Energy Credit Risk Managers’ Forum - April 30 2002 - London
Challenges for Credit Risk Management in the Energy Sector
|A credit spread is the margin demanded by risk-takers over and above the risk free price of an investment. Usually the risk-free price is taken to be the yield on US Government Treasury Bonds in the case of US$ investments, for example. If risk-takers demand a return in excess of the US Treasuries rate for assuming a particular risk, that excess is the ‘credit spread’, or an indication of the market participants’ overall perception of the relative riskiness of the asset.|
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
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credit analysts deal with those businesses that do not publish accounts and do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
are four major categories of credit risk mitigation
tools: Guarantees, Trade Credit Insurance, Collateral and Netting.
Guarantees/Letters of Credit/Credit Default Swaps:
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
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
Credit Default Swaps (CDSs)
- also known as Credit Derivatives - are another form of risk transfer to a
policies, covering trade credit, may
contain a good deal of ‘wriggle-room’ in their interpretation and
obligations (e.g. the duty of ‘utmost good
disclosure of relevant facts)
on the insured.
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.
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)
requirements for security interests and (c) of
the possible effects of fluctuations in the pledged
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
(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.
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.
outside of special clearing house systems, in
practice is not
generally effective in
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
and in real time. Hence it is easy to miss some
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.
Potential Term Contract Credit Exposure – The Forward Price Curve:
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.
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.
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.
subject of ‘pre-deal conclusion’ checks and the calculation of Value
at Risk (VaR) for proposed transactions was considered.
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
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.
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.
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.
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
– European Edition / May 6, 2002 [ISSN 0007-7135].
International Survey of Business Failure
– Edward I Altman and Paul Narayanan – Financial Markets,
Institutions & Instruments. V 6, N 2, May 1997.
© 1997 New York University Salomon Center.
comments on Knowledge
Management appear on .
– ‘Shred or Dead? The fall of Enron has
shaken public faith in the accountancy profession’. Financial Management April
2002 [ISSN 1471-9185] the professional magazine of the Chartered
Institute of Management Accountants (
How one Company Developed its own Trade Credit
Mary S Schaeffer, IOMA January 2001, published at http://www.barrettwells.co.uk/credit_scorecard.html.
– Credit Risk Management White Paper ().
Product Strategy Director of Raft
International plc (www.raftinternational.com),
led the discussion on credit
risk measurement systems and techniques, and edited the related part of