Volatility index and the return–volatility relation: Intraday evidence from Chinese options market

• Author: Xiaoli Yu,Jupeng Li,Xingguo Luo
• Date: 01 November 2019
• DOI: http://doi.org/10.1002/fut.22012
• Published date: 01 November 2019

Received: 30 March 2019

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Accepted: 2 April 2019

DOI: 10.1002/fut.22012

RESEARCH ARTICLE

Volatility index and the return–volatility relation: Intraday

evidence from Chinese options market

Jupeng Li

1

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Xiaoli Yu

2

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Xingguo Luo

3

1

Shanghai Stock Exchange, Shanghai,

China

2

School of Economics, Zhejiang

University, Hangzhou, China

3

School of Economics and Academy of

Financial Research, Zhejiang University,

Hangzhou, China

Correspondence

Xingguo Luo, School of Economics and

Academy of Financial Research, Zhejiang

University, 310027 Hangzhou, China.

Email: xgluo@zju.edu.cn

Funding information

National Natural Science Foundation of

China, Grant/Award Number: 71771199;

Fundamental Research Funds for the

Central Universities

Abstract

We use unique intraday data to investigate the validity of the Shanghai Stock

Exchange’s the revised Chinese implied volatility index (iVX). We find that iVX

is an effective barometer for the underlying exchange‐traded fund (ETF) market

and can be used as a valid “fear index”when there is anxiety over large drops.

Furthermore, we use robust quantile regressions and document the asymmetric

relation between returns and iVX changes. We also show that behavioral

theories offer better explanations for this asymmetric relation than do

fundamental theories. More important, we examine the role of iVX in selecting

trading strategies.

KEYWORDS

intraday data, iVX, return–volatility relation, SSE 50 ETF

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INTRODUCTION

The monthly trading volume of the Shanghai Stock Exchange (SSE) 50 ETF options increased from 232,508 to 38.57 million

from February 2015 to February 2019.

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It is meaningful to establish a benchmark volatility index for risk management in

both the stock and stock options markets. In June 2015, the SSE published the Chinese implied volatility index (iVIX), which

is a VIX‐like measure of the stock market’s expectation of volatility implied by SSE 50 ETF options. Later, in November 2016,

the the revised Chinese implied volatility index (iVX) was released. To the best of our knowledge, there has been little

research on the newly released Chinese volatility index. One exception is a study by Zheng, Jiang, and Chen (2017), in which

they find that daily iVIX is positively related to the price of the SSE 50 ETF. They argue that iVIX should be referred to as the

“greed index”rather than the “fear index,”a term often used to describe VIX (Whaley, 2000).

Meanwhile, there is an ongoing debate over explanations for the asymmetric return–volatility relation, in which volatility

responds more intensively to negative return shocks than to positive ones. Taking advantage of high‐frequency VIX data,

Talukdar, Daigler, and Parhizgari (2017) document that behavioral theories offer more suitable explanations for this puzzling

phenomenon. This finding is considerably different from previous studies, which are based on low‐frequency data (daily,

weekly, or monthly) and accompanied by the claims that the leverage effect and the volatility feedback effect are the causes

of the asymmetric relation (e.g., Ait‐Sahalia, Fan, & Li, 2013; Dennis, Mayhew, & Stivers, 2006).

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J Futures Markets. 2019;39:1348–1359.wileyonlinelibrary.com/journal/fut1348

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© 2019 Wiley Periodicals, Inc.

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On February 23, 2005, the SSE launched the first ETF in mainland China, named SSE 50 ETF. This ETF picks up the 50 largest and most liquid A‐share stocks listed on the SSE and aims to reflect the

overall performance of the most influential blue‐chip Shanghai stocks. Ten years later, the SSE introduced the SSE 50 ETF options on February 9, 2015, which are the first exchange‐traded options in

mainland China. See more details in Ahn, Bi, and Sohn (2019).

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Specifically, the leverage hypothesis suggests that a negative return should make the firm more levered, and therefore lead to higher volatility (e.g., Christie, 1982; Duffee, 1995; Schwert, 1990); The

volatility feedback hypothesis states that the negative change in expected return tends to be intensified whereas the positive change in the expected return tends to be dampened (e.g., Campbell &

Hentschel, 1992; French, Schwert, & Stambaugh, 1987).