Credit Derivatives Risk Management, Trading & InvestingGeoff Chaplin MA, DPhil, FFA Credit Derivatives For other. Credit Derivatives: Trading, Investing and Risk Management, Second The credit derivatives industry has come under close scrutiny over the. The book is accompanied by a website which contains tools for credit derivatives valuation and risk management, illustrating the models used in the book and.
|Language:||English, Spanish, Portuguese|
|Distribution:||Free* [*Registration needed]|
PDF | Credit derivatives were at the centre of the recent meltdowns in the financial sector. The article analyzes The article proposes regulatory prescription of 'minimum board responsibilities' and mandatory risk assessment. . 'sub-investment grade' component showed an is about streamlining the trade in derivatives. Credit Derivatives: Trading, Investing and Risk Management. Файл формата pdf; размером 3,08 МБ. Добавлен пользователем. portfolio. An investment bank can use credit derivatives to manage the risks it incurs credit derivatives market to continue its rapid growth, market System, pervipercora.gq
If recovery is, more realistically, anticipated to be above zero then the spread model for pricing bonds cannot be reconciled with a realistic default and recovery model. A few bonds are special on repo and have yields that do not lie on the surface but are explained by this additional factor.
Credit bonds and loans generally do not trade in the same volumes as government bonds — they are less liquid. Figure 4. Even here the pattern of spreads is not as smooth as we would expect and the deviations cannot be ascribed to repo, seniority or other independently measurable factors. Such differences are usually ascribed to liquidity effects or illiquidity. The situation worsens if we move away from the major borrowers.
Many companies only have one or two bonds in issue: Even if the maturities are similar, an investor would prefer the bond that trades regularly because it is relatively easy to dispose of at a fair price, and there is also a transparent market that allows the valuation of this asset. How much cheaper wider spread should the other bond be? There is no straightforward answer to this question. Estimating a fair price for the callable given the convertible spread involves modelling in some way equity prices and volatility, interest rates and volatility, and default risk.
Suppose only one bond is in issue and rarely trades. Can we estimate the bullet spread, and how? Looking at marks provided by credit traders may not give us the answer. Front book credit bond traders make markets in bonds and will attempt to make a market in illiquid issues and names.
If possible they will attempt to match a seller with a downloader on such issues. A large bank may have a portfolio of debt issued by 10 or more entities. This might 34 Credit Derivatives 80 60 40 20 0 0 2 4 6 Life 8 10 12 Figure 4. Of the traded issues, perhaps trade regularly in the bond market, perhaps another trade irregularly in the bond and secondary loan market, and the other trade rarely. This pattern is illustrated in Figure 4. Marking such a book to market would present considerable problems.
But suppose the bank wishes to clear the economic risk associated with a large part of its portfolio off its books by issuing a derivative related to the reference entities. Investors in the derivative who are synthetically downloading the underlying risks will need to know the current spread associated with each of the names.
Other maturities present a problem. The bulk of the portfolio does not trade. Some products combine both of these methods. The spreads estimated may be bond spread or default swap premium rates — the principles and methods used are nearly identical. We describe products for default swap data below. The contributing individuals are effectively acting as market-makers and are trying to come up with a spread bid and offer on which they would actually be prepared to deal.
As such, a good market-maker will look at various sources of information — known deals, how the market has moved, how this company compares with other similar companies, ratings and changes, current company news, etc. Given certain points on the maturity spectrum, produce spreads for all other maturities. Given a historic spread for the name, estimate where it is likely to be trading now, given data on the general movement of spreads over the intervening period.
Such approaches are used by several data providers. The starting point is to collect data for traded names and calculate average spread and the standard deviation — see Table 4. Also there will be considerable Table 4. Such a process of estimation is only appropriate for derivatives based on large portfolios of names e.
