We examine the relationship between the risk premium on the S&P500 index total return and its conditional variance. We propose a new semiparametric model in which the conditional variance process is parametric, while the conditional mean is an arbitrary function of the conditional variance. For...

We investigate a class of semiparametric ARCH(∞) models that includes as a special case the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible function form with regard to the 'news impact' function. We propose an...

After the seminal paper of Jarrow and Rudd (1982), several authors have proposed to use different statistical series expansion to price options when the risk-neutral density is asymmetric and leptokurtic. Amongst them, one can distinguish the Gram-Charlier Type A series expansion (Corrado and...

Volatility risk premia compensate agents for holding assets whose payoffs correlate with times of high return variation. This paper takes a structural approach to explain the cross-section of volatility risk premia of stocks using a Lucas orchard with heterogeneous beliefs, stochastic...

Several authors have proposed series expansion methods to price options when the risk-neutral density is asymmetric and leptokurtic. Among these, Corrado and Su (1996) provide an intuitive pricing formula based on a Gram-Charlier Type A series expansion. However, their formula contains a...

With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an...

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series...

Inequality measures are often used to summarise information about empirical income distributions. However , the resulting picture of the distribution and of the changes in the distribution can be severely distorted if the data are contaminated. The nature of this distortion will in general...

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from different families be in dependent. We establish consistency and asymptotic...