0.5
Probability
0.4
0.3
0.2
0.1
0.0
−5
−4
−3
−2
−1
0Data
1
2
3
4
5
Figure 12.4 Normal probability density function.
The normal probability function is not mathematically straightforward – the equation for the graph is in fact:
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Normal probability functionprobability density =
1x2√22π e
This function is important here because it is widely used in VaRcalculations.Probability distributionThe cumulative probability distribution of a series of numbers is theprobability that any one of them will be no greater than a particularnumber. Thus the probability distribution for throwing the die is:probability 16 that the number thrown will be 1probability 26 that the number thrown will be 1 or 2probability 36 that the number thrown will be 1, 2 or 3probability 46 that the number thrown will be 1, 2, 3 or 4probability 56 that the number thrown will be 1, 2, 3, 4 or 5probability 66 that the number thrown will be 1, 2, 3, 4, 5 or 6The normal probability function shown in Figure 12.4 has thecorresponding probability distribution shown in Figure 12.5. If thestandard deviation were greater, so that the curve in Figure 12.4was shorter and fatter, then the curve in Figure 12.5 would also bemore stretched out horizontally. If the standard deviation were less,it would be compressed horizontally.1.11.0Cumulative probability
0.90.80.70.60.50.40.30.20.10.0
−5
−4
−3
−2
−1
0
1
2
3
Data
Figure 12.5 Normal cumulative probability distribution.
4
5
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With this normal probability distribution, there is a probabilityof around 84.1% that the outcome will be less than or equal to 1standard deviation higher than the mean; a probability of around97.7% that the outcome will be less than or equal to 2 standarddeviations higher than the mean; a probability of around 99.9% thatthe outcome will be less than or equal to 3 standard deviations higherthan the mean, and so on.In each of these examples, we are considering the probability of anynumber being in a range which goes down to minus inﬁnity but goesup only to a certain deﬁned level (1, 2, 3 and so on), leaving a tailexcluded at the top. As the normal curve is symmetrical, we couldequally well consider a range which goes up to plus inﬁnity but goesdown only to a certain deﬁned level (−1, −2, −3 and so on), leaving atail excluded at the bottom. This one-tailed measurement is what isgenerally used in risk calculations, because a risk manager is worriedabout how much loss the bank might make, rather than how muchproﬁt.We have now reached the important point. If we assume thatthe probability distribution for a particular price is normal and canestimate the standard deviation, it is possible to calculate what isthe probability of any particular number being less than −1, or lessthan −2 etc. A similar calculation can also be made based on theassumption of a probability distribution other than a normal one. Inﬁnancial risk management, this translates into being able to estimatethe probability of a loss being worse than a particular level.
Overview of VaRIn order to control market risk, the bank needs to be in a position tomeasure it. In line with EU and BIS regulations, banks have thereforeincreasingly developed computer models to assess market risk, witha view to deriving an overall measurement of the bank’s risk – valueat risk (VaR) or earnings at risk.VaR is the maximum potential loss which the bank might sufferon its positions over a given time period, estimated within a givenconﬁdence level. Suppose, for example, that the bank has a VaRof USD 10 million with a time horizon of 30 days and a conﬁdence level of 95%. This means that if the bank’s positions remainunchanged, there is a 95% probability that its total losses will notexceed USD 10 million over the next 30 days – or to put it anotherway, a 5% probability that the total losses will exceed this amount.The total expected loss would be smaller (greater) if the time horizonwere less (more) than 30 days and smaller (greater) if the conﬁdencelevel were lower (higher) than 95%.
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VaR is:the maximum potential loss which the bank might sufferover a given time periodestimated within a given conﬁdence level.In arriving at this summary number of USD 10 million, the bankmust make certain assumptions. Firstly, there is an assumptionabout the probability distribution of price movements for each instrument in which the bank has a position – what is the pattern of pricemovement and are the price changes up and down expected to belarge or small? Mathematically, this means: What is the probabilitydistribution and what is its standard deviation? Typically, banks usean assumption that asset returns show a normal probability distribution as the basis for risk assessment, and the standard deviationused will be based on historic movements.Secondly, the bank will take into account correlation betweendifferent instruments. Suppose, for example, that a particular dollarbased bank is long of 1 million euros and short of an equivalentamount of Swiss francs. Because there is some correlation betweeneuros and Swiss francs, the bank is less vulnerable to market movements than if it were only long of 1 million euros without any offsettingposition. In this particular case, the greater the correlation, the lessrisky the position. If the two positions were both long (rather thanone long and one short), then the riskiness of the total position wouldinstead increase with higher correlation, rather than decrease. Ingeneral, a portfolio of positions is less risky if it is more diversiﬁed.
