A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was...

Using more than two years of daily interest rate cap price data, this paper provides a systematic documentation of a volatility smile in cap prices. We find that Black (1976) implied volatilities exhibit an asymmetric smile (sometimes called a sneer) with a stronger skew for in-the-money caps...

This paper deals with nonparametric inference for second order stochastic dominance of two random variables. If their distribution functions are unknown they have to be inferred from observed realizations. Thus, any results on stochastic dominance are in uenced by sampling errors. We establish...

The diffusion of innovations is an important process and its models have applications in many fields, with particular relevance in technological forecast. The logistic equation is one of most important models in this context. Extensions of this approach as the Lotka-Volterra model have been...

Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional correlations processes, although with the drawback, when the number of financial returns series considered increases, that the parameterizations entail too many parameters.In general, the...

We revisit the question whether U.S. fiscal policy in the pre-Volcker period was active or passive. To determine the policy stance, we estimate a DSGE model with monetary and fiscal policy interactions employing a sequential Monte Carlo algorithm (SMC) for posterior evaluation. In contrast to...

Abstract only: Today’s data analysts and modellers are in the luxurious position of being able to more closely describe, estimate, predict and infer about complex systems of interest, thanks to ever more powerful computational methods but also wider ranges of modelling distributions. Mixture...

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate...

ENGLISH ABSTRACT: Life insurance and pension funds offer a wide range of products that are invested in a mix ofassets. These portfolios (II), underlying the products, are rebalanced back to predetermined fixedproportions on a regular basis. This is done by selling the better performing assets...

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the...