The seasonality of gold prices in China does the risk-aversion level matter?

This article aims to investigate the seasonality of gold prices at the Shanghai Gold Exchange over the 2002–2016 period. Our contributions rely in the distinction between risk-averse and risk-seeking investors regarding their investment strategies. The results show the existence of positive Monday and January effects. However, the Monday effect is more suitable to risk-seeking investors while the January effect is more suitable to risk-averse investors in bearish periods only. A robustness check shows that the Monday effect does not hold on gold futures prices. These results indicate the importance to consider the risk-aversion level of investors in seasonal investment strategies.     

Alternative estimation method of earnings growth rate for PEGR strategy

The ambiguous return pattern for the PEGR (the ratio of the stock’s price/earnings to its estimated earnings growth rate) strategy has been documented in literature for the US stock markets. As stock prices and earnings per share (EPS) are objective data, earnings growth rate, however, is estimated by analyst whose method partial explains the PEGR vague return pattern. The purpose of this study is not to deny or substitute analysts’ estimation, but rather, to provide a simple and popular method, log-linear regression model, to forecast the earnings growth rate (G), and examine whether the typical PEGR effect, such as PER (price/earnings ratio) or PBR (price/book ratio) effect, exists by using our alternative estimation method. Our evidence indeed shows that returns on the lowest PEGR portfolio not only dominate over all higher PEGR portfolios, but also beat the market with stochastic dominance (SD) analysis, which is consistent with our prediction. Our results, at least, imply that using the log-linear regression model to construct the PEGR-sorted portfolios can benefit investors and the model is also a good choice for analysts in their forecasting.     

中国与中东欧各国进口贸易的潜力分析——基于时变随机前沿引力模型的实证研究

运用时变随机前沿引力模型,基于2011—2018年中国与中东欧17国的进口贸易数据测度进口贸易潜力、技术效率及其影响因素。实证结果表明:中国与中东欧国家进口的贸易潜力较大,且不同国家之间呈现较大的不均衡性,波黑、克罗地亚、黑山可挖掘的进口潜力较大,匈牙利、保加利亚、斯洛伐克和波兰的进口效率较高。从影响因素看,中东欧国家的供给能力、中国的市场规模、“17+1”合作机制等因素推动了进口贸易的发展,而中国的供给能力、中东欧国家的市场规模、文化距离等因素对进口贸易有阻碍作用。经济自由度、基础设施质量、中欧班列等构成了影响进口效率的重要因素。     

World economic convergence: Does the estimation methodology matter?

An extant empirical literature produces evidence on economic convergence using methods that assume an underlying deterministic trend. Competing approaches that assume a stochastic trend, however, produce only limited evidence of economic convergence. In this paper we address this puzzling feature of the literature by providing a comprehensive analysis of economic convergence using three methodologies that cover all possible underlying assumptions: deterministic, stochastic, and combination trends. We also develop a method for an overall Stochastic Convergence Rate Index, that combines the outcomes of alternative stochastic tests and provides a single measure of the intensity of stochastic convergence. We consider 135 economies over the period 1980–2017. We find that economic convergence occurs at a global level through the formation of convergence clubs, and economic convergence emerges as a deterministic rather than a stochastic process. Tests that ignore deterministic trends tend to understate the evidence for convergence.     

Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of “green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019.