Counterparties which do not meet these criteria will generally not be accepted as counterparties without other conditions being met — for example, full collateral being posted or being accepted for certain types of trades or maturities only. Even once a counterparty has been deemed an acceptable risk, further measures described below are taken to control the risk on individual deals.
Thus a mature deal — for example, an interest rate swap or default swap which had an initial value of zero — may have moved to being USD 2m positive value for party A and the opposite for party B. Under the collateralisation agreement party B will have paid party A USD 2m collateral on which party B receives interest. If party B defaults, and the interest rate swap contract vanishes, then party A has 2m capital which it can use to replace the deal with another counterparty.
To be effective, collateralisation also requires a netting agreement — in the event of default by party B the net value of all the trades with party A some of which have a positive mark to market, others have a negative mark-to-market only is a claim of one on the other.
Note that collateralisation does not eliminate the counterparty risk for two reasons: This risk may be positive or negative. Also in practice the failure of a counterparty may take some time to establish. A counterparty may fail to post collateral one day because of an administrative error. If the counterparty is failing it will probably try to maintain trading relationships as long as possible — and make excuses why collateral has not been posted.
In practice it takes between about 3 and 10 days to determine that a 40 Credit Derivatives counterparty has failed and to close a deal. If the underlying contract is a credit derivative there may be a correlation between the counterparty and the reference name s in the credit derivative which is commonly the case for portfolio credit derivatives. The default of the counterparty will be associated with a widening of the spread on the reference names and a substantial change in the value of the deal.
Default of the counterparty may be related to a jump in the spread on the reference name s , hence a step move in the value of the underlying deal when the counterparty defaults. It is common to require that there is no correlation between the counterparty and the reference deal on credit derivatives, in addition to the other counterparty requirements described above.
Assessing whether correlation exists between a reference entity and the counterparty is usually a subjective judgement and is typically based on some or all of a region and industry overlaps b direct and indirect business relationships c equity correlation. The more certain deals use up the allocated capital, the fewer such deals are done in favour of less counterparty capital-intensive deals.
Detailed understanding of VaR approaches are not needed for this section — Jorion or Grayling provide much more detail. We can calculate a forward value of that asset but are interested in how far the value can fall below the expected value. More generally we are interested in the distribution of market values, and the low-value tail of this distribution. We shall refer to this concept as VaR — we shall suppose that we measure this as the deviation of value below the expected value corresponding to a certain percentile of the distribution of value.
Our interest is in credit risky assets — the term creditVaR is sometimes used to refer to the risk to the value arising from changing credit spreads or defaults. A trade may have counterparty risk, for example, and interest rate swap or a credit default swap has exposure to a counterparty. We can calculate the expected exposure to the counterparty at a forward date. But, again, our interest may be in how great this exposure to the counterparty could be.
In this case we are interested in the high-value tail of the distribution of the asset value at the forward date. We refer to this concept as counterpartyVaR. For both measures we need to be able to produce a distribution of forward values of the underlying asset.
Typically we are not interested in the VaR or counterpartyVaR numbers on a particular deal. We are much more interested in the VaR on the entire portfolio of assets, or the counterpartyVaR to a particular counterparty for all trades related to that counterparty.
For example, let us suppose that we own a 5-year FMC bond priced at par. Then this will have a certain VaR — say X. If — when thinking about the two deals together — the bond falls in value, the insurance will rise in value since the combination of the two will always be worth par see Part II.
So in this case the VaR of the pair of deals is zero. We cannot add the VaR numbers for individual deals in order to get this: We can calculate the expected forward price of a bond of this name using the transition matrix as described in Sections 2. We shall do something extra here because we want to be able to price the bond in the presence of other reference entities whose transitions are correlated. For example, it might contain autos, telecoms, consumer good names, etc.
Similarly, consumer goods and autos will show some correlation because of general economic factors, while utilities and brewing will be much less correlated.
