10
Distance to default $DD$ should be measured in standard deviations. You convert this into a probability $p_{default}$ using the normal CDF: $p_{default} = N(-DD)$. So if $DD = 2.978$ then the firm is about 3 standard deviations from default and has a $\frac{1 - 0.997}{2} = 0.0015 = 0.15 \%$ chance of defaulting in the next period. I divided by two because ...
8
In practice, I would begin with the recovery assumption. In the case of Greece, dealers are probably already quoting recovery swaps, allowing you to set this parameter directly. In general, you have to be willing to make assumptions based on history or on conversations with bankruptcy experts.
Once I have the recovery assumption, I can take any instrument,...
8
I could not find any such detailed documentation after some weeks of looking (not non-stop obviously). It is appallingly documented. I do understand fully what it does though so am happy to field some questions on it if you like.
In a nutshell, I can tell you it is a standard reduced-form credit model under a constant hazard rate (i.e. homogeneous Poisson ...
7
well you can use CDS spreads to strip out implied default probabilities for default before time $T.$ These had better be increasing as a function of $T$ or you have an arbitrage opportunity.
However, there is an assumption here that there is no default risk on the CDS swap itself once you take that into account there may be a good chance of profit but no ...
6
One explanation might be purely quantitative: The spread is to compensate for the present value (cost) of a possible future default. When interest rates rise all else equal, the discounted cost of future default decreases, which translates into tighter spreads. See for instance Leland(1994b) as presented here.
As investigated in a paper from Kansas Fed ...
5
Actuarial science traditionally focuses on estimation of joint probabilities using real data where math finance is on valuation of contracts under an arbitrary distribution.
It means the first one deals with methods of estimation of future distributions (the number of accidents of a given kind, the probability of someone with a given profile to have a ...
5
You could read it like this:
The typical change in equity value is equal to the typical change in asset value, adjusted for the probability of the assets surviving.
Note that the formula is not specific to Merton models, it's also true for regular options and their underlyings. It's just that volatility of option prices isn't typically a concern in "...
5
Via Liquidity Horizons $LH$ (which have to be taken into consideration anyway when modelling for $Basel_3$) as function of the specific concentrations $c$'s.
Increasing the effective maturity of the contract, $M_0+LH_0$, by a quantity proportional to its concentrations with respect to different slicings magnifies the credit risk. $M_0$ is the maturity ...
5
Both 'credit risk' and 'counterparty credit risk' refer to the same type of risk, i.e. the risk that the opposite side of a contract will not honor its obligations to repay.
But 'credit risk' will be typically used in the context of traditional loans business, i.e. for practitioners 'credit risk' will be associated with lending a money to someone - here ...
5
This is indeed a markit vocabulary that spread worldwide. Both recoveries are indeed "often" equal, but there is nevertheless a huge difference between them : one is a pure quotation tool whereas the other is an average or selected market "prices" :
the assumed recovery rate is only used for a quotation purpose : to do the (quoted spread,coupon) --> upfront ...
4
Markit is a pretty good source for CDS information, and their prices are pretty much the standard the industry goes by. Your best bet for finding large spreads would be to look at some of the European Banks or possibly TEPCO after the Japan Tsunami.
Derivatives by default aren't "standard," the instruments are designed to be flexible, but the closest ...
4
Most of the credit risk models are some derivative of survival models. Cox Proportional Hazard is one of the early and more popular models, Kaplan-Meier and Logrank tests are others you may have heard of. There are a few ways to go from here. The simplest is to model the sample as binomial with one population as current and the other as in default. A ...
4
Well, the main intuition of the Merton model is that a company's equity can be treated as a call option on its assets, thus allowing for the application of Black-Scholes option pricing methods. Let's consider a company that has assets $A_{t}$ financed by equity $E_{t}$ and a zero-coupon debt $B_{t}$ with face value K, and maturity T. At time of maturity T, ...
4
All else being equal, buying back stock would cause a company's credit spread to widen (increase). This is because a share buyback involves shrinking the firm's assets (spending cash to buy back the stock) and shrinking equity/retained earnings, while leaving the liabilities unchanged (or increasing them in the case of a leveraged buy back). This is an ...
