The global functional brain network (graph) is more suitable for characterizing brain states than local analysis of the connectivity of brain regions. Therefore, graph-theoretic approaches are the natural methods to study the brain. However, conventional graph theoretical analyses are limited due to the lack of formal statistical methods for estimation and inference for random graphs. For examp...

This paper analyzes the impact of model complexity on the net present value distribution and the expected default probability of equity investments in project finance. Model complexity is analyzed along two dimensions: simulation complexity and forecast complexity. We aim to identify model elements which are crucial for the valuation of project finance in practice. First, we present a simulatio...

the standard Gaussian copula model yet preserve tractability and computational efficiency. In one extension, we randomize recovery rates, explicitly allowing for the empirically well-established effect of inverse correlation between recovery rates and default frequencies. In another extension, we build into the model random systematic factor loadings, effectively allowing default correlations t...

We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correlation. The test is a direct application of Bayesian techniques for variable selection in regression models. The test is easy to apply and yields practical advantages that the standard frequentist tests lack; in particular, the Bayesian test can quantify evidence in favor of the null hypothesis and...

Borrowers with large mortgages relative to their home values are more likely to default. This paper asks whether this correlation is due to moral hazard—larger balances causing borrowers to default— or adverse selection—ex-ante risky borrowers choosing larger loans. To separate these information asymmetries, I exploit a natural experiment resulting from (i) the unique contract structure of Opti...

A new methodology for incorporating LGD correlation effects into the Basel II risk weight functions is introduced. This methodology is based on modelling of LGD and default event with a single loss variable. The resulting formulas for capital charges are numerically compared to the current proposals by the Basel Committee on Banking Supervision.

We study the effect of risk aversion on the valuation of credit derivatives. Using the technology of utility-indifference valuation in intensity-based models of default risk, we analyze resulting yield spreads for single-name defaultable bonds, and a simple representative two-name credit derivative. The impact of risk averse valuation on prices and yield spreads is expressed in terms of effecti...

A new methodology for incorporating LGD correlation effects into the Basel II risk weight functions is introduced. This methodology is based on modelling of LGD and default event with a single loss variable. The resulting formulas for capital charges are numerically compared to the current proposals by the Basel Committee on Banking Supervision.