Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptivemethod to choose the number of order statistics involved in an optimal way, balancing variance and biascomponents. Recently this has been achieved for the similar but somewhat less involved...

Econometric estimation using simulation techniques, such as the efficient method of moments, may betime consuming. The use of ordinary matrix programming languages such as Gauss, Matlab, Ox or S-plus will very often cause extra delay. For the Efficient Method of Moments implemented to...

We investigate the potential presence of time variation in the coefficients of the ''Fama regression'' for Uncovered InterestRate Parity. We implement coefficient constancy tests, rolling regression techniques, and stochastic coefficient modelsbased on state space modelling. Among six major US...

An abundance of high quality data sets requiring heavy tailed models necessitates reliablemethods of estimating the shape parameter governing the degree of tail heaviness.The Hill estimator is a popular method for doing this but its practical use isencumbered by several difficulties. We show...

In this paper a post-sample prediction test is derived forestimators based on the Efficient Method of Moments. The mainadvantage of this particular test over other stability tests isthat no time-consuming estimation of the structural parameters forthe post-sample is needed. The asymptotic...

This paper suggests a unified framework for testing the adequacy of anestimated GARCH model. Nothing more complicated than standard asymptotictheory is required. Parametric tests of no ARCH in standardized errors,symmetry, and parameter constancy are suggested. Estimating the alternativewhen the...

In this paper we test for (Generalized) AutoRegressive Conditional Heteroskedasticity [(G)ARCH] in daily data on 22 exchange rates and 13 stock market indices using the standard Lagrange Multiplier [LM] test for GARCH and a LM test that is resistant to patches of additive outliers. The data span...

We show that a sufficient condition for the identification ofall parameters of the censored regression model with astochastic and unobserved threshold is that the errors are jointlynormally distributed. Exclusion restrictions are not needed.