The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility

By Gong-meng Chen, Michael Firth, Oliver M. Rui. 2001

387 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ebeac89ad9304bdbb9f8d456c048dcc081fd45ea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. E. Girard
2. Jun Yang
3. S. Leng
4. Chia-Chang Chuang
5. Lili Li
6. Meihua Yu
7. Suk-Joong Kim
8. Rita Biswas

In Finance

1. E. Girard
2. Jun Yang
3. S. Leng
4. Chia-Chang Chuang
5. Lili Li
6. Meihua Yu
7. Suk-Joong Kim
8. Rita Biswas

Citations in the corpus

• Causality in quantiles and dynamic stock return–volume relations (2009, methodology): “ Chen, Firth, and Rui (2001) , and Lee and Rui (2002).”
• Information leadership in the advanced Asia-Pacific stock markets: Return, volatility and volume information spillovers from the US and Japan (2005, background): “In addition, dynamic causal relationships are reported in Lee and Rui (2002), Chen, Firth and Rui (2001) and Chordia and Swaminathan (2000). 2 Bae and Karolyi (1994), however, report that the degree of market linkages between the U.”
• Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression (2016, background): “[20] find a causal link between volume and stock indexes in some countries.”
• Trading Volume and Market Volatility: Developed versus Emerging Stock Markets (2007, positive): “” This is the Mixture of Distributions Hypothesis (MDH) used by Clark (1973) and more recently by Lamoureux and Lastrapes (1990), among others, to link trading volume and stock price volatility.”
• Order book approach to price impact (2003, methodology): “A technical alternative to the calculation of correlation functions would be Granger causality tests using a VAR analysis (Chen et al. 2001) or a semi-nonparametric estimate of the joint density of price change and volume (Gallant et al. 1992).”
• Can volume predict Bitcoin returns and volatility? A quantiles-based approach (2017, neutral): “Practically, if the transaction volume in the Bitcoin market has a predictive power for its returns, this suggests that practitioners will be able to construct volume-based strategies to increase profits (Chen et al., 2001).”