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Risk-neutral default probability implied from CDS is approximately $P=1-e^\frac{-S * t}{1-R}$, where $S$ is the flat CDS spread and $R$ is the recovery rate. The CDS Spread can be solved using the inverse: $$S=\ln(1-P) \frac{R-1}{t}$$ $S$ is the spread expressed in percentage terms (not basis points) $t$ are the years to maturity $R$ is the recovery rate ...

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You can use the "credit triangle" which states that the (annualised) credit spread $S$ equals the annualised probability of default $p$ times the loss given default LGD which equals par minus the expected recovery amount $R$, i.e. $S=p(1-R)$. This is a "back-of-the-envelope" approximation to a full hazard rate credit model - from experience I find that the ...

6

For an individual firm, a theoretical model of the capital structure was developed by Robert Merton in 1974. The simplest form of this model assumes the firm has zero-coupon debt maturing at some future time $T$. Default is defined as the condition where the value of the firm's assets fall below the outstanding debt. The firm equity is viewed as a call ...

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I believe the answer can be further improved for all those being directed here by google after 3 years. A common way to model the default probability is by the hazard rate. As @Bob correctly mentions, a traditional requirement is for it to satisfy (see Option Futures and Other Derivatives section 23.4 in which the author discusses also other more exact ...

5

This is what Moody's does to calculate default probabilities, but I don't believe they give a whole lot of detail on their exact methodology because they sell their models as software. I quickly found this which gives a brief overview: http://www.moodysanalytics.com/~/media/Brochures/Enterprise-Risk-Solutions/RiskCalc/RiskCalcPlus-Fact-Sheet.ashx Edit- ...

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Firstly, the use of the logit models to estimate the PDs is particularly appreciated in some credit industries, as, for instance, the credit retail one. The logit model predicts pretty well the PD on loans, consumer credit, credit cards, ... and all concerns the retail consumer world. Mainly, those listed are the principal sub-industries in the credit ...

