Energy Credit Risk Managers’ Forum - April 30 2002 - London

Challenges for Credit Risk Management in the Energy Sector 
(Ron Wells)

Credit Risk Managers active in the oil, gas and power trading arena attended a forum in London recently. Three areas were discussed: credit analysis, credit risk mitigation tools, and credit risk measurement systems and techniques. 

Credit Analysis

Quality trade credit analysis can provide a competitive advantage while protecting a Seller from suffering catastrophic losses, due to cash flow interruptions (late payments) or Buyer bankruptcy (actual loss of revenue).

Analysis aims to determine whether a customer (a) ‘can pay’ and (b) ‘will pay’; bearing in mind that “Credit is an Option to default”.

The well known, serious limitations of traditional credit analysis methods have been sharply brought into focus by the Enron event. That, and other similar shocks, led Business Week to remark; “Excessive Pay, Weak Leadership, Corrupt Analysts, Complacent Boards, Questionable Accounting, Enough Already!”.¹  

Traditional credit analysis has a heavy reliance on (a) financial ratio analysis and annual report based reviews, (b) Debt Ratings provided by the big three Agencies, and (c) mathematical models that are based on assumptions and extrapolate the past. This approach to credit analysis will have to be re-invented if it is to be relevant in the 21^st Century.

The forum was challenged to begin the process of re-invention, starting from the premise that there is a lot of empirical evidence to suggest companies most often fail due either to management dishonesty or management incompetence.

A study by von Stein and Ziegler (1984) ² “attempted to identify the characteristics and concrete behavioural indications that distinguish failed firms from solvent ones. The qualities found to set failed company management apart (from those in the non-failed group) were the following:

·        Being out of touch with reality.

·        Large technical knowledge but poor commercial control.

·        Great talents in salesmanship.

·        Strong-willed.

·        Sumptuous living and unreasonable withdrawals (of cash from the business).

·        Excessive risk-taking.

The management of solvent companies were found to be more homogeneous than (those of) failed companies and seldom showed a lack of consciousness of reality. The authors recommend all three components of analysis (balance sheet, account behaviour and management) be pursued to assess a company.”

“Knight (1979) ² analysed the records of a large number of small business failures as well as conducting interviews with key persons involved. Knight (found that) some type of managerial incompetence accounts for almost all failures.”

Traditional analysis does not generally uncover these key traits successfully. The result is a lot of reliance being placed on subjective evaluation by individual analysts. Analysts generally reach an intuitive conclusion after weighing all the evidence.

Several forum attendees commented that personal site visits and interviews with target company management provide the best sources of subjective information. This should be combined with 'internally generated input' from sales, marketing, trading personnel, operations staff, sales accounting staff, etc. To this should be added ‘market input’ from competitors, suppliers and credit research agencies.

This process could be summarised as 'corporate knowledge management'. It is said that in the 21^st Century; 'Knowledge Shared is Power'; but this is a concept that is difficult to popularise. ³  

It was generally agreed that in-depth analysis of financial statements – such as that undertaken in intensive financial analysis courses – is seldom undertaken in practice. Hence another suggestion was that Annual and Quarterly Reports should be subjected to in-depth forensic analysis by a specialised neutral agency. Such analysis could then form an input to each Seller’s individual in-house credit analysis process. 

An alternative method of credit analysis involves tracking target company stock or share price movements and credit spreads. 