Variance/covariance, Monte Carlo and historicapproachesThere are 3 widely used approaches to calculating VaR, each withadvantages and disadvantages.Variance/covarianceThe approach considered so far for estimating VaR is essentially thevariance/covariance approach. This approach involves the following:• Assume a probability distribution – generally taken to be thenormal distribution.• Assume a variance for each ﬁnancial instrument and a covariancebetween each pair of different ﬁnancial instruments – generallybased on historic data.• Because it is impractical (and gives rise to mathematical difﬁculties) to use variance and covariance data for each instrument in a
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portfolio, only a selection of standard instruments is used. Eachinstrument in the actual portfolio is then expressed in terms of anequivalent number of these standard instruments. For example,a foreign exchange forward can be expressed in terms of a spotposition and two loans or deposits; an interest rate-based securitycan be broken down to individual cashﬂows and expressed asan equivalent strip of zero-coupon instruments. This process isknown as mapping, and vastly reduces the number of instrumentsto be considered.• Calculate the VAR.The variance/covariance approach has the advantage of being relatively straightforward. A matrix of variances and covariances for thestandard instruments can be obtained from third parties, such as JPMorgan’s Riskmetrics. The properties of the normal distribution arewell known (a 95% conﬁdence level, for example, implies a VaR of 1.65standard deviations below the mean) and allow for relatively simplerisk assessment. If the standard deviation is doubled, for example,the VaR is doubled as well; if the time horizon is doubled, the VaR ismultiplied by the square root of 2.A disadvantage is that the relationship between the VaR and theunderlying risk variables is linear (i.e., they are directly proportional). For example, it assumes that if yields move by a certainamount, security prices will move in direct proportion. Similarly,it assumes that option values move in direct proportion to underlying prices. In practice, this is not the case, so that for an optionportfolio, the variance/covariance approach is generally unsatisfactory.Monte Carlo simulationAn alternative is to use a Monte Carlo simulation. This involvesgenerating a very large number of random prices, applying these tothe current portfolio of instruments and measuring the net effect:• For each instrument or set of instruments, choose a probabilitydistribution and pricing model.• Assume variances and covariances.• Generate a random set of data (changes in prices, rates, optionvolatilities). Although this computer-generated series of prices israndom, it must still ﬁt an assumed probability distribution; if thecurrent price of an asset is 100, for example, a move to 50 is muchless likely than a move to 99.• Apply these data to the portfolio and calculate the resulting proﬁtor loss.
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• Repeat this process many times, to generate a large number ofpossible proﬁt or loss outcomes.• List these results in order. For a 95% conﬁdence level, for example,exclude the worst 5% of results. The VaR will then be the worstresult remaining.A disadvantage of this technique is that it is more expensive interms of computer time than the variance/covariance approach. It isalso more complex to set up and to explain and still depends on theassumptions made about probability distributions and variances andcovariances. It can, however, be much more reliable for option-relatedinstruments.HistoricAnother possibility is to use historic price movements and apply theseto the current portfolio of instruments, on the assumption that futureprice movements will mimic past ones, and look at the net effect, aswith a Monte Carlo simulation. This approach is therefore as follows:• Map the actual portfolio to a selection of standard instruments forwhich historic price data are available, either from a third party orfrom an in-house database.• Estimate the change in value of the entire portfolio that wouldhave arisen over a particular period in the past – for example, from365 days ago until 335 days ago – by applying actual historic pricechanges. Then measure the change that would have arisen from364 days ago until 334 days ago, then the change from 363 daysago until 333 days ago, and so on.• List all these results and, for a 95% conﬁdence level, exclude theworst 5% of results. The worst result remaining is the VaR for a30-day time horizon.This approach has the advantage of being a simpler techniquemathematically than the variance/covariance and Monte Carlo approaches, and simple to explain in concept. No particular probabilitydistribution (such as a normal one) needs to be assumed, as the probability distribution is implicit in the historic data used. Similarly, thevariances and covariances do not need to be measured or even known,because they are also implicit in the historic data. It also does notsuffer from the same linear restrictions as the variance/covarianceapproach.However, this method still suffers from the drawback that the size ofprice movements and the relationships between price movements ofdifferent instruments (i.e. their correlations or covariances) might notbe the same in the future as in the past. It also depends on the choice
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of historic period. Too short a period might be unrepresentative (forexample, it might have been a particularly volatile or quiet period).Too long a period might include a period so long ago that variancesand/or covariances have changed signiﬁcantly since then.
