This study provides new insights on the nexus between Tweet sentiments and stock price in China. Based on machine learning, we classify the Tweets from Weibo, a Twitter's variant in China into five sentiments of anger, disgust, joyful, sadness, and fear. Using wavelet analysis, we find close...

This study is the first attempt to investigate both the linear and non-linear Granger causality between wavelet transformed spot and futures oil prices. Our findings consistently indicate bidirectional causality between the spot and futures oil markets at different time scales, under linear and...

This paper uses the novel quantile coherency approach to examine the tail dependence network of 49 international stock markets in the frequency domain. We find that geographical proximity and state of market development are important factors in stock markets networks. Both the short- and...

The statistical estimate of the branching ratio η of the Hawkes model, when fitted to windows of mid-price changes, has been reported to approach criticality (η = 1) as the fitting window becomes large. In this study -- using price changes from the EUR/USD currency pair traded on the...

The endo-exo problem lies at the heart of statistical identi fication in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and...

Treasury stock and firm market value using a modified Tobin's q are modeled by using a firm utility preference function and a quadratic constraint function. The choice of the quadratic form is based on an econometric analysis of the relationship of q to T, the amount of treasury stock held by...

We aim to explore the interplay between ESG scores and assets characteristics, specifically focusing on volatility. We classify stocks on the basis of both high/low ESG and high/low ESG momentum and we evaluate ESG effects by measuring the distance between the 2 group distributions. The analysis...

We present a framework for modeling and estimating dynamics of variance and skewness from time-series data using a maximum likelihood approach assuming that the errors from the mean have a non-central conditional t distribution. We parameterize conditional variance and conditional skewness in an...