The dynamic impact of bilateral trade linkages on stock market correlations of Australia and China

ABSTRACT

This article aims to examine the long-run equilibrium relationship between bilateral trade linkages and stock market correlations of Australia and China using quarterly data from 1993 to 2015. Further, this study explores the impact of trade intensity on stock market correlations using OLS, Dynamic OLS (DOLS) and Fully Modified OLS (FMOLS) models. The empirical results confirm that there is a significant long-run relationship among the variables. In addition, our results, based on OLS, DOLS and FMOLS, show that increasing trade intensity between Australia and China has a significant and positive impact on their stock market correlations. The Global Financial Crisis also contributed for their stock market interdependence. Our results therefore suggest that the bilateral trade relations between Australia and China have brought their stock markets together over time. The findings of this study offer significant policy and practical implications. The policymakers need to be aware of the economic changes in those countries as they will immediately reflect on their stock market performance and relationship. Similarly, the global investors need to be aware of the fact that the diversification opportunities between Australia and China have considerably declined over time as their markets became more interdependent in the recent past.

KEYWORDS:

• Bilateral trade intensity
• stock market correlations
• GFC
• AGDCC

JEL CLASSIFICATION:

• C22
• F15
• G15

Acknowledgement

The authors would like to thank SiriVanitha Reddy Paramati for her research assistance work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ The description of the MSCI indices for Australia and China is provided in the following links: https://www.msci.com/documents/10199/ec1e0308-fb1a-42b7-baa3-756cab1a9de1 and https://www.msci.com/documents/10199/aa99c3a4-d48b-44ac-8caa-49522caa9021

² Total Australia’s exports and imports with China divided by the total Australian exports and imports with all of the countries in the world.

³ Australian deposit interest rate minus Chinese deposit interest rate; and the same is with inflation rate differentials.

⁴ The recent literature (e.g. Paramati, Gupta, and Tandon 2016; Paramati et al. 2016) has started to use the AGDCC model to measure the conditional correlations of asset returns.

⁵ Study uses correlations from the last week of each quarter that is March, June, September and December in the regression analysis.

⁶ Paramati and Gupta (2013) documented that both interest rates and exchange rates significantly affect stock market returns.

⁷ The GFC period is from third quarter of 2007 to the fourth quarter of 2009. The dummy value is ‘1’ during the GFC, otherwise ‘0’.

⁸ Developed by Saikkonen (1992) and Stock and Watson (1993).

⁹ Proposed by Phillips and Hansen (1990).

¹⁰ Panopoulou and Pittis (2004) provided detailed explanation on the models which deal with the serial correlation issue in the estimation.

¹¹ Paramati, Gupta, and Hui (2016) documented that both bilateral trade and investment linkages play an important role for their stock market relationship in the long run. In another study, Paramati and Gupta (2011) reported that industrial production and stock market returns have a feedback relationship.