General problems with VaR calculationsOne obvious difﬁculty with the variance/covariance calculation lies inthe assumption of a normal probability distribution. In practice, theprobability distribution relating to ﬁnancial price movements tendsto have fatter tails than suggested by the normal curve (the left-handand right-hand ends of the graph are fatter than shown above). Thissuggests a greater probability of large proﬁts or losses.There is also a likelihood of negative skewness – a greater probability of a large downward movement in prices than of a large upwardmovement. This is because a falling market can trigger panic stop-losssales which accelerate prices downward.Variances and correlations are also not constant. Those seen in thepast are not necessarily a good guide to the future.A further problem with correlation is that historic relationshipssometimes break down under extreme circumstances. For example,in quiet markets, the US dollar and the Singapore dollar might bevery closely correlated, so that a portfolio long of one and short ofthe other looks relatively safe. When the market panics, however,they might diverge signiﬁcantly. The use of correlation in assessingrisk must therefore be partly based on historic data and partlysubjective.In using VaR for risk management, the bank must also make adecision on the holding period and conﬁdence level (30 days and 95%in the examples above). These parameters are clearly arbitrary anddepend on the bank’s ability to accept risk and the speed with whichit believes it can close out a loss-making position from a managementpoint of view. They also depend on how liquid are the markets inwhich the bank is working, as this affects the speed and ease withwhich it is possible to close a position from a practical market pointof view.
Stress-testingAn alternative approach to statistical analysis such as VaR is scenarioanalysis – assessing risk by assuming a particular scenario andseeing what happens:• Choose a particular outcome for all the variables (FX rates, interestrates etc.).
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• Calculate what would happen to the portfolio’s value under thesecircumstances.• Repeat the process for different assumptions.A sensible check on a VaR model, for example, is stress testingby applying worst-case assumptions – not necessarily historicalones – to assess how different the result is for the portfolio comparedwith the VaR result. This might not necessarily be a basis forregulatory reporting, or for adjusting the portfolio, but is a usefuladditional view of both the portfolio and the VaR model.For example, the bank can consider what is the greatest actualmovement which has occurred in the past in each instrument or typeof instrument. This gives rise to several problems. Firstly, historymight not repeat itself because conditions do change. Secondly, theworst that has happened in the past might in fact not be as bad as theworst that could happen in the future. Thirdly, there is the problemof correlation: depending on the nature of the portfolio, it might behighly improbable that the worst will happen simultaneously in allthe bank’s positions.The advantages of stress testing are that it is good for extremeevents and shows up potential weaknesses in other models. As it canbe based on any assumptions, it is good for ‘what if’ modeling. Adisadvantage of course is that it is only as good as the assumptionsmade – garbage in, garbage out.
BIS recommendations on VaRThe BIS recommends that a supervisory authority should allow thebank to use its own internal VaR model for calculating its capitaladequacy requirements (see the next section). The VaR is taken to bethe greater of either the latest VaR measurement, or the average of thelast 60 days’ VaR measurements multiplied by a factor (which mustbe at least 3) determined by the supervisory authority. This gives thecapital charge for market risk. However, the recommendation is thatan internal VaR model can only be used if the following conditionsare met:• There should be a qualitative check on the model’s use: the bankshould have a risk control unit which is independent of the dealers,and also the model’s results should be integrated into the bank’sday-to-day controls.• There should be a quantitative check: a test portfolio should berun through the model to check its measurements.• VaR using the model should be calculated at least daily.
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• The holding period used should be 10 days and the conﬁdencelevel should be 99% (i.e. there is only a 1% probability of the lossexceeding the VaR number over a 10-day period).• The model should be tested against the previous year’s historicdata (backtesting) to check that the losses actually incurred bythe bank did agree with the results estimated by the model. Fourexceptions or less would be acceptable; between ﬁve and nineexceptions would require the multiplicative factor to be increasedfrom 3; ten exceptions or more would require the model to beinvestigated before it could be used.• VaR is measured for four separate areas of business or ‘portfolios’ – foreign exchange, interest rate instruments, equities andcommodities – and the four ﬁgures added. The bank is allowedto take correlation (i.e. diversiﬁcation) into account to reducethe measured VaR within each portfolio, but not across portfolios – i.e., it may not consider any correlation effects arisingfrom positions in two different portfolios.
