This paper studies loss given default using a large set of historical loan-level default and recovery data of high loan-to-value residential mortgages from several private mortgage insurance companies. We show that loss given default can largely be explained by various characteristics associated with the loan, the underlying property, and the default, foreclosure, and settlement process. We find that the current loan-to-value ratio is the single most important determinant. More importantly, mortgage loss severity in distressed housing markets is significantly higher than under normal housing market conditions. These findings have important policy implications for several key issues in Basel II implementation.

We examine the relevance and effectiveness of stock return correlations among financial institutions as an indicator of systemic risk. By analyzing the trends and fluctuations of daily stock return correlations and default correlations among the 22 largest bank holding companies and investment banks from 1988 to 2008, we find that daily stock return correlation is a simple, robust, forward-looking, and timely systemic risk indicator. There is an increasing trend in stock return correlation among banks, whereas there is no obvious correlation trend among non-banks. We also disaggregate the stock returns into systematic and idiosyncratic components and find that the correlation increases are largely driven by the increases in correlations between banks’ idiosyncratic risks, which give rise to increasing systemic risk. Correlation spikes tend to predict or coincide with significant economic or market events, especially during the 2007–2008 financial crisis. Furthermore, we show that stock return correlations offer a perspective on the level of systemic risk in the financial sector that is not already captured by default correlations. Stock return correlations are not subject to data limitations or model specification errors that other potential systemic risk measures may face. Therefore, we recommend that regulators and businesses monitor daily stock return correlations among those large and highly leveraged financial institutions to track the level of systemic risk.

We compare six modeling methods for Loss Given Default (LGD). We find that non-parametric methods (regression tree and neural network) perform better than parametric methods both in and out of sample when over-fitting is properly controlled. Among the parametric methods, fractional response regression has a slight edge over OLS regression. Performance of the transformation methods (inverse Gaussian and beta transformation) is very sensitive to ε, a small adjustment made to LGDs of 0 or 1 prior to transformation. Model fit is poor when ε is too small or too large, although the fitted LGDs have strong bi-modal distribution with very small ε. Therefore, models that produce strong bi-model pattern do not necessarily have good model fit and accurate LGD predictions. Even with an optimal ε, the performance of the transformation methods can only match that of the OLS.

We conduct a thorough analysis on the role played by the unobserved systematic risk factor in default prediction. We find that this latent factor outweighs the observed systematic risk factors and can substantially improve the in-sample predictive accuracy at the firm, rating group, and aggregate levels. Thus it might be helpful to include the unobserved systematic risk factor when simulating portfolio credit losses. However, we also find that this factor only marginally improves out-of-sample model performance. Therefore, although the models we investigated all show reasonably good ability to rank order firms by default risk, accurate prediction of default rate remains challenging even when the unobserved systematic risk factor is considered.