The Relationship Between Default Rates And Recovery Rates And Its Impact On Credit Risk Management

Recently, many studies have shown that collateral values and default recovery rate showed volatility. Moreover, when the economic is in a downturn, the increase in default rate at the same time, collateral values and recovery rates show a downward trend. This phenomenon shows that default rates and recovery rate may be show some correlation. But most of the credit risk models assume default rate and recovery rate independent of each other, they only focus on the probability of default, the recovery rate as a constant parameters or a random variable which is independent of the probability of default. This assumption would have impact on credit risk management. Obviously, if we analyze the impact we need to look at the relationship between default rates and recovery. Main contents of in the paper as follow:First, from the perspective of theoretical analyze how did systemic risk effect default process and recovery process to explore how the default rate and recovery are linked. Second.from the perspective of empirical analysis tests the relationship between the default rate and recovery rate.Once the relationship between default rates and recovery specified, in order to clearly demonstrate the extent of this affect, uses Monte Carlo simulation techniques to compare the different assumptions of the relationship between default rates and recovery rate, and found that if ignored the relations between default rate and recovery rate only just assumed independent of each other, it will underestimate the credit risk level, it will lead to insufficient bank reserves and even the financial markets would suffer unnecessary shocks.Insufficient place, contribution and the major viewpoint of paper:1. Based on Moody’s global corporate default data for empirical analysis of single-factor model to study default risk and systematic risk correlation and recovery risk and systematic risk relevance. We found that the relationship between asset returns and systematic risk has a relative stable relationship over time, which is in a relatively short period of time, the correlation coefficient remained relatively unchanged, from the long term, while the correlation coefficient fluctuates over time, but the relative invariance of the correlation coefficient may be used to predict default rate. Second, analysis of the correlation coefficient of different rating companies and found that except Aa rating, Rating the lower with the absolute correlation coefficient higher, i.e. companies with lower ratings than higher companies more dependent on systemic risk.2. In the economic boom periods to reduce default rates, recovery rates rise, while in the economic downturn, default rates rise, the recovery rate is lower, but this does not mean that the relationship is symmetry. On the contrary, it is non-symmetrical relationship the reason is that recovery exists is because of the events of default, In different economic states has different default rates, at the economic boom periods default rates lower, recovery rates higher, and vice versa, to a certain extent, the default rate as reflected the macro-economy status, it can be said that default rate decide the systemic the recovery rate, which led to defaults and recovery rates between the non-symmetric relation. With default rates as the explanatory variable, the recovery rate as the dependent variable to test the relationship between them. In the regression analysis, besides default rates, we incorporate a number of macroeconomic variables as independent variables to explain the recovery rate.3. This paper analyses the impact of various assumptions on which most credit risk measurement models are presently based:namely, it analyses the association between default rate and the loss given default on bank loans and corporate bonds, and seeks to empirically explain this critical relationship. Moreover, it simulates the effects of this relationship on credit VaR models, as well as on the procyclicality effects of the new capital requirements proposed in 2001 by the Basel Committee. Summing up, if PD and LGD were driven by some common factors, then not only the risk measures based on standard errors and percentiles (i.e. the unexpected losses usually covered with bank capital), but even the amount of "normal" losses to be expected on a given loan (and to be shielded through charge-offs and reserves) could be seriously underestimated by most credit risk models.4. There are still some deficiencies in this paper. Firstly, single-factor model was based solely on empirical analysis, but made no new theoretical model to examine the correlation between default rates and recovery rate. Secondly, we only applied Moody’s data to regression analysis, but not more economic variables on the recovery of empirical analysis.