Admissible Clustering of Aggregator Components: A Necessary and Sufficient Stochastic Semi-Nonparametric Test for Weak Separability.

In aggregation theory, the admissibility condition for clustering together components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper deals with semi- nonparametric tests for weak separability. It introduces both a necessary and su¢ cient test, and a fully stochastic procedure allowing to take into account measurement error. Simulations show that the test performs well, even for large measurement errors.weak separability, quantity aggregation, clustering, sectors, index number theory, semi-nonparametrics

Tests of structural changes in conditional distributions with unknown changepoints

This paper focuses on a procedure to test for structural changes in the first two moments of a time series, when no information about the process driving the breaks is available. To approximate the process, an orthogonal Bernstein polynomial is used and testing for the null is achieved either by using an AICu information criterion, or a restriction test. The procedure covers both the pure discrete structural change and the continuous changes models. Running Monte-Carlo simulations, we show that the test has power against various alternatives.Structural changes, Bernstein polynomial, AICu.

A GARCH analysis of dark-pool trades

The ability to trade in dark-pools without publicly announcing trading orders, concerns regulators and market participants alike. This paper analyzes the information contribution of dark trades to the intraday volatility process. The analysis is conducted by performing a GARCH estimation framework where errors follow the generalized error distribution (GED) and two different proxies for dark trading activity are separately included in the volatility equation. Results indicate that dark trades convey important information on the intraday volatility process. Furthermore, the results highlight the superiority of the proportion of dark trades relative to the proportion of dark volume in affecting the one-step-ahead density forecas

An Omnibus Test to Detect Time-Heterogeneity in Time Series

A paraître dans Computational StatisticsIn this paper, we present a procedure that tests for the null of time-homogeneity of the first two moments of a time-series. Whereas the literature dedicated to structural breaks testing procedures often focuses on one kind of alternative, i.e. discrete shifts or smooth transition, our procedure is designed to deal with a broader alternative including i) discrete shifts, ii) smooth transition, iii) time-varying moments, iv) probability-driven breaks, v) GARCH or Stochastic Volatility Models for the variance. Our test uses the recently introduced maximum entropy bootstrap, designed to capture both time-dependency and time-heterogeneity. Running simulations, our procedure appears to be quite powerful. To some extent, our paper is an extension of Heracleous, Koutris and Spanos (2008).Cet article introduit une procédure pour tester l'homogénéité temporelle pour les deux premiers moments d'une série temporelle. Par rapport à la littérature existante, notre test considère une hypothèse alternative large incluant par exemple, i) des sauts discrets, ii) une transition lissée, iii) des moments temps-dépendants, iv) des modèles avec transition markovienne, v) des modèles GARCH où à volatilité stochastique pour la variance. La procédure que nous développons, utilise un ré-échantillonnage basé sur le maximum d'entropie et nous montrons, à l'aide de simulations de Monte-Carlo qu'elle est assez puissante. Notre papier étend ainsi l'approche de Heracleous, Koutris and Spanos (2008)

Detecting Performance Persistence of Hedge Funds : A Runs-Based Analysis

In this paper, we use nonparametric runs-based tests to analyze the randomness of returns and the persistence of relative returns of hedge funds. Runs tests are implemented on a universe of hedge extracted from HFR database over the period spanning January 2000 to December 2012. Our findings suggest that i) For about 80% of the funds, we fail to reject the null of randomness of returns, ii) A similar ...gure is found out when focusing on relative returns, iii) Hedge funds that do present clustering in their relative returns are mainly found within Event Driven and Relative Value strategies, iv) For relative returns, results vary with the benchmark nature (hedge or traditional). The paper also emphasizes that runs tests may be a useful tool for investors in their fund' s selection process

Investigating the Role of Real Divisia Money in Persistence-Robust Econometric Models

This paper investigates the causal relationships between real money and real activity. Whereas previous literature has mainly focused on simple-sum aggregates, we instead use Divisia ones, thus avoiding the so-called Barnett Critique. Standard Granger non-causality tests are implemented in two di¤erent frameworks: Fully Modied VARs (Phillips, 1995) and surplus-lag VARX models (Bauer and Maynard, 2012). These two environments allow modeling mixtures of I(0)/I(1) variables with possible cointegration without pretesting for ntegration nor for the dimension of the cointegration space. Moreover the latter method is also robust to various other forms of persistence such as local-to-unity processes, long memory/fractional integration, or unmodeled breaks-in-mean in the causal variables. By implementing the tests on di¤erent sub-samples identied by standard structural break tests, and using three di¤erent measures of money (DM4, DM4- and DM3), the tests suggest a unidirectional causality from activity to money. Moreover, from one period to another, the whole causal structure of the systems seem to change, as well as the stationarity of the series. At last, the two methodologies return similar results

Admissible Clustering of Aggregator Components: A Necessary and Sufficient Stochastic Semi-Nonparametric Test for Weak Separability

This is the author's accepted manuscript. The publisher's official version is available electronically from doi:10.1017/S1365100509090300In aggregation theory, the admissibility condition for clustering components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper introduces a new class of weak separability tests, which is seminonparametric. Such tests are both based on a necessary and sufficient condition and are fully stochastic, allowing to take into account measurement error. Simulations show that the tests perform well, even for large measurement errors

An Omnibus Test to Detect Time-Heterogeneity in Time Series

This paper focuses on a procedure to test for structural changes in the first two moments of a time series, when no information about the process driving the breaks is available. We model the series as a finite-order auto-regressive process plus an orthogonal Bernstein polynomial to capture heterogeneity. Testing for the null of time-invariance is then achieved by testing the order of the polynomial, using either an information criterion, or a restriction test. The procedure is an omnibus test in the sense that it covers both the pure discrete structural changes and some continuous changes models. To some extent, our paper can be seen as an extension of Heracleous et al. (Econom Rev 27:363-384, 2008).Structural changes; Bernstein polynomial; time-homogeneity

Admissible clustering of aggregator components: a necessary and sufficient stochastic semi-nonparametric test for weak separability

In aggregation theory, the admissibility condition for clustering together components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper deals with semi-nonparametric tests for weak separability. It introduces both a necessary and sufficient test, and a fully stochastic procedure allowing to take into account measurement error. Simulations show that the test performs well, even for large measurement errors

Admissible clustering of aggregator components: a necessary and sufficient stochastic semi-nonparametric test for weak separability

In aggregation theory, the admissibility condition for clustering together components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper deals with semi-nonparametric tests for weak separability. It introduces both a necessary and sufficient test, and a fully stochastic procedure allowing to take into account measurement error. Simulations show that the test performs well, even for large measurement errors