Capital adequacy requirementsIn order to prevent banks from taking excessive risks (and hence toprotect the banking system as a whole), the European Union (EU)and the BIS have established guideline limits to the risks whicheach bank may take. In turn, each country’s regulator enforces theselimits, or stronger ones if it prefers, on the banks under its control.The details of these guidelines are complex, and the following remarksare necessarily a simpliﬁcation of them. The concept is that if some ofthe risks are realized, the bank should still have adequate capital toremain in business – hence the term capital adequacy. The variousdirectives from the EU which together set out the risk measurementrules are known as the Capital Adequacy Directives (CAD).
Risk asset ratioEach loan or other transaction undertaken by the bank is measuredin terms of risk according to certain rules, which currently combinemeasures of market risk and credit risk. The total of these risks iscalled its risk-weighted assets. These must be limited in relation tothe bank’s own funds – i.e. its available capital and reserves. Thecapital adequacy rules state that the bank’s risk asset ratio – theratio of own funds to risk-weighted assets – must always be equal toat least 8%. Each regulator may impose a higher requirement than8% on any particular bank under its control if it so chooses.
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The effect of the CAD is to limit the amount, and type, of businesswhich the bank does. If the type of deals undertaken by a particularbank are measured as mostly having a low risk, it can do more ofthem than if they are measured as having a high risk.
Risk asset ratioown funds (i.e. available capital and reserves)=risk-weighted assets (i.e. the amount of moneyput at risk by doing business)Risk asset ratio must be at least 8%
Own fundsOwn funds are roughly equivalent to the value of the bank’s shareholders’ equity in the bank plus reserves and certain other fundingraised by the bank. There are three tiers within own funds:Tier 1:
Tier 2:
Tier 3:
equityreservesretained proﬁtminority interestsless goodwill, other intangibles and current lossesreserves from ﬁxed asset revaluationsmedium-term subordinated debtless holdings of other banks’ issuesdaily trading proﬁtsshort-term subordinated debt
Tier 3 capital can be used only to cover trading book risks (see below)and there are various other restrictions on the uses and relative sizesof the three tiers in covering different aspects of risk.
Risk-weighted assetsTrading book or general banking book?The bank’s business is split into two areas – the trading book andthe rest of the banking book. Essentially, a transaction is in thebanking book if it is a cash loan made by the bank, or a long-terminvestment. It is in the trading book if it involves trading in ﬁnancialinstruments for the purpose of making trading proﬁts, or is a hedgeagainst something else in the trading book – largely the type of activityundertaken by the bank’s dealing rooms rather than by its lendingdepartments.
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If the bank’s overheads are large in comparison with its business,the risk-weighted assets are taken as being 25% of the previous year’sﬁxed overheads. Normally, however, this constraint is not relevantand the risk-weighted assets are the total of the following:Counterparty risk on all businessCurrency risk on all businessLarge exposure risk on all businessPosition risk on trading activitiesSettlement risk on trading activitiesCounterparty riskThe counterparty risk for any transaction takes account of twoaspects – the creditworthiness of the counterparty and the natureof the transaction. For example, a USD 1 million FRA dealt witha particular counterparty does not expose the bank to the samepotential loss as a USD 1 million loan made to the same counterparty.With the FRA, if the counterparty defaults, the bank can lose only thedifference between two interest rates. With a loan, the bank can losethe entire principal amount as well as the interest on it. Counterpartyrisk is therefore measured as:Risk weighting for the counterparty ×
factor depending onthe transaction
Examples of measures for some different risk weightings and transactions are:Risk weighting for counterpartygovernment in ‘Zone A’ (mostly OECD countries) orlocal currency transactions with government in‘Zone B’bank in ‘Zone A’mortgagescorporate (for derivative transactions)corporate (other transactions)
0%
20%50%50%100%
Factor for transaction typeon balance sheet (e.g. loan orbond purchase)off balance sheet but notderivatives transaction
100% of amount0% for low risk (e.g. undrawn,uncommitted, short-termcredit facility)
Bank risk management
exchange-traded derivativeswith daily marginrequirementsinterest rate-related derivativetransactions (such as FRA)with maturity up to 1 yearinterest rate-related derivativetransactions with maturitymore than 1 yearFX-related derivativetransactions with maturity upto 1 yearFX-related derivativetransactions with maturitymore than 1 yearrepos, buy/sell-backs andsecurities lending in thetrading book
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20% for medium/low risk (e.g.documentary credits withgoods as collateral)50% for medium risk (e.g.documentary credits andindemnities)100% for full risk (e.g. billacceptances and guarantees)0%
