An Empirical Study of Price-Volume Relation: Contemporaneous Correlation and Dynamics Between Price Volatility and Trading Volume in the Hong Kong Stock Market.
Business & Finance, Personal Finance, Investing, Finance & Investing, Investments & Securities
| Author: | Wing Ho NG, Kam Hung LEUNG | ISBN: | 9781533764133 |
| Publisher: | ABoy'sConundrum | Publication: | June 2, 2016 |
| Imprint: | Language: | English |
| Author: | Wing Ho NG, Kam Hung LEUNG |
| ISBN: | 9781533764133 |
| Publisher: | ABoy'sConundrum |
| Publication: | June 2, 2016 |
| Imprint: | |
| Language: | English |
Abstract
This paper investigates the contemporaneous correlation and dynamic relation between absolute price change (***|******Δ******P|***) and trading volume (V) in the Hong Kong stock market. With daily data of thirty-eight blue-chips from 2011 to 2015, evidence of positive correlation is found. Besides, the statistical results from Granger-causality tests indicate that the existence of the dynamic relation is very probable. The existence of different directions of causality may also be very probable, which depends on the use of different number of lagged values. Regarding the effect of earnings announcements, it may suggest that we can be more confident to use the stochastic time series model based on G-causality to predict |ΔP| or V when there is less information flowing into the market.
Abstract
This paper investigates the contemporaneous correlation and dynamic relation between absolute price change (***|******Δ******P|***) and trading volume (V) in the Hong Kong stock market. With daily data of thirty-eight blue-chips from 2011 to 2015, evidence of positive correlation is found. Besides, the statistical results from Granger-causality tests indicate that the existence of the dynamic relation is very probable. The existence of different directions of causality may also be very probable, which depends on the use of different number of lagged values. Regarding the effect of earnings announcements, it may suggest that we can be more confident to use the stochastic time series model based on G-causality to predict |ΔP| or V when there is less information flowing into the market.
