We develop a structural bond valuation model to simultaneously capture liquidity and credit risk. Our model implies that renegotiation in financial distress is influenced by the illiquidity of the market for distressed debt. As default becomes more likely, the components of bond yield spreads attributable to illiquidity increase. When we consider finite maturity debt, we find decreasing and convex term structures of liquidity spreads. Using bond price data spanning 15 years, we find evidence of a positive correlation between the illiquidity and default components of yield spreads as well as support for downward-sloping term structures of liquidity spreads. Copyright 2006 by The American Finance Association.

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This paper presents an innovative way of animating actors at a high level based on the concept of synthetic vision. The objective is simple: to create an animation involving a synthetic actor automatically moving in a corridor avoiding objects and other synthetic actors. To simulate this behaviour, each synthetic actors uses a synthetic vision as its perception of the world and so as the unique input to its behavioural model. This model is based on the concept of displacement local automata (DLA), which is similar to the concept of a script for natural language processing. A DLA is an algorithm that can deal with a specific environment. Two DLAs, called follow‐the‐corridor and avoid‐the‐obstacle, are described in detail.

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In this paper we analyse recovery rates on defaulted bonds using the Standard and Poor's / PMD database for the years 1981-1999. Due to the speci…c nature of the data (observations lie within 0 and 1), we must rely on nonstandard econometric techniques. The recovery rate density is estimated nonparametrically using a beta kernel method. This method is free of boundary bias, and Monte Carlo comparison with competing nonparametric estimators show that the beta kernel density estimator is particularly well suited for density estimation on the unit interval. We challenge the usual market practice to model parametrically recovery rates using a beta distribution calibrated on the empirical mean and variance. This assumption is unable to replicate multimodal distributions or concentration of data at total recovery and total loss. We evaluate the impact of choosing the beta distribution on the estimation of credit Value-at-Risk.

In this paper we analyse recovery rates on defaulted bonds using the Standard and Poor's / PMD database for the years 1981-1999. Due to the speci…c nature of the data (observations lie within 0 and 1), we must rely on nonstandard econometric techniques. The recovery rate density is estimated nonparametrically using a beta kernel method. This method is free of boundary bias, and Monte Carlo comparison with competing nonparametric estimators show that the beta kernel density estimator is particularly well suited for density estimation on the unit interval. We challenge the usual market practice to model parametrically recovery rates using a beta distribution calibrated on the empirical mean and variance. This assumption is unable to replicate multimodal distributions or concentration of data at total recovery and total loss. We evaluate the impact of choosing the beta distribution on the estimation of credit Value-at-Risk.

Abstract. This paper provides the derivation of the hitting time density of an Ornstein-Uhlenbeck process to a flat boundary. The derivation relies on a change of measure approach and delivers an explicit formula. This formula is an amended expression of the result given in Leblanc and Scaillet (1998). It corresponds to the formula given by a time substitution approach when the boundary level coincides with the mean of the invariant measure. It can for example be used to price digital up-and-in credit spread options when the logarithm of the credit spread is assumed to follow an Ornstein-Uhlenbeck process.