4
Most, but far from all, companies maintain a relatively steady debt load. When a bond matures, they fund its principal payout with a new bond.
Sometimes companies do take on more and more debt, meaning that CDS protection sold during earlier times of small debt loads becomes more valuable (and underpriced, from the point of view of the protection seller). ...
4
It depends on how one is thinking about the hedge. One might be thinking of it as
A hedge against catastrophic risk (default of the issuer), or
A hedge against changes in (market-implied) default intensity or hazard rate
In the former case, which seems to be how you are considering it, the hedge is a static hedge, kept for up to 5 years, and insulates you ...
4
If you don't have a significant amount of losses in your portfolio to validate the model, you should be able to obtain external loss data and adjust it where necessary to better fit your organization. This is very common with operational loss models where operational losses are quite scarce.
4
Regarding the terminology, there is no relation between CDS spread and bid/ask spread. The term spread in this sense refers to the related difference (spread) of the effective (credit risky) interest rate and the "risk free" rate (also see "credit spread").
4
You would need to provide more details for an accurate PnL attribution. However, here are some additional points to consider that might help.
When you sold protection, you effectively became long the 5Yr synthetic debt of the reference entity at a credit spread of 190bps. Since the coupon is 5%, this would imply a 5Yr (at the coupon frequency of the ...
3
As I see it, the term $\Pi_B(t, T)$ is the value of the derivatives already owned by the bank. So, it's not some price they need to pay but an asset on the balance sheet. This increase in asset value leads to a profit.
Balance sheet Example
Imagine the balance sheet of OTC Subsidiary with rating A:
Assets | Liabilities
-----------------...
3
The classical connection is the http://en.m.wikipedia.org/wiki/Esscher_transform developed for actuaries in 1932 which essentially transforms the objective probability measure into the risk neutral one used in quant finance.
3
I just reviewed the paper Corporate Bond Liquidity Before and After the Onset of the Subprime Crisis by Dick-Nielsen, Feldhütter and Lando. They define a liquidity measure $\lambda$ as a conglomerate of
price impact (Amihud) and its variability
spread covariance (Roll) and its variability
turnover
imputed roundtrip cost (Feldhütter)
zero trading days
I ...
3
Here are some practical tips for selecting stochastic processes for spread curves, for example, in Monte Carlo simulation.
Typically you formulate a joint stochastic model for yields at key maturities due to data limitations.
The corporate yield curves generally maintain order with the AAA yield below AA yield, AA yield below A yield, etc. If, for ...
3
Yes, you can have two different ratings. The issuer has one credit rating, but the individual issues, even if they are both senior unsecured/secured with the same maturity, coupon, etc. can have different ratings. The key factor is going to be the structure/provisions of the issue itself. For example, an issue with a sinking fund is going to be viewed as ...
3
The equation stated in the question is not at the core of Merton's credit model, (Not saying you claimed it is) but is a simple device in helping to solve the system of linear equations.
The equation given simply establishes a relationship between the volatility of equity and the volatility of the assets and it follows from the application of Black Scholes ...
3
Here is another Credit Default Swap database which is rather extensive, daily spreads of roughly 700 entities starting in 2006.
3
One way to approach this is to use a structural credit model which links the price of debt and equity.
To start with, you may wish to consider the JP Morgan / Deutsche Bank Credit Grades model which came out in 2002. In those days, the growing CDS market made it possible for hedge funds to short credit and to play any perceived mispricing of debt-equity ...
3
I do not know the regulatory rules for this case, but methodologically you could take another similar dataset "peer data" and then check how correctly your model predicts the losses of this dataset.
3
Two papers by AQR might be of use:
Asvanunt, A. and S. Richardson (2016), “The Credit Risk Premium”:
Despite theoretical and intuitive reasons for a credit risk premium,
past research has found little supporting empirical evidence. This is
primarily due to biases in computing credit excess returns which
improperly account for term risk. Using data ...
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