Hi

@Kavita.bhangdia
@Delo 's link is relevant. On the one hand (above presumably, but more exactly Delo's link), Gregory's assertion is based on a narrow, mathematical application of the well-used approximation: λ = S/(1-R); i.e., if we hold the spread, S, constant,

*then conditional on a constant spread*, if we increase recovery, R, then the hazard rate (conditional PD) increases. This is just a mathy way of saying: if the market charges you fixed S for credit risk, but the recovery is/goes higher (compared to baseline), the compensation must be due to increased default probability. Or just: Spread ~= PD*LGD; i.e., credit spread is compensation for PD and LGD. (nevermind this is also just an simplifying approximation!)

Gregory Chapter 10 is (i) an empirical observation and, more importantly, (ii) doesn't say "conditional on constant spread." Gregory Chapter 10:

Recovery values, like default probabilities, tend to show a significant variation over time, as illustrated in Figure 10.10. We can see further variation according to variables such as sector (Table 10.5). Recoveries also tend to be negatively correlated with default rates (e.g., see Hamilton et al., 2001). This negative correlation means that a high default rate will give rise to lower recovery values. Hence, the random nature of default probability and recovery over time coupled to the negative

... we'd expect the higher default rates and correlated lower recovery values to be associated with higher spreads. This is a good illustration of not getting too mesmerized by formulas

and the difference between a model, which exist in an unrealistic setting, and empirical observations. I hope it helps, thanks!

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