We therefore wish to build in the idea of correlated transitions. First we shall create correlated transitions; then, once we have the forward rating for each entity, we use the forward rating and price the bond using the previous method. If we choose the latter, then the TMs are calibrated to replicate the average spread by rating the bonds in the actual portfolio held, rather than market average spreads.
Once we have chosen the transition matrix we wish to use, we can simulate the forward prices of the bond as follows. Pick a random number x from a normal distribution. Compare this probability with the probability from the transition matrix that the A bond has migrated to 1. Figure 6. We can use this method to simulate the price of the bond at the end of the year knowing the simulated rating and the forward life we can calculate the spread using the TM approach described in Chapter 2 and hence the price.
Of course if we are using the natural measure then the prices themselves are unrealistic — but typically we are looking at the distribution of price changes and we apply those changes to the actual market price.
Two random numbers from the uniform distribution are taken a standard Excel function , one is converted to an exponential distribution, and then a simple transformation produces two independent numbers for the normal distribution. Credit Portfolios and Portfolio Risk 43 0.
Normal distribution and areas corresponding to rating transition probabilities If we use the risk-neutral measure we not only incorporate accurate bond pricing but also a volatility measure for bond spreads based on for example actual spread volatility rather than just volatility arising from rating transitions. We can now use this simulated forward bond price distribution for VaR calculation. Similar techniques can be used for credit derivatives — and we can also use this method to calculate the distribution of counterparty exposure on a credit default swap at a forward date.
If we need to simulate over an n-year period then we have two choices: The latter approach is usually adopted because of speed. This is found to be trivial: How do we produce correlated normal random numbers?
There are various ways: The Cholesky matrix is a triangular matrix with the following property: So, if we generate two independent uniform random numbers x1 and x2 then the two numbers x1 and 0. The extension to many names is mathematically trivial but is best handled in code rather than a spreadsheet. The process described above — generating correlated Normal random numbers using a correlation matrix and independent Normal random numbers — is referred to as the Normal or Gaussian Copula and is discusses in detail in Part III.
Imaging that the random variables are three coins which may come up heads or tails. If A comes up heads then B is very likely to come up heads, and C is also very likely to come up heads. Likewise, if A comes up tails, B and C are both very likely to come up tails.
So in most experiments all three coins come up with the same face showing. The correlation between B and C has to be high — it turns out that 0. The implication is that the correlation matrix cannot be set up arbitrarily, and cannot be stressed arbitrarily. Once we have the forward rating we calculate the forward price using the method of section 6. The missing step so far is: Names that have the same rating at the start of the period will therefore have the same rating at the end.
Observing the transitions of names over many years is not likely to lead to useful results since, in particular, default transitions are rare and a very large number of years would be required. There are several approaches to correlation: We could consider each name by name pair and calculate a correlation based on some data history.
One approach commonly adopted is to use the equity price correlation matrix. We introduced the idea in section 6.
Here we are using industry as a tag to group companies together rather than considering a name-by-name correlation. Another approach is to choose correlations arbitrarily usually driven by tags — e. In the context of VaR calculations it is common to use correlations derived from equity data. The process of derivation of those correlations is typically not pairwise but based on a factor approach.
We shall revisit these topics in a different context in Part III.
Likewise we shall regard credit bonds and loans as credit derivatives. We shall present models which cover these instruments as well as the more obvious credit derivatives. The traditional credit market covers many more instruments than simply bullet debt. Callable or puttable debt follows the same pattern as for the non-credit market.
Such bonds require a model of interest rates and credit spread and default risk. In addition, convertible debt gives an option to exchange the bonds for a certain number of shares. These add a third dimension to the model which also requires equity prices to be modelled. Commercial banks have been offering a variety of derivatives of varying complexity for nearly as long as they have been granting loans. It is economically the same as a spread option on a bond.
The commercial banking forms are often not priced in the same way, are generally not traded but held to maturity or expiry, and do not require a mark-to-market value — unlike the traded equivalents.
