Intraday Volatility and Volume in China's Stock Index and Index Futures Markets

• Author: Bianxia Sun,Yusaku Nishimura
• Date: 01 December 2015
• Published date: 01 December 2015
• DOI: http://doi.org/10.1111/ajfs.12117

Intraday Volatility and Volume in China’s

Stock Index and Index Futures Markets*

Yusaku Nishimura

Institute of International Economy, University of International Business and Economics

Bianxia Sun**

Department of Financial Mathematics and Financial Engineering, South University of Science and Technol-

ogy of China

Received 22 January 2015; Accepted 04 October 2015

Abstract

This paper examines the effects of intraday trading volume on return volatility across China’s

stock index and index futures markets using 5-min intraday data. The periodic characteristics

of intraday data are considered and a FIEGARCH model is employed to allow for long mem-

ory properties of intraday volatility. We find that volume has significant positive influences

on volatility across the two markets, and that the impact of futures volume on spot volatility

is significantly stronger than the impact of spot volume on futures volatility. These findings

indicate a stronger information flow from the futures market to the spot market than vice

versa, which might result from current access restrictions on investors in China’s stock index

futures market.

Keywords Intraday volume; Intraday volatility; FIEGARCH model; Stock index futures;

Information transmission

JEL Classification: G10, G12, G14

1. Introduction

In recent years, high-frequency trading (HFT) has become a key component of the

language of the Chinese financial markets. Using computer algorithms to trade

securities rapidly, HFT has a long history in the financial markets of developed

*This paper benefited from the valuable comments offered by the two referees. For these

comments, we are extremely grateful. This research is financially supported by the Collabora-

tive Innovation Center for China’s Multinational Enterprise (NO. 201502YY002B) and the

SUSTC Research Fund (FRG-SUSTC1501A-21), to which we are all deeply grateful.

**Corresponding author: Bianxia Sun, Department of Financial Mathematics and Financial

Engineering, South University of Science and Technology of China, Room 313, Faculty Build-

ing 2, Nanshan District, Shenzhen 518055, China. Tel: (86)755-88018601, Fax: (86)755-

88018680, email: sunbx@sustc.edu.cn.

Asia-Pacific Journal of Financial Studies (2016) 44, 932–955 doi:10.1111/ajfs.12117

932 ©2015 Korean Securities Association

countries. In addition, its influences on market microstructure, information trans-

mission and asset price-forming mechanisms have attracted the interest of academi c

researchers and financial practitioners. The financial markets in China are unique in

that the T +1 trading system is implemented in the stock markets, whereas the

stock index futures markets adopt T +0 trading rules.

1

In addition to the impor-

tance of HFT that is based on its looming prevalence in the Chinese securities mar-

kets –and particularly with respect to its popularity in the futures markets –

studying the intraday information transmission between the Chinese stock index

and the index futures markets has both academic and practical significance.

Price movements and volume changes reflect the information flow in financial

markets; thus, exploring the relationship between return volatility and trading vol-

ume can reveal the information transmission mechanism among various markets.

Indeed, volatility-volume studies represent one of the most popular segments in the

financial market research field, and contemporaneous or lead-lag relationships

between these measures are frequently investigated.

Before the use of high-frequency data in financial studies in the 21st century,

low-frequency –typically daily –financial data were examined in the literature.

Some of this research focuses on the volatility-volume relationship in contemporary

periods. Karpoff (1987) reviews numerous papers studying volatility and volume

and notes a positive correlation between the two market variables. Lamoureux and

Lastrapes (1990) initially apply a GARCH model to study 20 stocks traded on the

NYSE and find that stock return volatility is positively affected by trading volume.

Among others, Najand and Yung (1991), Sharma et al. (1996), and Daigler and

Wiley (1999) use different models in different financial markets, including futures

markets, and reach the same basic conclusion regarding the positive influence of

trading volume on return volatility.

Other studies target the lead-lag relationship between volatility and trading vol-

ume. Blume et al. (1994) investigate the informational role played by volume and

argue that it may have predictive power in forecasting price movements over the

short run. Exploring a broad dataset from nine of the largest stock exchanges

worldwide, Chen et al. (2001) examine the Granger causali ty relationship between

the two information variables and also find a positive association between return

variance and lagged trading volume.

Since the early days of the 21st century, with the rapid development of high-fre-

quency data storage and processing, researchers have begun to employ high-fre-

quency data to explore intraday information transmission in the financial markets.

Darrat et al. (2003) use the 5-min intraday data of all the constituent stocks of the

Dow Jones Industrial Average Index (DJIA) and find similar positive results

1

Here, the term T +1 for the Chinese stock markets means that the stock shares bought on

trading day T are only available for sale at or after trading day T +1. For Chinese futures

markets, the term T +0 indicates that the positions opened at day T can be covered on the

same day.

China’s Stock Spot and Futures Markets

©2015 Korean Securities Association 933