mark-to-market proﬁt
mark-to-market proﬁt plus 0.5%of amount of dealmark-to-market proﬁt plus 1.0%of amount of dealmark-to-market proﬁt plus 5.0%of amount of dealany amount ofunder-collateralization(including accrued coupon onthe bond and accrued intereston the cash)
(Derivative transactions can alternatively be weighted by ignoring themark-to-market proﬁts but considering the original exposures andusing different factors.)Thus, for example:• The risk for a USD 1 million loan to a corporate counterparty is:USD 1 million × 100% × 100% = USD 1 million• The risk for a USD 1 million 2-year FRA dealt with a Zone Abank is:20% × ((the current mark-to-market proﬁt on the deal)+ (0.5% × USD 1 million))
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In the case of the FRA, if the deal is currently running at a loss, thereis clearly no mark-to-market exposure if the counterparty defaults.The add-on of 0.5% is to allow for the possibility of the deal movinginto proﬁt before a mark-to-market calculation is next made.In assessing the risk, banks are entitled to take account of formalnetting agreements with counterparties, such as that contained inISDA documentation, so that the amount of the deal used in thisadd-on can be reduced by as much as 60%.• For a reverse repo of USD 1 million with a Zone A bank where ourbank has taken extra collateral, the risk is:20% × zero = zero• For a reverse repo where the other bank has taken the extracollateral, the risk is:20% × (USD 1.02 million − USD 1 million) = USD 4,000Currency riskThis measures the net exposure to foreign currencies, as the greaterof the following:• the sum of all net long positions in each currency• the sum of all net short positions in each currencyAn amount of 2% of own funds is then deducted from this total. Banksare, however, allowed to net and/or reduce the measured exposuresfor closely related currencies, provided that the supervisory authorityallows.Large exposure riskThe consideration of large exposure risk requires the bank to allocatemore capital to its assets if the total exposure to any one counterpartyis a particularly large proportion of the total.Position riskPosition risk, which applies only to the trading book, measures exposure to changes in interest rates and security prices. This involvesthe most complex calculations of the CAD. The measurement is of netpositions in particular issues and net positions in closely matchedinterest rate positions (i.e. net positions in each time bucket). ForFRAs, futures, forward purchases and swaps, the bank must considerboth sides of the position. For example, a 3v4 FRA is consideredas a 3-month short position and a 4-month long position or viceversa.
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The risk is measured in two components. The ﬁrst is speciﬁc risk,which considers the issuer of a security held by the bank, withthe amount of risk-weighted asset being between 0% and 8% of thenominal amount of the risk, depending on the nature of the issuer.The second is general risk for the market position. This can bemeasured by using the CAD’s complex and detailed rules for eachtype and maturity of position. Alternatively, because of the complexityof assessing such risk, which varies widely from one bank’s portfolioto another’s, the CAD allows for each bank to use its own internal VaRmodels, provided that this is approved by the supervising authority.Settlement riskSettlement risk is the risk that the counterparty will not settle atransaction for which the settlement date has already passed. Therisk is limited to mark-to-market proﬁt which has already been made,multiplied by a factor which increases towards 100% as the delayincreases.
Future developmentsThe BIS has proposed that the capital adequacy rules be changedin various ways, in a new framework consisting of three pillars. Theﬁrst pillar is the existing system of minimum capital requirement.However, the risk-weighting system for counterparty credit describedabove will be replaced by one based on external credit ratings, so thatdealing with a highly rated bank counterparty will absorb less capitalthan dealing with a low-rated bank counterparty – the reverse of thepresent situation. This will adversely affect weaker banks, who willﬁnd counterparties less willing to do business with them.The second pillar is the supervisory role of the regulator in monitoring the bank. The third pillar is market discipline, encouraginghigh standards of disclosure by banks on their risk management.
13
Corporate riskmanagementAnalysis of risksIt is worth stating at the outset that exposure simply means risk.In the context of corporate ﬁnancial risk, it is common to use theterm exposure rather than risk, so we shall do so here. Companiesgenerally distinguish three types of currency exposure – transactionexposure, economic exposure and translation exposure – althoughdifferent types of economic exposure such as competitive exposureare sometimes considered separately. Not everyone uses exactly thesame deﬁnitions – for example, in whether a particular risk shouldbe categorized as transaction or economic – but the importance liesin understanding the ideas, rather than in agreeing the deﬁnitions.