It is similar to insurance on the debt of the company, with the main differences that it is not an insurance policy and there is usually a range of deliverable debt. The CDS forms the core of the credit derivative business in terms of numbers of deals done. Spread options arise in a variety of forms. A typical example is the right to sell a bond at a certain spread over a reference rate at a certain time in the future.
Instead of a bond the underlying instrument may be a default swap. Callability or puttability in bonds is usually more complicated, driven by both interest rate levels and spreads. Effectively the underlying traded asset is exchanged to an off-balance sheet asset, which is economically equivalent. A total return swap is very similar to a repo trade.
The note may terminate early, and repay less than par, on a trigger credit event of a reference entity or entities. The simplest example is where a single name default swap is repackaged into a CLN, but the structure may be much more complicated than this, for example, being related to the risk on a mezzanine tranche of a CDO. A single contract describes a collection of default swaps, but otherwise the two deals are identical.
An average basket is actually a single tranche CDO. Examples are the iTraxx and sub-index portfolio CDSs. Nth to default baskets are quite different products. A payment is made in return for defaulted debt on a trigger event for any one of a pre-agreed list of reference names — usually between 3 and The contract then terminates.
The nth to default basket is becoming a vanilla product among portfolio credit derivatives. The product often occurs when a commercial bank seeks protection on its portfolio of loans. CDOs occur in many forms. The iTraxx Europe index relates to a standard portfolio of , and there are over 10 smaller indices from 10 to 30 names representing industry groups, sub and senior CDSs, high-yield and other sets of names.
Market-makers trade standard portfolio structures based on this — generally a single tranche CDS subject to premium which is close to the average CDS premium when the portfolio started. In addition they trade a standard indexing structure on the name reference portfolio.
Portfolio spread options have also recently been introduced. The presence of actively traded tranches of standard CDOs has opened up the possibility of trading spread and spread volatility on a standard benchmark. The portfolio spread option gives the downloader the right to download or sell the underlying CDO tranche at a predetermined tranche premium.
Introduction to Credit Derivatives 49 7. Figure 7. It should be borne in mind when looking at market data on credit derivatives that the underlying product is a structured product that is not traded through an exchange.
There is no independent source of volume of transactions or size and type of deals, so data, particularly from the earlier years when the market was less transparent, has to be viewed with caution.
Table 7. These products form the core of the CD business, are the vanilla trading products, and form the core of the credit instruments that are used to hedge more complicated credit structures.
Credit-linked notes are also largely embedded single name risks. Note that embedded credit derivatives, in particular spread options embedded in other products, are not captured by the survey. The reference entity in the transaction may be a corporate including banking entity or a sovereign. In the case of a sovereign reference entity the default risk typically refers to obligations issued in a currency other than that of the sovereign. Some traders and investors use the same terms to refer to downloading or selling risk the opposite position — it is obviously important to be clear on the terminology you and your counterparties are using.
The quality of the counterparty to the deal has risk and pricing implications but note the contents of section 8. We can see, before looking at details of the contract, that the deal is a portfolio deal: We shall examine the pricing impactions of this later, and in most of this part we shall assume that both the writer and the downloader are risk free.
The contract typically pays par in return for nominal of debt if the reference entity suffers a credit event before the maturity of the deal. The downloader typically pays a premium quarterly in arrears with a proportion up to the default date of the reference name in the event that default occurs before the maturity of the trade.
Section 8. DB Figure 8. FMC Protection downloader: Post-default Figure 8. Note that delivered debt may be YEN, EUR or other currency debt, but the notional amount of debt is chosen to be the same as USD 10m, taking the current exchange rate on the day that notice of the bond to be delivered is given. This is called the delivery option, and was originally introduced to reduce the risk of a squeeze developing on a deliverable issue.
A credit event would cause that particular deliverable to rise to par. If the bond could be bought below par, the holders of protection with no debt to deliver could download the bonds to deliver and obtain the capital gain on the CDS; holders of debt and CDSs would have no incentive to sell debt below par, and holders of bonds only would be bid up until the price reached par.
