Influence analysis of robust Wald-type tests

We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum likelihood estimators. An extensive study of their robustness properties is given though the influence functions as well as the chi-square inflation factors. It is theoretically established that the level and power of these robust tests are stable against outliers, whereas the classical Wald test breaks down. Some numerical examples confirm the validity of the theoretical results.

The 12-Item General Health Questionnaire (GHQ-12): Reliability,external validity and factor structure in the Spanish population

The purpose of this study was to analyze the internal consistency and the external and structure validity of the 12-Item General Health Questionnaire (GHQ-12) in the Spanish general population. A stratified sample of 1001 subjects, ages between 25 and 65 years, taken from the general Spanish population was employed. The GHQ-12 and the Inventory of Situations and Responses of Anxiety-ISRA were administered. A Cronbach’s alpha of .76 (Standardized Alpha: .78) and a 3-factor structure (with oblique rotation and maximum likelihood procedure) were obtained. External validity of Factor I (Successful Coping) with the ISRA is very robust (.82; Factor II, .70; Factor III, .75). The GHQ-12 shows adequate reliability and validity in the Spanish population. Therefore, the GHQ-12 can be used with efficacy to assess people’s overall psychological well-being and to detect non-psychotic psychiatric problems. Additionally, our results confirm that the GHQ-12 can best be thought of as a multidimensional scale that assesses several distinct aspects of distress, rather than just a unitary screening measure.

Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes

The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop a new multivariate asymmetric long memory volatility model, and discuss the associated asymptotic properties.

Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation guarantees the positivedefiniteness of the dynamic covariance matrix. The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988, 39(1-2), 69–104), especially for developing tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the likelihood function of RMESV-ALM, and the finite sample properties of the quasi-maximum likelihood estimator of the parameters are analysed. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions from returns to volatility and co-volatility.