Dynamic correlation and volatility spillovers across Chinese stock and commodity futures markets

• Author: Sang Hoon Kang,Seong‐Min Yoon
• DOI: http://doi.org/10.1002/ijfe.1750
• Date: 01 April 2020
• Published date: 01 April 2020

RESEARCH ARTICLE

Dynamic correlation and volatility spillovers across Chinese stock

and commodity futures markets

Sang Hoon Kang

1

| Seong-Min Yoon

2

1

Department of Business Administration,

Pusan National University, Busan, 609-735,

Republic of Korea

2

Department of Economics, Pusan National

University, 2, Busandaehak-ro 63beon-gil,

Geumjeong-gu, Busan, 46241, Republic of

Korea

Correspondence

Seong-Min Yoon, Professor, Department of

Economics, Pusan National University, 2,

Busandaehak-ro 63beon-gil, Geumjeong-gu,

Busan 46241, Republic of Korea.

Email: smyoon@pusan.ac.kr

Funding information

National Research Foundation of Korea,

Grant/Award Number: NRF-

2017S1A5B8057488

Abstract

This paper examines the return links and volatility transmission between Chinese

stock and commodity futures markets and draws implications for portfolio risk

management. To these ends, we consider three vector autoregression-multivariate

generalized autoregressive conditional heteroskedasticity-class models with which

to model volatilities and conditional correlations between Chinese stock and three

commodity futures markets. Our empirical results reveal evidence of return linkage

and volatility transmission between the Chinese stock and commodity futures mar-

kets. We also analyse optimal portfolio weights and hedging ratios between

s1tock–commodity pairs. Finally, we assess implications for mixed commodity–

stock portfolios and find strong evidence of hedging effectiveness and downside

risk reductions.

KEYWORDS

Chinese commodity futures, downside risk, optimal portfolio weight, risk reduction, time-varying

hedge ratio

JEL CLASSIFICATION

C58; F30; G14; G15; Q31

1|INTRODUCTION

Commodities are considered the most popular investment

for diversifying, hedging, and managing portfolio risk. They

offer different volatilities and have a weaker correlation with

other financial assets (stocks and bonds) when macroeco-

nomic shocks tend to push returns on commodity assets and

investors' portfolios in opposite directions (Silvennoinen &

Thorp, 2013; Hammoudeh, Nguyen, Reboredo, & Wen,

2014; Andreasson, Bekiros, Nguyen, & Uddin, 2016). For

this reason, portfolio investors interested in adding commod-

ity futures with weak or negative correlations with equity

assets should provide better diversification benefits than

those possible in a portfolio without commodities (Bekiros,

Boubaker, Nguyen, & Uddin, 2017; Sadorsky, 2014). There-

fore, investors are interested in examining the dynamics of

commodity prices with the aim of designing strategies for

optimal asset allocation, portfolio optimization, downside

risk reduction, and hedging (Andreasson et al., 2016; Bel-

ousova & Dorfleitner, 2012; Karyotis & Alijani, 2016; Ski-

adopoulos, 2012; Vivian & Wohar, 2012).

Since the development of the multivariate generalized

autoregressive conditional heteroskedasticity (MGARCH)

models, a significant body of literature has focused on using

various MGARCH models to examine the dynamic correla-

tion, portfolio design, and hedging effectiveness of

commodity–equity portfolios. Arouri, Jouini, and Nguyen

(2011, 2012); Arouri, Lahiani, and Nguyen (2015) examined

the extent of volatility transmission, portfolio design, and

hedging effectiveness in oil, gold, and stock returns using

the following MGARCH models: CCC-GARCH, DCC-

GARCH, diagonal BEKK-GARCH, scalar-BEKK-GARCH,

Received: 8 May 2018 Revised: 20 October 2018 Accepted: 13 September 2019

DOI: 10.1002/ijfe.1750

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