CDS premia are often referred to as spreads, which is misleading since they are not a spread to anything the terminology arose because a CDS premium — expressed in basis points per nominal — is of similar magnitude to a bond spread. Wrapped bonds have many of the features of a vanilla CDS contract. Average baskets refer to a portfolio of names and are documented in a very similar way to a single name CDS.
Each name may have a notional amount and premium rate associated with it. On a credit event on one name, the notional amount is paid in return for defaulted debt, the premium associated with that name ceases, and the basket continues with the remaining names. In this case an average basket is merely a portfolio of single name CDS contracts, the only difference being that a single piece of documentation is signed rather than many.
Average baskets formed the predecessor to standardised portfolio credit derivatives — such as the products based on the iTraxx indices. The iTraxx-based deals are mostly of this form. Bank guarantees are traditional contracts granted by commercial banks to certain customers. Economically they are similar in form to CDS contracts. This documentation represents a format that can be varied to a greater or lesser extent 58 Credit Derivatives and has changed over time — so many older contracts on the books of traders and investors have slightly different documentation.
At one time the term default option was used for deals where the CDS premium was paid up-front. Insurance Contracts and Documentation A CDS is economically very like an insurance contract on debt issued by a company.
What are the differences between a CDS and an insurance policy? The key differences are as follows: An insurance contract requires the owner of the insurance to own the insured risk at the time a claim is made. In addition, insurance companies — the writers of insurance policies — are regulated by an insurance regulator; banks are regulated by a central bank; and other bodies may not have a regulator other than generally through accounting and legal requirements.
Insurance documentation may look very different from ISDA documentation. Materially different conditions may apply — for example, replacement clauses for reference entities may exist in certain circumstances e. They are then not insurance contracts but simply a CDS where the counterparty is an insurance company. An insurance contract written under ISDA vs a banking contract. Minimal — the counterparty is strictly regulated.
A CDS written by a hedge fund vs a banking contract. Unregulated counterparty, probably unrated. A CDS written by a corporate vs a banking contract. As above, though large corporates typically have rated debt. How would you control the risk in the above? For example, a CDS traded on 5 February may have had an effective date of 12 February and a maturity date of 12 February This means that a new 5-year deal will have anything from 5 to 5.
Single up-front or at maturity premia also occur. In these cases a proportion may or may not be refunded paid on early default. This is usually related to a commercial bank trade where the bank wants to get protection for a particular asset on its books. At one time regulatory treatment was unclear, and specifying the deliverable was an attempt to prove to an uncertain regulator that the protection did in fact cover the risky asset, and regulatory relief could then be obtained.
Cash or Physical Cash settlement is an alternative possibility to physical settlement.
On a credit event the writer pays par less the market value of defaulted debt of the reference name less any accrued premium and subject to a minimum of zero. This typically involves a dealer poll to get independent valuations of defaulted debt of the appropriate seniority and taking an average price.
The process has to be spelt out in detail in the CDS documentation, together with possible recourses for the other counterparty if they disagree.
Usually the writer is the calculating agent. Cash settlement is rare in a vanilla CDS contract. It is sometimes offered as an alternative if physical settlement is impossible for some reason such as an inability to transfer debt because of a newly imposed legal restriction.
It was perceived that Railtrack would continue to meet its obligations on its bonds — and most bonds were high coupon — so the debt continued to trade over par. Holders of such bonds and of CDS protection would not deliver notice that the CDS had been triggered because delivery of above par debt would result in a loss.
On the other hand, writers of protection would deliver such a notice but typically the downloader would fail to deliver since this would result in a loss on bonds over par the choice not to deliver is always available. However, Nomura owned some convertible debt trading at a price of around 60 because it carried a low coupon. This case does point out one additional risk of writing CDS protection rather than owning a bond. If a credit event occurs then the downloader can deliver defaulted debt of the same seniority as the reference obligation or a higher seniority for example, the CDS downloader could deliver transferable loans even if the CDS referenced senior unsecured debt.
