The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only – but substantially – when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.

No abstract

This paper is dedicated to recovery and residual value risks' modelling issues of automotive lease portfolios. First, loss-given-default distributions are estimated and compared for different samples based on risk drivers. Second, the residual value risk is approached through a resampling technique to provide one of the first empirical analysis on residual value losses in the automotive lease sector. Probability density function of losses and Value-at-risk measures are estimated on the basis of a private database comprising a unique set of 4828 individual automotive lease contracts issued between 1990 and 2001 by a major European financial institution. Then, a discussion is led in relation to the capital requirements related to residual value risk stemming from the Basel II Accord. As the greatest part of recovery risk is diversifiable, our conclusion is that a wider recognition of physical collateral in capital adequacy regulations should allow us to better reflect the relatively low-risk profile of automotive lease exposures.leasing, residual value risk, loss given default (LGD), Basel II Accord,

No abstract

Standard sector classification frameworks present drawbacks that might hinder portfolio manager. This paper introduces a new non-parametric approach to equity classification. Returns are decomposed into their fundamental drivers through Independent Component Analysis (ICA). Stocks are then classified according to the relative importance of identified fundamental drivers for their returns. A method is developed permitting the quantification of these dependencies, using a similarity index. Hierarchical clustering allows for grouping the stocks into new classes. The resulting classes are compared with those from the 2-digit GICS system for U.S. blue chip companies. It is shown that specific relations between stocks are not captured by the GICS framework. The method is applied on two different samples and tested for robustness. JEL: G11, G19