Trading volume and return volatility of Bitcoin market: evidence for the sequential information arrival hypothesis

2019 ◽  
Vol 14 (2) ◽  
pp. 377-418 ◽  
Author(s):  
Pengfei Wang ◽  
Wei Zhang ◽  
Xiao Li ◽  
Dehua Shen
2021 ◽  
Vol 66 (4) ◽  
pp. 517-534
Author(s):  
Serkan Samut ◽  
Rahmi Yamak

In this study, it was investigated whether the Covid-19 pandemic, which started to affect the world in early 2020, influenced the relationship between return volatility and trading volume in the cryptocurrency market. In the empirical part of the study, 40 cryptocurrencies were included in the analysis. The data were divided into two separate periods as before and during the pandemic. Two alternative estimators developed by Garman and Klass (1980) and by Rogers and Satchell (1991) were used to measure the return volatility of cryptocurrencies. With causality and simultaneous correlation analyses, it was determined that the sequential information arrival hypothesis was valid in the cryptocurrency market in the pre-pandemic period. In the pandemic period, the sequential information arrival hypothesis lost its effect and left its place to the mixture of distribution hypothesis.


2019 ◽  
Vol 8 (3) ◽  
pp. 48
Author(s):  
Kobana Abukari ◽  
Tov Assogbavi

Using weekly Egyptian stock exchange data on the 34 most active companies stretching from 2011 to 2017, this study finds that price changes Granger cause trading volume up to 8 weeks (lags), supporting the sequential information arrival model in the EGX. We also find a robust contemporaneously positive asymmetric relationship between price change and trading volume, confirming two well-documented characteristics of the price-volume relationship as well as two major adages of Wall Street: “it takes volume to move prices” and “volume in bull markets is heavier than volume in bear markets”. Overall, our results imply that although there is some sequential diffusion of information, the EGX’s efforts at improving its microstructure through initiatives such as the 2009 Presidential Degree on structure and governance, appear to have helped in improving instantaneous access to information – as exemplified by our evidence of strong contemporaneous positive price-volume relationship.


2020 ◽  
pp. 097215091986508
Author(s):  
Aritra Pan ◽  
Arun Kumar Misra

Bid-ask spread, along with profit, also encompass the impact of asymmetric information cost and order processing cost. Asymmetric information influences stock prices with varying degree of investors’ perception. Estimation of asymmetric information cost and its determinants have been explored significantly under low-frequency trading. The literature hardly attempts to study asymmetric information cost under high-frequency trading (HFT). Asymmetric information cost significantly influences bid-ask spread, and hence the nature of its impact under different market conditions needs to be analyzed under HFT. The study attempts to estimate asymmetric information cost in HFT and analyze its determinants under different industry sectors and market conditions. The study followed Affleck-Graves et al. (1994 , The Journal of Finance, 49(4), 1471–1488) model to estimate the asymmetric information cost using 5 minutes interval data for a period of 82 trading days. Information gets reflected in equity through the movement in price, variation in trading volume, and return volatility. The study has found share price, traded volume, return volatility and trading frequency as the major determinants of asymmetric information cost in different market conditions. The findings of the study have significant implications for market microstructure for trading, lowering information asymmetry in market and enhancing market quality.


2000 ◽  
Vol 03 (03) ◽  
pp. 467-472 ◽  
Author(s):  
GIULIA IORI

We propose a model with heterogeneous interacting traders which can explain the observed cross-correlation between stock return volatility and trading volume. Transaction costs are introduced which, by responding to price movements, create a feedback mechanism on future trading and generates volatility clustering.


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