What drives the liquidity premium in the Chinese stock market?

This paper examines the dynamics of the liquidity premium in the Chinese stock market by adopting a multivariate decomposition approach to measure the individual contributions of various driving forces of the premium (such as firm size, idiosyncratic volatility, and market liquidity betas). By employing a wide range of liquidity measures, we show that liquidity premium is generally significant in the Chinese stock market. Furthermore, this premium is increasing in recent years starting from 2011; this observation is different from the United States market, in which the premium has declined over the years. Moreover, the multivariate decomposition approach highlights several asset pricing factors as the main driving forces of the premium. Based on the Amihud liquidity measure, the decomposition approach indicates that the size factor contributes 45–65% to the liquidity premium. However, the measure based on turnover suggests that idiosyncratic volatility accounts for at least 60% of the liquidity premium. In contrast, the global market liquidity beta does not significantly contribute to the premium. However, there is some evidence that the local market liquidity beta has become more significant in its impact on the premium during the period from 2011 to 2015. Our results imply that the findings on the liquidity premium in the Chinese stock market could be sensitive to the liquidity measure used and period of analysis.     

The impact of non-normality,sample size and estimation technique on goodness-of-fit measures in structural equation modeling: evidence from ten empirical models of travel behavior

Ten empirical models of travel behavior are used to measure the variability of structural equation model goodness-of-fit as a function of sample size, multivariate kurtosis, and estimation technique. The estimation techniques are maximum likelihood, asymptotic distribution free, bootstrapping, and the Mplus approach. The results highlight the divergence of these techniques when sample sizes are small and/or multivariate kurtosis high. Recommendations include using multiple estimation techniques and, when sample sizes are large, sampling the data and reestimating the models to test both the robustness of the specifications and to quantify, to some extent, the large sample bias inherent in the χ ² test statistic.     

Multivariate dynamic intensity peaks-over-threshold models

We propose a multivariate dynamic intensity peaks-over-threshold model to capture extremes in multivariate return processes. The random occurrence of extremes is modeled by a multivariate dynamic intensity model, while temporal clustering of their size is captured by an autoregressive multiplicative error model. Applying the model to daily returns of three major stock indexes yields strong empirical support for a temporal clustering of both the occurrence and the size of extremes. Backtesting value-at-risk and expected shortfall forecasts shows that the consideration of clustering effects and of feedback between the magnitudes and the intensity of extremes results in better forecasts of risk.     

A comparison of the ratio of variances in distance-based and classical multivariate analysis

In multivariate analysis, the measure of variance accounted for plays a central role. In this paper, we show that an alternative approach, distance-based multivariate analysis, also yields solutions that can be summarized by a ratio of variances. For classical multivariate analysis, this ratio is equal to the variance accounted for (VAF) and in distance-based multivariate analysis it equals distance accounted for (DAF). We show that DAF in distance-based multivariate analysis can always be made higher than VAF in classical multivariate analysis. This property is illustrated for principal components analysis, multiple correspondence analysis, multiple regression, and analysis of variance.     

Dominance Statistics: A Simulation Study on the d Statistic

Quality &; Quantity - Cliff (1993) has proposed the use of a measure of effect size alternative to traditionalmean differences: δ {? = Pr(xi1 &;gt; xj2) - Pr(xi1 &;lt; xj2)}which,...     

Non-Gaussian models for CoVaR estimation

In this paper we show how to obtain estimates of CoVaR based on models that take into consideration some stylized facts about multivariate financial time series of equity log returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is captured by AR-GARCH dynamics of the Glosten-Jagannathan-Runkle (GJR) type, the other stylized facts are explained by non-Gaussian multivariate models and copula functions. We compare the different models in the period from January 2007 to March 2020. Our empirical study conducted on a sample of listed banks in the euro area confirms that, in measuring CoVaR, it is important to capture the time-varying dynamics of the volatility. Additionally, a correct assessment of the heaviness of the tails and of the dependence structure is needed in the evaluation of this systemic risk measure.     

Gaussian semiparametric estimation of multivariate fractionally integrated processes

This paper analyzes the semiparametric estimation of multivariate long-range dependent processes. The class of spectral densities considered is motivated by and includes those of multivariate fractionally integrated processes. The paper establishes the consistency of the multivariate Gaussian semiparametric estimator (GSE), which has not been shown in other work, and the asymptotic normality of the GSE estimator. The proposed GSE estimator is shown to have a smaller limiting variance than the two-step GSE estimator studied by Lobato [1999. A semiparametric two-step estimator in a multivariate long memory model. Journal of Econometrics 90, 129–153]. Gaussianity is not assumed in the asymptotic theory. Some simulations confirm the relevance of the asymptotic results in samples of the size used in practical work.     

