We derive distribution free tests based on the Maximum Entropy densities to test the null hypotheses of symmetry and normality. The proposed tests are derived from maximizing the differential entropy subject to moment constraints. By exploiting the equivalence between Maximum Entropy and Maximum...

In this paper we study the finite sample behavior of the Hill estimator under α-stable distributions. Using large Monte Carlo simulations we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small length. Utilizing our results, we introduce...

• It is not widely emphasized in the literature that derivatives are complex random quantities which should, by custom, be characterized by their probability density functions. • It is understood that Black-Scholes style of derivatives pricing represents an expected value, i.e. the...

Spot electricity prices are very volatile, particularly due to the fact that electricity cannot be economically stored and requires immediate delivery. However, the inability to store electricity means that fluctuations in demand and supply are often transmitted directly into spot prices of...

To their credit, empirical legal scholars try to live up to the highest methodological standards from the social sciences. But these standards do not always match the legal research question. This paper focuses on normative legal argument based on empirical evidence. Whether there is a normative...

We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set...

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

In this paper we consider the ducial argument of Fisher from the point of view of constructing e cient approximations of (or importance distributions to) posterior distributions in econometric models with intractable likelihood functions. The importance or proposal distributions are used along...

In this paper we take up Bayesian inference in multivariate stable distributions through innovative multivariate stable copulae. The problem that the characteristic function is defined through a difficult object, the spectral measure is completely bypassed by our approach. The new methods are...

In this paper we take up Bayesian inference in general multivariate stable distributions. We exploit the representation of Matsui and Takemura (2009) for univariate projections, and the representation of the distributions in terms of their spectral measure. We present e cient MCMC schemes to...