Transaction exposureThe term transaction exposure is used to describe transactions whichhave already been entered in the company’s books of account, suchas imports, exports, interest and dividend payments. We would alsouse it to describe transactions which are conﬁdently expected to bebooked soon, and transactions which are forecast, as long as the forecast is a reasonably close one. There is a point in time – not clearlydeﬁnable – when transaction exposures merge into economic exposures – see below. In general, transaction exposures are relativelyeasy to identify.Example 1A US company exporting to a German customer in euros hasa transaction exposure to the EUR/USD exchange rate on themoney it is due to receive for sales it has already made and
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invoiced. If the exchange rate falls before the money is received,the company will receive fewer dollars for the transaction.Similarly, a French importer is at risk if sterling rises and he hasinvoices to pay in sterling. Interest payments due in a foreign currencyare also transaction exposures.
Translation exposuresTranslation exposures or balance sheet exposures arise from theeffect of converting foreign currency assets and liabilities into thebalance sheet-reporting currency. This might be because of foreigncurrency loans and investments, or because the company has overseas subsidiaries.Example 2A UK company has a US subsidiary. If the subsidiary’s balancesheet is unchanged from one year-end to the next, the UKcompany is unaffected. However, if the US dollar weakens overthe same period, the subsidiary’s net assets will fall in value asmeasured in sterling. Therefore when the subsidiary’s value isconsolidated into the parent company’s sterling balance sheet,the value of the balance sheet will show a fall from the previousyear. This loss does not represent any cash outﬂow.Hedging translation exposuresIf the parent company wishes to protect against this risk, it mustconsider selling dollars for a date at least as far forward as thebalance sheet reporting date, or going short of dollars in some otherway. This could be by taking a dollar loan, or using options.Example 3A European shipping company is buying some new ships andneeds to ﬁnance the purchases. The company’s analysis showsthat its revenues are largely in euros, while its expenses aremostly in US dollars. The company believes that the nature ofthe ship industry is such that the future value of the ships on itsbalance sheet will be essentially a dollar value. In what currencyshould the ships be ﬁnanced?If the company is focusing entirely on the balance sheet, itshould probably consider ﬁnancing in dollars. Then, if the dollar
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weakens, the fall in the value of its assets, which might forma large part of its balance sheet, will be offset by a fall in theliability side of the balance sheet. However, the business ofthe company represents a continual net inﬂow of euros andoutﬂow of dollars. On a cashﬂow basis therefore, a dollar loanwould put the company at even greater risk than it already isto a strengthening of the dollar. On this basis therefore, thecompany should probably consider euro ﬁnancing instead. Theissue is essentially whether the company wishes to focus on itsnet asset value, or its continuing business.
Economic exposureThe term economic exposure may be used to describe any exposure which is forecast to become a transaction exposure in duecourse – although if the forecast can be made fairly accurately, thiswould often be regarded as a transaction exposure. In general, theterm economic exposure describes the extent to which a company isvulnerable to currency movements in addition to its transaction andtranslation exposures. This can happen in a variety of ways.The simplest example is when a forecast transaction exposure mightnot occur. For example, the sales budget for next year (as opposed tonext week) can only be an estimate. If we forecast sales in euros of50 million but only achieve 30 million, the difference of 20 million isa transaction exposure which we expected but which never occurred.It would therefore have been unwise to hedge the whole exposurebecause if we had hedged against it and the euro had weakened, wewould have made a currency loss with no income to offset it.Another example is where the currency underlying the economicsof the goods or services is not the same as the currency of payment.Suppose, for example, that a Japanese company is paying for fuel ineuros but that the price of the fuel in the medium term is linked to theUS dollar – because that is the currency in which oil is traded internationally. The fuel purchases which have already been invoiced, but notpaid, represent a euro risk – a transaction exposure; the company willsuffer if the euro rises against the yen, but is indifferent to the fate ofthe dollar. However, future purchases represent a US dollar risk – aneconomic exposure; if the dollar strengthens, the euro invoices willrise in line, so that the company must buy dollars rather than eurosif it wishes to hedge against the risk for these future purchases.A type of economic exposure which is much more difﬁcult to analyseis competitive exposure. Suppose we have an Australian companywhose main competitor is a Japanese company. If the Japanese yen
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weakens sharply, our competitor might be able to reduce his pricesand steal some of our market share. If we wished to hedge againstthis risk, we would need to consider selling Japanese yen. It is ofcourse often very difﬁcult to be at all precise about such risks.As mentioned, the deﬁnitions of economic and transaction exposures vary, and are less important than