On Testing for the Nullity of Some Skewness Coefficients

Three tests for the skewness of an unknown distribution are derived for iid data. They are based on suitable normalization of estimators of some usual skewness coefficients. Their asymptotic null distributions are derived. The tests are next shown to be consistent and their power under some sequences of local alternatives is investigated. Their finite sample properties are also studied through a simulation experiment, and compared to those of the √ b ₂-test.     

Nonparametric efficiency analysis: A multivariate conditional quantile approach

This paper focuses on nonparametric efficiency analysis based on robust estimation of partial frontiers in a complete multivariate setup (multiple inputs and multiple outputs). It introduces α-quantile efficiency scores. A nonparametric estimator is proposed achieving strong consistency and asymptotic normality. Then if α increases to one as a function of the sample size we recover the properties of the FDH estimator. But our estimator is more robust to the perturbations in data, since it attains a finite gross-error sensitivity. Environmental variables can be introduced to evaluate efficiencies and a consistent estimator is proposed. Numerical examples illustrate the usefulness of the approach.     

Stock price synchronicity to oil shocks across quantiles: Evidence from Chinese oil firms

This paper investigates behaviour of stock price synchronicity to oil shocks across quantiles for Chinese oil firms. The spillover effects of the oil market on a firm are segregated into firm-specific and market-wide information. First, our results report a higher level of synchronicity by dynamic conditional correlations than by R-square since the former better captures dynamic linear dependence. Second, we find strong evidence of size effect. In particular, stock price synchronicity is generally higher in large-cap firms than in small-cap ones. Oil shocks affect synchronicity in the upper quantiles differently based on firm size. Third, we also find that synchronicity responds to oil shocks significantly in extreme low quantiles, implying that shocks in the oil market are transmitted to Chinese oil firms via firm-specific information. Finally, we determine that oil shocks have little or no immediate impact on stock price synchronicity; instead, cumulative lagged effect is evident. This evidence highlights the lagging effect of spillover of oil shocks on Chinese oil firms.     

International spillovers of U.S. monetary uncertainty and equity market volatility to China’s stock markets

Due to the global economy that is currently being increasingly integrated and liberalized, the cross-country transmission of U.S. monetary policy surprises has become a critical issue attracting scholarly attention. This research thus extends the existing literature by assessing the causal linkages among U.S. monetary policy uncertainty (USMPU), equity market volatility, and China’s stock price index over the period from January 1994 to August 2021. We apply Granger causality in quantile analysis to explore the relationships in each quantile of the distribution in a comprehensible manner. The results indicate that equity market volatility and China’s stock price dynamics play little role in affecting USMPU. We also find that only greater changes in both positive monetary policy uncertainty and stock prices lead to changes in equity market volatility. Furthermore, fluctuations in monetary policy uncertainty and equity market volatility in the United States Granger-cause China’s stock prices. Knowing such causality results could prevent market participants from adopting a one-size-fits-all strategy.     

Oil price shocks,geopolitical risks,and green bond market dynamics

This research explores the causal relation among oil price, geopolitical risks, and green bond index in the United States from December 2013 to January 2019. Unlike the conventional linear model specification used in earlier works, we evaluate causal relations based on Granger-causality in quantile analysis. Our empirical results reveal unidirectional Granger-causality from geopolitical risk to oil price at the extreme quantiles. We also observe a significant bi-directional causality from oil price to green bond index for the lower quantiles. Findings also reveal causality from geopolitical risk to green bond index in the lower quantiles of the distribution. Therefore, knowledge of these causal relationships can help policy makers to evaluate and implement effective policies to prevent sudden and substantial oil price shocks and geopolitical risk.     

AN APPROXIMATE CONFIDENCE INTERVAL FOR THE DIFFERENCE BETWEEN QUANTILES IN A BIO-MEDICAL PROBLEM

In this note it is described how two distributions arising in a bio-medical investigation are compared by means of a confidence interval for the difference of appropriate quantiles. It is briefly indicated how an approximation for such a confidence interval is derived.     

A DISTRIBUTION–FREE CONFIDENCE INTERVAL FOR THE DIFFERENCE BETWEEN QUANTILES WITH CENSORED DATA

Abstract. In the context of a two–sample problem, a confidence interval for the difference of appropriate quantiles of the two survival distributions is described. This method is especially useful when the data include some right–censored observations. A relevant mathematical result is proved.     

The Determinants of Entry are not Independent of Start-up Size: Some Evidence from Spanish Manufacturing

In this paper we explore the determinants of firm start-up size of Spanish manufacturing industries. The industries’ barriers to entry affect the ability of potential entrants to enter the markets and the size range at which they decide to enter. In order to examine the relationships between barriers to entry and size we applied the quantile regression techniques. Our results indicate that the variables that characterize the structure of the market, the variables that are related to the behaviour of the incumbent firms and the rate of growth of the industries generate different barriers depending on the initial size of the entrants. JEL classifications: L110, L600.     

Multinational Companies and Entrant Start-up Size: Evidence from Quantile Regressions

This paper analyses the impact of foreign multinationals on the start-up size ofdomestic entrants in Irish manufacturing industries. We use the regression quantileestimator, which allows us to take account of the heterogeneity of entrants' start-upsize. Estimation results show that the presence of foreign multinationals in a sectorimpacts negatively on the start-up size of domestic entrants of all sizes in modernsectors and on the start-up size of large domestic entrants in traditional sectors. Thisnegative effect may be attributed to competition of domestic plants with foreignmultinationals and other domestic plants in the final goods and/or the intermediategoods market.