In these cases the CDS refers to senior unsecured debt. Any error in the choice of reference obligation may make the CDS legally unclear or may change the seniority from what was intended. Quoted from Mark-it Partners summary6: The key credit events are 1. Failure to pay.
This usually has to be of a certain amount of money — the failure to settle a telephone bill on time will not usually meet the requirement! Railtrack in the UK. Restructuring see below. This may involve lowering the coupon, lengthening the debt, reducing the nominal, reducing the seniority, or otherwise replacing the debt with something of lower value.
Sovereign borrowers typically restructure, and it is far from uncommon for corporate entities to restructure. CDSs currently trade with various types of restructuring clause.
No restructuring trigger rare. The detail of the following is not necessary for an understanding of the remainder of the book, but is described for completeness.
A reduction in the rate or amount of interest or the amount of scheduled interest accruals. A reduction in the amount of principal or premium payable at maturity or scheduled termination date. A postponement or other deferral of a date or dates for a payment or accrual of interest, or b payment of principal or premium. A change in the ranking in priority of payment of any obligation causing the subordination of that obligation.
The Conseco case caused US market participants to question the clause. Conseco and its banks agreed to a restructuring of certain loans, including an extension of maturities and a commitment to pay down those loans early from the proceeds of asset sales. Holders of longdated bonds trading at very depressed prices and of CDS protection, delivered such debt into CDS protection under the above restructuring clause.
On the occurrence of a restructuring event only debt that has maturity on or before the earlier of a 30 months after the restructuring date, and b the latest maturity of the restructured debt, is deliverable but subject to any debt being deliverable that has a life less than or equal to that of the CDS itself. The above do not apply if the seller triggers the CDS.
Generally this results in deliverable debt being of a similar maturity to the CDS if this is longer than 30 months , and results in the CDS being closer to a proxy bond. Other conditions introduced at the same time were: In this section we look at some basic applications of single name products in general terms, covering more detail and more applications in Chapters 10 to To some extent the CDS market developed as an alternative to an open repo market in credit debt.
CDSs are a simple practical means of taking a bearish position on credit debt. Suppose, instead, your view is that GM looks more attractive than Ford. Selling protection on GM makes money if spreads narrow, but loses money if spreads widen.
The opposite position in FMC — downloading protection to the same maturity — will largely offset this if spreads move together. Work through the example where a spreads both widen by 50 bp b where GM widens by 20 bp but FMC widens by 70 bp. Debt Hedging An owner of 5-year senior unsecured France Telecom bonds could hedge the risk in a general sense by downloading CDS protection. In the occurrence of a credit event during the life of the CDS, the debt can be delivered into the CDS and par is received.
The value of the bond or its download price may be 90, so this represents over-protection equivalently, too much is being paid for the protection. The CDS therefore protects not only against the default event risk but also against the mark-to-market change in value of the bond arising from spread change.
Suppose now that the FrTel asset is not a bond but a bank loan. It may be possible to arrange a CDS referencing loan at an appropriate cost but liquid CDS contracts usually cover risk on less senior debt and are more expensive. Even so, loans — being more senior than bonds — are in principle deliverable into standard CDS contracts.
But on a credit event the owner of the loan may have a problem delivering the loan — it may be non-assignable.
The portfolio manager can therefore protect non-assignable loans using a vanilla CDS, albeit at a cost. Some index-based CDS products may be appropriate — for example, the iTraxx TMT telecom, media and technology and auto indices — but often the view has to be implemented by dealing in a portfolio of representative names.
If your view is that the motor industry looks attractive relative to telecoms, then again you could write protection on a portfolio of auto names and download protection on a portfolio of telecom names in order to reduce exposure to any single name. The usual minimum trading size is 5m per name. Clearly how the view is implemented depends on how large an adverse move you are prepared to withstand, and the size of your risk limits.
