Revisiting Volume-Volatility Relationship: Evidence from India
10 Pages Posted: 19 Jan 2007
Date Written: August 25, 2006
Abstract
Positive relationship between trade volume and return volatility is a well-known empirical verified regularity in the financial research. Several studies examined what causes to volume-volatility to evolve and numerous theoretical explanations have been developed to predict/explore this relationship (see Karpoff (1987) and Board et al (1990) for a number of reasons why price-volume affiliation is positive). However, the recent literature provides evidence of revisiting the volume-volatility relationship as volatility and transaction counts relationship. So far no study examines the role of transactions frequency over and above volume in explaining the volatility; hence, the present study attempts to uncover the relevance of transaction counts for Indian stock market. Specifically, the study considers component stocks of Indian barometer indices, NSE Nifty and Nifty Junior, for the period 2005. In addition, study measures volatility by five minute intra day volatility apart from traditional absolute and squared price changes. Volume is measured as average trade size. The basic hypotheses that the number of transactions drives the volatility rather than the volume has been examined by the cross-sectional averages of Nifty & Nifty Junior stocks after running time series regressions.
Keywords: Volume, Volatility and Transaction Counts
JEL Classification: C10, C13, G10, G12
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
Time-Varying Conditional Covariances in Tests of Asset Pricing Models
-
By David M. Cutler, James M. Poterba, ...
-
Asymmetric Volatility and Risk in Equity Markets
By Geert Bekaert and Guojun Wu
-
Why Do Security Prices Change? A Transaction-Level Analysis of Nyse Stocks
By Ananth Madhavan and Mark Roomans
-
By Martin Eichenbaum, Lars Peter Hansen, ...
-
Heteroskedasticity in Stock Returns
By G. William Schwert and Paul J. Seguin
-
Estimating Models with Intertemporal Substitution Using Aggregate Time Series Data