Topics 21-23 Flashcards
Compare the different ways of representing credit spreads
Define and compute the Spread ‘01
DV01 captures the dollar price change from a one basis point change in the
current yield. A similar concept for credit spreads is known as DVCS (i.e., spread ‘01). Here, the potential change in the bond price is estimated from a one basis point change in the z-spread. Specifically, the z-spread is shocked 0.5 basis points up and 0.5 basis points down and the difference is computed.
Intuitively, the smaller the z-spread, the larger the effect on the bond price (i.e., the greater the credit spread sensitivity). This result is straightforward because the same one basis point change represents a larger shock relative to the current z-spread when the z-spread is low. Thus, the DVCS exhibits convexity.
Explain how default risk for a single company can be modeled as a Bernoulli trial
A Bernoulli trial is an experiment or process where the outcome can take on only two values: success or failure (the firm does or does not default during a particular time period).
An important property of the Bernoulli distribution is that each trial is conditionally
independent. That is, the probability of default in the next period is independent of
default in any previous period.
Explain the relationship between exponential and Poisson distributions
# Define the hazard rate and use it to define probability functions for default time and conditional default probabilities, calculate the conditional default probability given the hazard rate
The hazard rate (i.e., default intensity) is represented by the (constant) parameter λ and the probability of default over the next, small time interval, dt, is λdt.
If the time of the default event is denoted t*, the cumulative default time distribution F{t) represents the probability of default over (0, t):
P(t* < t ) = F(t) = 1 - e-λt
The marginal default probability (or default time density) function as the derivative of F(t) with respect to the variable t:
λe-λt
It is evident that this quantity is always positive indicating that the probability of default increases over time related to the intensity parameter λ.
If we examine the probability of default over (t, t + τ) given survival up to time t, the function is a conditional default probability. The instantaneous conditional default probability (for small τ) is equal to λτ.
The conditional one-year probability of default, assuming survival during the first year, is equal to the difference between the unconditional two-year PD and the unconditional oneyear PD, divided by the one-year survival probability.
Calculate risk-neutral default rates from spreads
Describe advantages of using the CDS market to estimate hazard rates
The primary advantage of using CDS to estimate hazard rates is that CDS spreads are observable.
We can draw on the logic of a reduced form model to use the observable, liquid CDS to infer the estimates of the hazard rate.
Liquid contracts exist for several maturities (e.g., 1, 3, 5, 7 and 10 years are common). Furthermore, a large number of liquid CDS curves are available (800 in U.S. markets, 1,200 in international markets) and the contracts are more liquid than the underlying cash bonds (i.e., narrower spreads and more volume).
Explain how a CDS spread can be used to derive a hazard rate curve
- The CDS spreads provide several discrete maturities to extract hazard rates.
- Technically, hazard rates are measured every instant in time so the CDS data will only provide a few observable data points and will require some form of interpolation or piecewise construction to complete the curve.
- The fact that the CDS swap spread is observable allows for the inference of default probability for the 1-year maturity by equating (PV of expected payments in default) and (PV of expected premiums paid). Thus, given an assumed recovery rate (usually 40%), the probability of default and, hence, the hazard rate can be inferred for the first period (using the first piecewise portion of the earlier hazard function).
- The bootstrapping procedure is then employed so that the hazard rate for the first period is used to infer the hazard rate for the second period from the piecewise function (using the observable information from the second CDS contract with a 3-year maturity, a recovery rate assumption, and the swap curve). Similarly, the hazard rate from the second period is an input to find the hazard in the third period, and so on. In this fashion, a graph can be constructed showing the CDS spreads, hazard rates, and default density.
Explain how the default distribution is affected by the sloping of the spread curve
As a benchmark, consider the impact of spreads that are constant for all maturities, that is, the market’s expectations for default is constant. In this case, the spread curve would be flat implying the probability of defaulting in the near term is the same as defaulting in the long run.
The most common spread curve is upward sloping. Thus, the aggregate market forecast is that default is unlikely in the near term but increases with the forecast period. In contrast, spread curves, although unusual, may be downward sloping. This phenomenon would indicate relatively high expectations of short-term default (distress) but, if the firm can right itself, it will likely survive for a sufficiently long period of time.
Define spread risk and its measurement using the mark-to-market and spread volatility
To measure spread risk, the mark-to-market of a CDS and spread volatility can be used. The mark-to-market effect is computed by shocking the entire CDS curve up and down by 0.5 basis points (similar to spread ‘01). Note the slight difference from spread ‘01 where the z-spread, a single value, was shocked. Thus, the entire CDS curve moves up and down by a parallel amount.
An alternative measure of spread risk is to compute the volatility (standard deviation) of spreads. The spread volatility can use historical data or can be forward-looking based on a subjective probability distribution. Not surprisingly, the spread volatility spiked extremely high during the recent financial crisis for many financial services firms.
Define and calculate default correlation for credit portfolios
Default correlation measures the probability of multiple defaults for a credit portfolio issued by multiple obligors.
! Default correlation has a tremendous impact on portfolio risk. But it affects the volatility and extreme quantiles of loss rather than the expected loss
Identify drawbacks in using the correlation-based credit portfolio framework
A major drawback of using the default correlation-based credit portfolio framework is the number of required calculations - the number of pairwise correlations is equal to n(n — 1).
In addition, certain characteristics of credit positions do not fit well in the default
correlation credit portfolio model. For example, credit default swap (CDS) basis trades may not be modeled simply by credit or market risk.
Additional drawbacks in using the default correlation-based credit portfolio framework are related to the limited data for estimating defaults.
Assess the impact of correlation on a credit portfolio and its Credit VaR. Define and calculate Credit VaR
The effects of default, default correlation, and loss given default are important determinants in measuring credit portfolio risk. A portfolio’s credit value at risk (credit VaR) is defined as the quantile of the credit loss less the expected loss of the portfolio. Default correlation impacts the volatility and extreme quantiles of loss rather than the expected loss. Thus, default correlation affects a portfolio’s credit VaR.
The term “granular” refers to reducing the weight of each credit as a proportion of the total portfolio by increasing the number of credits. As a credit portfolio becomes more granular, the credit VaR decreases. However, when the default probability is low, the credit VaR is not impacted as much when the portfolio becomes more granular.
Describe the use of a single factor model to measure portfolio credit risk, including the impact of correlation
An important property of the single-factor model is conditional independence.
Conditional independence states that once asset returns for the market are realized, default risks are independent of each other. This is due to the assumption for the single-factor model that return and risk of assets are correlated only with the market factor. The property of conditional independence makes the single-factor model useful in estimating portfolio credit risk.
Conditional Default Distribution Variance