Exercise Suppose your risk appetite is k i. You set a stop at a 20bp adverse move. How many names could you incorporate in the 5-year mini-portfolio in order to implement the trade? On 5m nominal what loss does a 20 bp move correspond to? An alternative approach would be to seek average basket deals each referencing 5—10 names with a total exposure on each of around 20m.
The average basket allows more names but a smaller exposure per name, because a single piece of documentation for each basket is used. The disadvantage is the limitation on the possibilities for unwinds. Average baskets have less appeal than single name products and offsetting trades are harder to come by — liquidity is lower — often tying the downloader to go back to the original counterparty for an unwind.
Typically this style of trading is implemented by proprietary or hedge fund traders and as an overlay to the general hedging of positions by a trading book. Often this is done against positions actually held — a large rise in the share then results in a limited gain on assets held rather than an actual loss.
CDSs can likewise be used to generate income by selling protection, but this would not normally have a natural hedge already in place. Such a trade often appeals to insurance companies or banks who are typically 7 Interpreted with respect to bond prices — i. So far investors have not generally traded in this way, preferring to build a portfolio of credit bonds instead. This is generally handled by setting limits on individual exposures, country exposures, industry exposures, rating exposures, etc.
What portfolio make-up would you suggest? This is best done by working through an example. Suppose we have a portfolio with an average spread of bp — and a 2bn size. If this is a managed portfolio of BBB names then the historical default rate may be 0.
If there are names in the portfolio then we would expect 0. Write a list of discussion points e. The example is in the context of Basle 88 regulations — the same basic approach applies under Basle II but the capital percentages will be different.
The example is not commonly implemented in this form — rather portfolio trades are done these are covered in Chapter Suppose a bank owns a EUR 10m 5-year loan to a corporate entity generating 80 bp in spread.
The bank downloads CDS protection on the reference entity at 60 bp — spreads may have narrowed since the loan was issued — for the same or slightly longer maturity than the loan this is required in order to get any capital relief. The net income is now reduced to 20 bp.
The regulatory capital depends on the counterparty to the CDS trade. Capital which is released can be used to put in place new loans earning not just the spread but also fee income.
An institution has internal rules for assessing risk on any deal — loans and CDSs, etc. See any book on probability — for example, Billingsley, The regulatory capital example above applies equally to economic capital. Once a line is full no further lending can take place to that client.
The option of selling existing loans to enable new loans to take place may not be attractive. The bank may require the consent of the borrower to sell these loans, which may jeopardise the client relationship.
FMC may not be aware that a CDS deal referencing its debt has taken place — hence this offers the bank a chance to reduce the risk to FMC on its banking books synthetically without risking any banking relationship that may exist between the bank and FMC. The non-assignable loan stays on the books but, because CDS protection has been bought, the exposure line to FMC is reduced during the life of the CDS, and new lending can take place. Other We look in detail at examples of curve trading, recovery trading, and other applications from Chapter 10 onwards.
Potential Future Exposure and Capital Allocation A further step to prepare for counterparty risk is to reserve against it. The market spread yesterday was bp and today it has moved to bp. What is the current approximate M2M? What key factors affect the M2M in the future? At what time in the life of the deal does potential future exposure reach a maximum? If spreads are static, then M2M remains zero, but if spreads are volatile it can become positive or negative.
If the premium on the trade is other than the current fair market premium, then there is the initial mark-to-market value to add on. This may be positive or negative e. How would you allocate capital? If a cautious estimate of default risk is taken, this should reduce as the number of counterparties increases.
A more sophisticated approach would be to look at counterpartyVaR. For vanilla deals this has been streamlined. This has been reduced and the term-sheet is now no longer used for vanilla deals between professional counterparties but a general form of contract is agreed between them before they begin dealing with each other. With that in place, the particular deal contract is usually signed within one day of dealing.
