In recent years multivariate models for asset returns have received much attention, in particular this is the case for models with time varying volatility. In this paper we consider models of this class and examine their potential when it comes to option pricing. Specifically, we derive the risk...

While stochastic volatility models improve on the option pricing error when compared to the Black-Scholes-Merton model, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant negative skewness and time varying higher order...

This research studies the perception of the risks associated with impaired driving-probability of being apprehended or of having an accident-and the relation between the perception of risks and driving behavior. The most important determinants of perceptual biases are age, an accumulation of...

The contingent claims analysis of the firm financing often presents a debt renegotiation game with a passive bank which does not use strategically its capability to force liquidation, contrary towhat is observed in practice. The first purpose of this paper is to introduce more strategic bank...

This paper proposes tests for stochastic dominance in mobility based on the empirical likelihood ratio. Two views of mobility are considered, either based on measures of absolute mobility or on transition matrices. First-order and second-order dominance conditions in mobility are first derived,...

We propose to compute the implied expected returns from several candidate mean-variance efficient portfolios, exploiting the fundamental relation between the expected returns, covariance matrix and the corresponding set of mean-variance efficient portfolios. Over the 1987-2012 period and for the...

In this paper, we investigate the information content of implied probabilities (Back and Brown, 1993) to improve estimation in unconditional moment conditions models. We propose and evaluate two 3-step euclidian empirical likelihood estimators and their bias-correction versions for weakly...

We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g., nonnegative) or completely bounded (e.g., in the unit interval). In addition, the variables may have a point mass. We...

In this paper we estimate density functions for positive multivariate data. We propose a semiparametric approach. The estimator combines gamma kernels or local linear kernels, also called boundary kernels, for the estimation of the marginal densities with semiparametric copulas to model the...

In this paper, we provide an overview of approaches used to model income distribution and poverty in CGE models. CGE models have started to use income distribution functional forms such as the lognormal, Pareto, beta distribution and Kernel non-parametric methods to apply GFT poverty indices....