An alternative formulation of Neyman’s smooth goodness of fit tests under composite alternatives

Summary We propose a natural extension of Neyman’s smooth goodness of fit tests under composite hypotheses. The components of our smooth tests are immediate analogues of the corresponding components in the completely specified null case. We show that, when testing for univariate normality, one of our smooth tests is similar to D’Agostino and Pearson’s (1973) statistic; when testing for multivariate normality, our smooth test for skewness is identical to Mardia’s (1970) measure of multivariate skewness; and, when testing for exponentiality, our simplest smooth test is equivalent to Greenwood’s (1946) statistic.     

New Evidence on Class Size Effects: A Pupil Fixed Effects Approach*

The impact of class size on student achievement remains a thorny question for educational decision makers. Meta‐analyses of empirical studies emphasize the absence of class‐size effects but detractors have argued against such pessimistic conclusions because many of the underlying studies have not paid attention to the endogeneity of class size. This article uses a stringent method to address the endogeneity problem using Trends in International Mathematics and Science Study data on 47 countries. We measure the class size effect by relating the difference in a student's achievement across subjects to the difference in his/her class size across subjects. This (subject‐differenced) within‐pupil achievement production function avoids the problem of the non‐random matching of children to specific schools, and to classes within schools. The results show a statistically significant negative effect of class size in 14 countries, but the effect size is small in most cases. Several robustness tests are carried out, including control for students’ subject‐specific ability and subject‐specific teacher characteristics, and correction for possible measurement error. Thus, our approach to addressing the endogeneity problem confirms the findings of meta‐analyses that find little support for class size effects. Additionally, we find that class size effects are smaller in countries with higher teacher quality.     

Local Economic Structure and Growth

Abstract

This paper tests how the local economic structure—measured by local sector specialization, competition and diversity—affects growth of manufacturing sectors. Most of the empirical literature assumes that in the long run more productive regions will attract more workers and use employment growth as a measure of local productivity growth. However, this approach is based on strong assumptions, such as those of national labour markets and homogeneous labour. This paper shows that if we relax these assumptions, regional adjusted wage growth is a better measure of productivity growth than employment growth. This measure is used in order to study regional growth in Portuguese regions between 1985 and 1994. Evidence is found of MAR externalities in some sectors and no evidence of Jacobs or Porter externalities in most of the sectors. These results are at odds with the findings for employment-based regressions, which show that regional concentration and the region's size have a negative effect in most of the sectors. It is also shown that simply using regional wage growth would overstate the effect of regional concentration and competition on long-run growth.     

Multivariate residual‐based finite‐sample tests for serial dependence and ARCH effects with applications to asset pricing models

In this paper, we propose several finite‐sample specification tests for multivariate linear regressions (MLR). We focus on tests for serial dependence and ARCH effects with possibly non‐Gaussian errors. The tests are based on properly standardized multivariate residuals to ensure invariance to error covariances. The procedures proposed provide: (i) exact variants of standard multivariate portmanteau tests for serial correlation as well as ARCH effects, and (ii) exact versions of the diagnostics presented by Shanken ( 1990 ) which are based on combining univariate specification tests. Specifically, we combine tests across equations using a Monte Carlo (MC) test method so that Bonferroni‐type bounds can be avoided. The procedures considered are evaluated in a simulation experiment: the latter shows that standard asymptotic procedures suffer from serious size problems, while the MC tests suggested display excellent size and power properties, even when the sample size is small relative to the number of equations, with normal or Student‐t errors. The tests proposed are applied to the Fama–French three‐factor model. Our findings suggest that the i.i.d. error assumption provides an acceptable working framework once we allow for non‐Gaussian errors within 5‐year sub‐periods, whereas temporal instabilities clearly plague the full‐sample dataset. Copyright © 2009 John Wiley & Sons, Ltd.     

Saving motives and multivariate precautionary premia

This paper extends the concept of precautionary premium to a multivariate setting so as to measure the intensity of the precautionary saving motive to protect against multivariate risks. This makes it possible to disentangle and to link the various motives of precautionary saving depending on the configuration of the risks the individual wants to protect against.     

Testing multivariate distributions in GARCH models

In this paper, we consider testing distributional assumptions in multivariate GARCH models based on empirical processes. Using the fact that joint distribution carries the same amount of information as the marginal together with conditional distributions, we first transform the multivariate data into univariate independent data based on the marginal and conditional cumulative distribution functions. We then apply the Khmaladze's martingale transformation (K-transformation) to the empirical process in the presence of estimated parameters. The K-transformation eliminates the effect of parameter estimation, allowing a distribution-free test statistic to be constructed. We show that the K-transformation takes a very simple form for testing multivariate normal and multivariate t-distributions. The procedure is applied to a multivariate financial time series data set.     

