings-based threshold amounts to estimate potential shortfall amounts. Any shortfall amount greater than the minimum transfer amount triggers a margin call event, and the exact amount of the margin call is determined by the rounding amount. These minimum transfer and rounding amounts are usually specified under the Credit Support Annex (CSA) provisions of the umbrella ISDA agreement.
5. Cost of Credit
From a risk management perspective, traders have a call option on a company’s assets. Under this view, left unchecked, traders have a tendency to take on maximum risks to maximize the upside potential of opportunities, while knowing there is little or no discernible downside personal risk. To somewhat subvert this trader mentality, it will be prudent for management to assess daily credit charges on trades put on by traders to recoup the cost of credit. These daily credit charges can be calculated as a function of trade MtM net of posted trade collateral and the counterparty’s credit spread, measured in basis points. The credit spread must be adjusted to reflect the underlying time horizon, which is “daily” in this narrative.
6. Hedge Effectiveness Test
In the wake of corporate financial disasters over the past decade, government regulatory bodies have stepped up their game and introduced new measures to protect investors. FERC wants ISO/RTO organizations to certify the risk management capabilities of their market participants. The Financial Accounting Standards Board (FASB) requires more transparency and disclosure in the use of derivatives in hedging applications, while the Dodd-Frank Act proposes all OTC trades be centrally cleared with exemptions only for those trades that mitigate commercial risk. At this time, it’s unclear which of these requirements, if any, will be rolled back under the Trump administration.
That said, notwithstanding the importance of the hedge effectiveness test that establishes a company’s ability to elect hedge accounting for reporting financials (FAS 133) and to claim Dodd-Frank exemption from clearing of OTC trades (Title VII), company executives need to know whether hedges are indeed producing the intended or desired economic results. The simple reason is that these hedging results (or lack thereof) materially impact company valuations. Three main methods are currently used to test hedge effectiveness: the dollar offset method, regression method, and variance reduction method. Of the three methods, the dollar offset method is the simplest to implement.
7. Stress Testing
The importance of stress testing a company’s ongoing financial performance cannot be overemphasized. It’s always wise to complement MtM, VaR, and Credit analyses with rigorous stress testing. Stress testing market events involves assessing the impact of significant changes in prices, volatilities, and correlations on MtM and VaR measures. On the other hand, stress testing credit events means assessing the impact of credit ratings changes (such as a downgrade) on counterparty credit exposures and collateral requirements. To the extent that MtM is often included in credit exposure estimates, it makes sense to conduct credit stress testing based simultaneously on market and credit events, in order to get a more complete picture of the state of counterparty credit exposure under those conditions.
In today’s energy markets, successfully managing the risks of energy trading is crucial to achieving optimal results. Trading firms need live data that deliver continuous position and P&L updates, so traders will always have the most up-to-date picture of their market exposure. We have touched on some key energy risk analysis types that have significance for effective corporate governance. Software solutions like OATI’s webCTRM risk management modules help implement these risk analyses. Adhering to industry best practices, such analyses should be conducted under the framework of a company’s risk policy and well-defined business processes that provide the requisite support for decision-making. OATI webCTRM keeps the front office focused on making money the right way, while keeping the middle office balanced and the back office compliant.
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 Bachelor’s Degree in Economics and a Masters in Applied Math from MIT, as well as a Masters 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).