On the trade date: General terms are agreed with the counterparty possibly via a broker — reference entity, maturity, premium, etc. The writer goes on risk and premiums start to accrue. Deals will typically need to be rebooked, with updated deal information for credit event data: The interactions are summarised in Figure 8.
The Transaction Supplement shall set forth, at a minimum, all of the information set out in the applicable form of Transaction Supplement attached hereto as Annex 1.
Preparation of Transaction Supplements. The preparation of a Transaction Supplement shall be the responsibility of the Seller in respect of the Transaction to which the relevant Transaction Supplement relates.
General Terms: Trade Date: As shown in the Transaction supplement Effective Date: As shown in the Transaction Supplement Transaction Type: Seller Calculation Agent City: As shown in the Transaction Supplement Business Day: If the Transaction Type indicated in the Transaction Supplement is: We shall address the question of how we estimate R prior to the default event in the following section.
At present formula 1. Argentinean debt in USD are very similar to those of corporates. Typically the claim amount is also par. Usually there is only one level of seniority for sovereigns. The major difference between corporate and risky government debt is in the recovery process itself.
The government may offer terms that are very different from the recovery levels one might expect from the issue document — typically long-dated debt recovers a smaller proportional of notional than short-dated debt. However, the lenders have no court to which they can go to seek a strict implementation of the process described in the issue document. In practice this means that recovery for sovereign debt is not at the same rate for all bonds6 — instead it is typically high for short-dated debt and low for long-dated debt.
Often the price quoted is a clean price, and the consideration paid also takes into account the accrued interest. Typically investment grade debt debt rated BBB or better by the rating agencies trades up to bp over the swap curve depending on the name and varying with time and the economic cycle, sentiment, etc.
Sub-investment grade debt typically trades wider — to 10 bp or bp above the swap curve. For investment grade names the market will usually talk in terms of spread rather than price. The reason for this is that the price of a 5-year Unilever bond for example will change moment by moment as interest rate futures tick up or down.
For sub-investment grade debt the market usually talks in terms of price. Imagine an insurance policy which insures the par value of the bond in the event of default of the underlying name. The downloader of the insurance policy is the downloader of protection, and the writer of the policy is the seller of protection.
We can also talk in terms of risk — the seller of protection is taking on risk, similar to the downloader of the bond itself, while the downloader of protection is also the seller of risk. In the credit derivative market both sets of terminology are used — downloader or seller of protection or of risk.
It is essential to be clear whether one is talking about risk or protection. In this book we shall generally talk in terms of downloading or selling protection when we refer to credit derivatives. This is easy for liquid bonds for example, and is called marking to market. For many other assets — such as credit derivatives, structured products and many option contracts — this is not always practical.
For example, consider a portfolio of equity call options of various maturities and strikes. We can obtain these prices and interpolate for other maturities and strikes on the same underlying asset.
Usually this interpolation uses a pricing model — such as the Black—Scholes model — and an intermediate variable volatility is obtained. Interpolation on this variable is performed perhaps involving a further model such as a volatility smile model and the interpolated variable put back into the model in order to get an estimated market price for the asset. This is referred to as marking to model. Suppose we are long a unit of Asset 1, mark it to mid-price P1 , and S1 is the estimate of half the offer—bid difference.
Exercises 1. The credit derivatives industry has come under close scrutiny over the past few years, with the recent financial crisis highlighting the instability of a number of credit structures and throwing the industry into turmoil. What has been made clear by recent events is the necessity for a thorough understanding of credit derivatives by all parties involved in a transaction, especially traders, structurers, quants and investors. It provides: Permissions Request permission to reuse content from this site.
Ultimate and Market-Value-Based Recovery 15 2. Rating and Other Factors 21 2. Basic Concepts and the Default and Recovery Model 81 9. Liquidity Clayton, Gumbel Pricing and Hedging