Nonparametric covariance estimation in multivariate distributions

The estimation problem of the unknown covariance matrix of a multivariate distribution with the known mean is studied under a matrix-valued quadratic loss function. The conditions on the sample sizes for the best unbiased estimator to have a smaller risk than the sample covariance matrix is established. The former estimator is completely (without exceptional sets of Lebesgue measure zero) characterized by its expectation in the class of all multivariate distributions with zero mean and finite fourth moments. Received: November 1998     

Surveillance of the mean behavior of multivariate time series

In this paper several cumulative sum (CUSUM) charts for the mean of a multivariate time series are introduced. We extend the control schemes for independent multivariate observations of crosier [ Technometrics (1988) Vol. 30, pp. 187–194], pignatiello and runger [ Journal of Quality Technology (1990) Vol. 22, pp. 173–186], and ngai and zhang [ Statistica Sinica (2001) Vol. 11, pp. 747–766] to multivariate time series by taking into account the probability structure of the underlying stochastic process. We consider modified charts and residual schemes as well. It is analyzed under which conditions these charts are directionally invariant. In an extensive Monte Carlo study these charts are compared with the CUSUM scheme of theodossiu [ Journal of the American Statistical Association (1993) Vol. 88, pp. 441–448], the multivariate exponentially weighted moving-average (EWMA) chart of kramer and schmid [ Sequential Analysis (1997) Vol. 16, pp. 131–154], and the control procedures of bodnar and schmid [ Frontiers of Statistical Process Control (2006) Physica, Heidelberg]. As a measure of the performance, the maximum expected delay is used.     

Top management successions: the choice between internal and external sources

There is no conclusive evidence on the firm's preference in appointments of top management. In this study four theoretical hypotheses on the likely source of appointment (internal vs. external) are examined using multivariate logistic regressions on a sample of 260 top management appointments in US firms. Two factors were found to have a significant effect: 1) Firm size – larger firms tend to appoint more from their internal sources, probably due to their informal commitment to their reservoir of internal talent; 2) power – the more power the firm is willing to delegate to its next manager, the more likely are external successions. Two other factors, the disposition of the former top manager (left/stayed with the firm) and firm's past performance, were found insignificant.     

Agency costs in the banking industry: An examination of ownership behavior,leverage and dividend policies

This paper examines agency theory arguments in the banking industry by analyzing the effect of four variables that proxy for agency costs—earnings volatility, managers' portfolio diversification losses, bank size, and standard deviation of bank equity returns—on the three financial policy variables of managerial stock ownership, leverage, and dividend yield. It is one of the first studies that examines the determination of financial policy variables, in light of agency concerns, in the banking industry. The study examines the largest 104 U.S. banks during the period 1985–1989. Evidence suggests that bank size and a measure of the managers' portfolio diversification opportunity set affect the bank's level of managerial stock ownership, leverage and dividends.     

Examination of the effects of the relationship marketing orientation on the company performance

The research objective is to examine empirically the relationship marketing and its successful implementation in Turkish Beverage Companies. Three scales were used to generate the data. Relationship marketing scale, environmental factors scale and company performance scale. Some of the variables in questionnaire used as control variables that reflect company-specific characters namely, company size, business type and sales volume. First the main effect of relationship marketing orientation on company performance was assessed, and then the moderating effect of environmental factors on the relationship between relationship marketing orientation and company performance was estimated by using multivariate techniques.     

Beta-product dependent Pitman–Yor processes for Bayesian inference

Multiple time series data may exhibit clustering over time and the clustering effect may change across different series. This paper is motivated by the Bayesian non-parametric modelling of the dependence between clustering effects in multiple time series analysis. We follow a Dirichlet process mixture approach and define a new class of multivariate dependent Pitman–Yor processes (DPY). The proposed DPY are represented in terms of vectors of stick-breaking processes which determine dependent clustering structures in the time series. We follow a hierarchical specification of the DPY base measure to account for various degrees of information pooling across the series. We discuss some theoretical properties of the DPY and use them to define Bayesian non-parametric repeated measurement and vector autoregressive models. We provide efficient Monte Carlo Markov Chain algorithms for posterior computation of the proposed models and illustrate the effectiveness of the method with a simulation study and an application to the United States and the European Union business cycle.     

Is small beautiful? Size effects of volatility spillovers for firm performance and exchange rates in tourism

This paper examines the size effects of volatility spillovers for firm performance and exchange rates with asymmetry in the Taiwan tourism industry. The analysis is based on two conditional multivariate models, BEKK–AGARCH and VARMA–AGARCH, in the volatility specification. Daily data from 1 July 2008 to 29 June 2012 for 999 firms are used, which covers the Global Financial Crisis. The empirical findings indicate that there are size effects on volatility spillovers from the exchange rate to firm performance. Specifically, the risk for firm size has different effects from the three leading tourism sources to Taiwan, namely USA, Japan, and China. Furthermore, all the return series reveal quite high volatility spillovers (at over 60%) with a one-period lag. The empirical results show a negative correlation between exchange rate returns and stock returns. However, the asymmetric effect of the shock is ambiguous, owing to conflicts in the significance and signs of the asymmetry effect in the two estimated multivariate GARCH models. The empirical findings provide financial managers with a better understanding of how firm size is related to financial performance, risk and portfolio management strategies that can be used in practice.