5

You cannot do it. It is an under-determined problem. That is to say, a whole multitude (subspace of $\mathbb{R}^{N\times N}$) of migration matrices will agree with any given table of default probabilities. Say you want to find a transition matrix for 2 states (IG, HY) plus default \left(\begin{matrix} p_{11} & p_{12} & p_{1D} \\ p_{21} &... 5 "Debt issuer default risk" and "counterparty risk" are very similar. From Risk magazine: Counterparty Risk The risk that a counterparty to a transaction or contract will default (fail to perform) on its obligation under the contract. Counterparty risk is not limited to credit risk (the risk that the counterparty cannot fulfill its contractual ... 4 The chapter in Hull on Credit Risk gives the same formula as emcor as a first approximation with a justification: Consider first an approximate calculation. Suppose that a bond yields 200 basis points more than a similar risk-free bond and that the expected recovery rate in the event of a default is 40%. The holder of a corporate bond must be expecting to ... 4 Actually, there is a practical way to do it. You can use you PoD estimates to assign a credit rating to your securities and then use a published transition matrix for your purposes. Or you can estimate transition probabilities by linear interpolation based on the PoD values that you have. Here is a publication containing transition matrices from Moody'... 4 Firstly it's good to straighten out our goal. You correctly say, that IFRS9 requires analysis of expected losses. There are two components of expected losses. 1) Expected probability of a default event 2) Expected recovery rate So, not only do we need the probability but also the recovery rate. Luckily, both are approximated by the credit spread, which ... 4 The relationship between volatility and CDS is very interesting. Volatility in finance is synonym of risk. There are many aspects of volatility. There are 2 primary ways to find CDS premium, one is using structural model and the other is reduced form or intensity based model. Structural models use equity valuation, outstanding debt and equity volatility to ... 4 Let \tau be the default time, \lambda be the constant hazard rate, and T=1.0 be the bond maturity. The value of the defaultable zero-coupon bond is given by \begin{align*} D(0, T) &= e^{-rT}P(\tau > T). \end{align*} Then the default probability is given by \begin{align*} P(\tau \le T) &= 1- P(\tau > T)\\ &=1-D(0, T) \times e^{rT}\\ &... 4 Let \tau_{(1)} = \min(\tau_1, \ldots, \tau_K) be the first-to-default time. Moreover, for 1< m \le K, let \begin{align*} \tau_{(m)} = \min\left(\tau_k: k=1, \ldots, K, \tau_{k} > \tau_{(m-1)}\right). \end{align*} be the m^{\rm th}-to-default time. In particular, \tau_{(K)} = \max(\tau_1, \ldots, \tau_K). Note that, for t \ge 0, \begin{align*} ... 4 Based on your definition, they are certainly not the same. Generally, the marginal default probability is the probability that the default happens in a given time period, such as [t, t+\Delta], that is, P(t < \tau \le t+\Delta). Here, \tau is the default time. See Chapter 10 of the book Counterparty Credit Risk and Credit Value Adjustment for ... 4 It is actually that you forgot your 1 - R in formula (2) :) The index survival curve is defined similarly to the tranche's : Q\left(t\right) = 1 - \mathbb{E} \left[L\left(t\right)\right] = 1 - \left(1 - R\right)\mathbb{P}\left(\tau < t\right). Hence, your formula for the 0-100 tranche survival curve does coincide with the index'. That history of loss ... 3 The question sounds like a conditional probability problem. However, note that, for conditional probability, people will generally say if survived to or conditional on. Here it says that survived in year one and (i.e., followed by) will default in year two. Then we should not treat this as a conditional or marginal probability. Based on the above ... 2 detailed description of the solution of this problem using Excel is in the second chapter of the book Credit Risk Modeling using Excel and VBA Gunter Löffler 2 This does not sum to 1 because you have forgotten to add the 6th scenario, the NonDefault (ND). If Ps is the probability of survival and Pd the probability of default, the ND has the probability Ps^5. This makes: Pd+ Ps*Pd+ ... Ps^4*Pd+ Ps^5= Pd*(1+Ps+...+Ps^4)+Ps^5= Pd*(1-Ps^5)/(1-Ps)+Ps^5= (1-Ps)**(1-Ps^5)/(1-Ps)+Ps^5=1. 2 I think the problem is that, for countries with a sizeable risk of hyperinflation, you will not have deep and mature markets to extract market expectations from. Argentina is a good example. Hyperinflation is just 'very big inflation', but you don't have inflation swaps in ARS. The CDS that you mention will pay in USD, and are therefore immune to ARS ... 2 RRL's answer is entirely correct in terms of the theoretical reason underpinning the relationship between equity IV and CDS spreads. "CDS spreads are not “pure” default risk compensation" - no they are not since the ISDA Quoted Spreads assume a homogeneous Poisson process (implying that instantaneous default risk is a constant over the life of a contract) ... 2 I believe that your problem can be formulated as: Find PD matrix that is as close as possible to a given PD matrix (result of some previous calibration, or the matrix computed using average hazard rate, or any other "target", or the penalty on non-smoothness) subject to the following constraints: The values that are given must be matched exactly ... 2 I suggest you to start from the Altman's model, that is the basic model to implement the kind of econometric analysis you're looking for. You can find the original paper at my Dropbox public folder. After that reading, you can find a number of paper about scoring models on SSRN or Google Scholar. Moreover, I suggest you to look for all academic papers that ... 2 From this research from Deutsche Bank, :p_{def}=\frac{CDS_{spread}}{1-Rec}\Leftrightarrow CDS_{spread}=p_{def}(1-Rec)$$where Rec is the recovery rate in case of default. 2 Suppose I give you objective probabilities \mathbb{P}(S_T \geq K) of an equity finishing above a certain level K at a future time T (or in your case a survival probability in the form of default rates). Can you convert these to risk-neutral probabilities \mathbb{Q}(S_T \geq K) ? Not immediately. First, I need to give you a model for the behaviour ... 2 Re LGD: you can look at Mark-iT for ISDA Credit Event Auction settlements, here's a link actually http://www.creditfixings.com/CreditEventAuctions/fixings.jsp Obviously these will give recoveries as determined by bond auctions, varying according to SUB and SENIOR in the type of names you are looking at. These outcomes are a bit pathological for banks as SUB ... 2 I'm no expert on this topic but here's my two cents. Hopefully if I'm wrong someone will correct me. From the 2 relations you wrote, we see that$$ DD_q = -N^{-1}(P) - \lambda R \sqrt{T}  or equivalently \begin{align} DD_q &= DD_p - \lambda R \sqrt{T} \\ &= \frac{\ln(A/D)+((\mu-\lambda \sigma R) - 0.5\sigma^2)T}{\sigma \sqrt{T}} \end{align} where ...

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First, I would emphasize that default protection is bought and sold on debt securities , not on assets. To answer your question, you cannot sell protection on your own debt. You can sell protection on sovereign debt, including the sovereign where your company is based. However, the buyer of this protection understands that there may be a high correlation ...

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It is mainly subjective, depends on country and sector. E.g. when I worked in private equity in a distressed fund a highly levered company was a company with a net-debt to EBITDA ratio > 7.0. Those are back of the envelope numbers. They actually do not tell you much about the health of the company nor its risk. A company with 7.0x net-debt to EBITDA ratio ...

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If there are 10 issuers and one defaults this year, the issuer weighted probability of default is 0.1. But if the one issuer that defaults is one with a larger than average amount of debt outstanding, the dollar volume weighted rate of default for the year is going to be > 0.1. Moody's tries to predict the default of issuers, so they mostly work with ...

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