Some analysts believe that ‘the market’ is so sensitive to the present and future prospects of companies that share prices and spreads are leading-indicators of changes in risk profiles. However, “it is … possible for misleading accounts and inadequate auditing to promote an inappropriate share price (therefore) … share prices are unreliable indicators of corporate value…. One of the clearest lessons of recent years is that there is no correlation between the value of a company and its share price, creating the possibility for individuals to make massive gains or losses. All too often the directors make the gains and the public sustains the losses…. The financial health and value of a company are a function of financial management (not a function of its share price or credit spread) …” ⁴

Most traditional approaches, and many of the alternate solutions suggested for credit analysis, assume that customers are publicly traded debt issuers. ‘How can credit analysts deal with those businesses that do not publish accounts and do not issue traded debt?’ Such entities, particularly in the global context, are in the majority. In these cases use of a Balanced Scorecard, employing non-financial factors (cross referenced to verify accuracy), could be considered. At least this would bring some consistency to the highly personal and seemingly random research and analysis processes widely adopted today. ⁵

Credit Risk Mitigation Tools ^A

There are four major categories of credit risk mitigation tools: Guarantees, Trade Credit Insurance, Collateral and Netting.

Guarantees/Letters of Credit/Credit Default Swaps:

Guarantees may be provided by either third parties or a parent company. The third party is traditionally a bank. Guarantees by a Parent Company are becoming increasingly preferred.

Parent Company Guarantees (PCGs) are usually cost free and are easy for a counterparty to arrange. However, they are ancillary to the prime contract. Hence lawyers could attack both the principal contract and the guarantee itself, if the PCG were called. Careful drafting is therefore essential.

Letters of Credit (LCs) and Third Party Guarantees are expensive to obtain and to process so they are less common, particularly in relation to long term supply contracts.

Credit Default Swaps (CDSs) - also known as Credit Derivatives - are another form of risk transfer to a third party.

Trade Credit Insurance:

Insurance policies, covering trade credit, may contain a good deal of ‘wriggle-room’ in their interpretation and impose additional obligations (e.g. the duty of ‘utmost good faith’ disclosure of relevant facts) on the insured.

On the positive side they can be written to cover the unfortunate coincidence of a counterparty default with a price peak. On the other hand, the negative cash flow effect of compensation pay-out waiting periods must be considered.

Collateral and Security:

Collateral generally takes one of two possible forms, the first being a Security Interest in an asset. This would usually be a pledge, charge, mortgage, lien or hypothecation. We should be wary (a) of the possible existence of negative pledges given by the counterparty, (b) of perfection requirements for security interests and (c) of the possible effects of fluctuations in the pledged asset’s value.

The second form of collateral is the Outright Transfer of ownership of an asset. This creates an obligation on the transferee to redeliver assets of the same type (but not necessarily exactly the same assets as originally delivered) upon discharge of the obligations of the transferor under the main contract. Default results in the value of the transferred assets being set-off against the transferor's obligations under the contract. In respect of mutual mark-to-market (MTM) collateral cover agreements (say the transfer of marketable securities) we should guard against finding ourselves in the position where we must transfer our securities to a less creditworthy counterparty because of a change in the underlying transaction’s value. This would happen when a mutual transaction moves ‘out of the money’. Should the position later reverse, and should the counterparty subsequently be tardy in returning collateral and/or become bankrupt before re-balancing the position, we could lose the profit on the trade and the excess collateral.

Netting:

Netting (or 'Set-off') involves effectively using our own perceived credit standing to reduce our exposure to the counterparty. There are four generic types of Netting, viz:

·          Settlement Netting that is effective only in respect of items of the same kind due on the same day. It takes effect only on the settlement day.

·          Novation Netting, in terms of which only like classes of transaction with the same settlement dates may be set-off against each other. It takes effect each time a subsequent transaction is entered into. Whilst risk reducing, it does not address netting on the occurrence of a default by a party.

·          Close-out Netting which stipulates that if there is a default by either party all mutual trades will be closed-out and set-off immediately. This is the most popular form of Netting.

·          Multi-lateral Netting which, outside of special clearing house systems, in practice is not generally effective in most jurisdictions.

Netting is information systems and people intensive, hence costly. It also carries considerable Documentation Risk. When effecting a Netting close-out, it is extremely difficult to ensure all transactions are valued correctly and in real time. Hence it is easy to miss some small losses or windfall gains on a close-out.

