Christian de Peretti

Empirical test of the efficiency of the UK covered warrants market: Stochastic dominance and likelihood ratio test approach

This paper first examines the efficiency of the UK covered warrants market by adopting a stochastic dominance (SD) approach to examine market efficiency. Our empirical analyses reveal that neither covered warrants nor the underlying shares stochastically dominate each other, which implies that the UK covered warrants market is efficient. To complement the SD results, the likelihood ratio (LR) test statistic is also employed to evaluate market efficiency, which also offers consistent results. The empirical evidence serves as a reference for market participants who are interested in covered warrants and also contributes to the growing body of academic knowledge on this new financial instrument.

Is the Consumption–Income Ratio Stationary? Evidence from Linear and Non‐Linear Panel Unit Root Tests for OECD and Non‐OECD Countries

This paper applies recently developed heterogeneous nonlinear and linear panel unit root tests that account for cross-sectional dependence to 24 OECD and 33 non-OECD countries’ consumption-income ratios over the period 1951–2003. We apply a recently developed methodology that facilitates the use of panel tests to identify which individual cross-sectional units are stationary and which are nonstationary. This extends evidence provided in the recent literature to consider both linear and nonlinear adjustment in panel unit root tests, to address the issue of cross-sectional dependence, and to substantially expand both time-series and cross sectional dimensions of the data analysed. We find that the majority (65%) of the series are nonstationary with slightly fewer OECD countries’ (61%) series exhibiting a unit root than non-OECD countries (68%).

Graphical methods for investigating the finite-sample properties of confidence regions

In the literature, there are not satisfactory methods for measuring and presenting the performance of confidence regions. In this paper, techniques for measuring effectiveness of confidence regions and for the graphical display of simulation evidence as regards the coverage and effectiveness of confidence regions are developed and illustrated. Three types of figures are discussed: called coverage plots, coverage discrepancy plots, and coverage effectiveness curves, that permits to show the “true” effectiveness, rather than a spurious nominal effectiveness. We prove that when simulations are run to compute the coverage for only one confidence level, which is usually done in the literrature for classical presentations in tables, all the information useful for computing the coverage for all the levels is present. Thus, there is absolutely no loss of computing time by using this method, whereas it provides more information than the corresponding tabular presentations. These figures are used to illustrate the finite sample properties of autoregressive parameter confidence regions in the context of AR(1) processes. Particularly, asymptotic, percentile, and percentile-t confidence intervals, as well as confidence intervals based on inverting bootstrap tests are presented and commented. Monte Carlo results assessing the performance of these confidence intervals for various situations are also presented. We show that classical confidence intervals have very poor performances, even the percentile-t interval, whereas confidence intervals based on inverting bootstrap tests have quite satisfactory performance. An application is made on stock market indices.

An artificial neural network based heterogeneous panel unit root test in case of cross sectional independence

In this paper we propose an artificial neural network (ANN) based panel unit root test, extending [1] neural test to a dynamic heterogeneous panel context, and following the [2] panel methodology. New asymptotic results are obtained both for the individual ANN-t test statistics for unit root, and the panel unit root test statistic. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided.