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Articles 31 - 35 of 35
Full-Text Articles in Econometrics
Market Segmentation And Information Values Of Earnings Announcements: Some Empirical Evidence From An Event Study On The Chinese Stock Market, Y. Gao, Yiu Kuen Tse
Market Segmentation And Information Values Of Earnings Announcements: Some Empirical Evidence From An Event Study On The Chinese Stock Market, Y. Gao, Yiu Kuen Tse
Research Collection School Of Economics
This paper investigates the trading activities of two distinct classes of shareholders, namely, the Chinese domestic investors and the foreign investors in the segmented Chinese A- and B-share markets, respectively. We conduct an event study on the annual earnings announcements based on two different accounting standards: International accounting standards (IAS) and PRC generally accepted accounting principles (PRC GAAP). The earnings announcements based on IAS and PRC GAAP are value relevant. The investors in the B-share market react to both the IAS and PRC GAAP earnings announcements, while the investors in the A-share market pay more attention to the PRC GAAP …
A Small-Sample Overlapping Variance-Ratio Test, Yiu Kuen Tse, K. W. Ng, Xibin Zhang
A Small-Sample Overlapping Variance-Ratio Test, Yiu Kuen Tse, K. W. Ng, Xibin Zhang
Research Collection School Of Economics
The null distribution of the overlapping variance-ratio (OVR) test of the random-walk hypothesis is known to be downward biased and skewed to the right in small samples. As shown by , the test under-rejects the null on the left tail seriously when the sample size is small. This property adversely affects the applicability of the OVR test to macroeconomic time series, which usually have rather small samples. In this paper, we propose a modified overlapping variance-ratio statistic and derive its exact mean under the normality assumption. We propose to approximate the small-sample distribution of the modified statistic using a beta …
Empirical Characteristic Function Estimation And Its Applications, Jun Yu
Empirical Characteristic Function Estimation And Its Applications, Jun Yu
Research Collection School Of Economics
This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A …
Deviance Information Criterion For Comparing Stochastic Volatility Models, Andreas Berg, Renate Meyer, Jun Yu
Deviance Information Criterion For Comparing Stochastic Volatility Models, Andreas Berg, Renate Meyer, Jun Yu
Research Collection School Of Economics
Bayesian methods have been efficient in estimating parameters of stochastic volatility models for analyzing financial time series. Recent advances made it possible to fit stochastic volatility models of increasing complexity, including covariates, leverage effects, jump components, and heavy-tailed distributions. However, a formal model comparison via Bayes factors remains difficult. The main objective of this article is to demonstrate that model selection is more easily performed using the deviance information criterion (DIC). It combines a Bayesian measure of fit with a measure of model complexity. We illustrate the performance of DIC in discriminating between various different stochastic volatility models using simulated …
Selection Correction And Sensitivity Analysis For Ordered Treatment Effect On Count Response, Myoung-Jae Lee
Selection Correction And Sensitivity Analysis For Ordered Treatment Effect On Count Response, Myoung-Jae Lee
Research Collection School Of Economics
In estimating the effect of an ordered treatment [tau] on a count response y with an observational data where [tau] is self-selected (not randomized), observed variables x and unobserved variables [epsilon] can be unbalanced across the control group ([tau] = 0) and the treatment groups ([tau] = 1, . . . , J). While the imbalance in x causes 'overt bias' which can be removed by controlling for x, the imbalance in [epsilon] causes 'covert (hidden or selection) bias' which cannot be easily removed. This paper makes three contributions. First, a proper counter-factual causal framework for ordered treatment effect on …