Even in relation to negotiations based on ISDA framework documentation, there is a general lack of standardisation of requirements. The result is that the negotiation of Netting documentation proves time-consuming and arduous.

Credit Risk Measurement Systems and Techniques ^B

Forecasting Potential Term Contract Credit Exposure – The Forward Price Curve:

Scenario Based Stress Testing is probably the most useful method available to generate reliable forward price curves in relation to the gas and power markets. This is the case because these markets have little or no historic data in hand so, for example, using purely statistical methods to evaluate credit risk in relation to a twenty year contract produces results that cannot easily be explained or understood.

Forward price curve generation can be accomplished using several techniques. For example historical simulation allows an exact set of past price movements to be reapplied to the current portfolio and Monte Carlo simulation allows multiple random variations to be applied to starting scenarios. This yields a statistical amount of risk that can be quantified by a confidence level (e.g. 90% or 95% normally) for a period of time in the future. It can therefore be said with a 95% level of confidence (95 days out of 100) that the customer exposure will not increase more than the forecast number.

However, historical and Monte Carlo simulation are all based on the premise that the past is indicative of the future. What happens if it is not? This is where scenario based simulation can effectively be applied. Here an “expert” can decide on scenarios that bear some or no resemblance to the past, and apply each scenario to the customer portfolio. These scenarios can reflect world disasters or particular world economic events that may never have occurred before. In addition, scenarios can be generated for selected time frames in the future.

Credit Value at Risk (Credit VaR):

The subject of ‘pre-deal conclusion’ checks and the calculation of Value at Risk (VaR) for proposed transactions was considered.

“In its most simple form credit risk can be described as accounts receivable (invoiced but not received), plus current mark-to-market (MTM) plus maximum increase in MTM in the future (Credit VaR). The next stage is to adjust this exposure by the amount of collateral held from the counterparty and then incorporate the default probability of the counterparty. Finally, loss recovery rates should be factored into the equation.

In summary we have the formula:

CE = (AR + MTM + PFE – collateral held – probable recovery amount) x default probability;

where CE = credit exposure, AR = accounts receivable,  
MTM = current mark-to-market, PFE = potential future exposure.” ⁶

Any system producing a VaR number for an energy trader, has to be fast enough to process the requisite large amount of data without delaying a decision. Delays could cause lost opportunities if otherwise desirable transactions cannot be concluded. Credit managers have to be able to understand VaR numbers produced, and able to explain and/or defend them.

The main distinction between VaR and CVaR is the time horizon over which the risk measure is made. Typically a VaR figure shows the amount of profit or loss that can be expected (with a set confidence level) over a period of 1 day or 30 days. CVaR requires an estimation of the rise in credit exposure over the whole remaining life of each trade.

Anecdotal evidence among forum participants indicated that few organisations are utilising VaR or – more pertinently – Credit Value at Risk (CVaR), as measures of potential risk inherent in transactions that are exposed to market price fluctuations.

Most energy companies still manage credit exposure on the basis of a mix of (a) historical activity (highest credit), (b) expected trading activity in the short-term, (c) current price levels, and (d) account receivable balances controlled against estimated, needs-based credit limits.

In respect of Netting agreements, particularly those requiring margining or partial collateralisation, it was mentioned that often counterparties disagree the quantum of exposure. Sometimes they even disagree the direction of the exposure; that is they even disagree who owes whom at a particular point in time. Hence the use of accurate real-time and ever more efficient systems to calculate and monitor exposures is a pressing need.
 

Conclusions

Credit Risk Management in the Energy Sector is particularly challenging, given the amounts involved and related price volatility. Several entities active in the sector have no history of credit risk management, since the sector has only relatively recently been opened to competition. Thus many credit executives are on a steep learning curve in difficult conditions. Strengthening the skills of those involved will contribute enormously to reducing systemic risk within the industry. More meetings of the forum are planned.

© Copyright 2002  R K Wells