Elsevier

Finance Research Letters

Volume 19, November 2016, Pages 181-188
Finance Research Letters

Dynamic spillovers between Shanghai and London nonferrous metal futures markets

https://doi.org/10.1016/j.frl.2016.07.010Get rights and content

Highlights

  • We examine dynamic spillovers between the Shanghai Futures Exchanges (SFE) and the London Metal Exchanges (LME).

  • Diebold and Yilmaz (2012) approach is adopted to nonferrous metal futures returns and volatilities (aluminum, copper, and zinc).

  • The LME market has a greater impact on the SFE market.

  • The recent financial crises intensify the strength of dynamic spillovers across nonferrous metal futures markets.

Abstract

This paper examines the dynamic return and volatility spillovers between the Shanghai Futures Exchange (SFE) and the London Metal Exchange (LME) from 2007 to 2016 using the new spillover index of Diebold and Yilmaz (2012). Our results indicate that the LME nonferrous metal futures have a greater impact on SFE nonferrous metal futures. In particular, these trends are more pronounced in the aftermath of the recent financial crises, indicating the strength of spillovers during periods of turmoil. The direction of spillovers significantly depends on time variation.

Introduction

The recent and ongoing financial crises and the attendant strength of commodity prices renew an interest in understanding the fundamental process of information transmission, through which returns and volatility among commodity markets have become more correlated with each other (Chng, 2009, Chan et al., 2011). This information transmission leads to another broad area of research in the contagion effect. Forbes and Rigobon (2002) described this effect as “significant increase in cross-market linkages after a shock to one country (or group of countries).” This effect can be intensified during financial crises, which further implies that both return and volatility persistently move together over time (Vivian and Wohar, 2012, Silvennoinen and Thorp, 2013, Sensoy et al., 2015, Yarovaya et al., 2016a). This deepens the interest of investors, portfolio and risk managers, manufacturers, and policy makers in better understanding the dynamics of commodity futures prices.

Over the past decade, the Chinese economy has experienced growth averaging 10% per annum, driven by rapid urbanization, heavy industrialization, and openness to global trade (Yue et al., 2015). The fundamental drivers of its economic growth accelerated the demand for nonferrous metals and import share of world trade. 1 As a result, Chinese economic activities play an important role in determining world nonferrous metals prices. According to the Future Industry Association (in 2014), Chinese futures contracts were the top four most traded metals contracts in the world. The Shanghai Futures Exchange (SFE) has become the second largest nonferrous metal futures market in the world, after the London Metal Exchange (LME). With the rapid development of Chinese nonferrous metal futures market and the process of globalization of trading and competition among world commodity futures markets, it is worthwhile investigating the return and volatility spillovers between the Chinese and global leading nonferrous metal future markets.

Surprisingly, however, few studies have examined the Chinese nonferrous metal futures, not only its linkage with the global leading market. Hua and Chen (2007) used a vector error correction model (VECM) to examine the cointegration relationship between SFE and LME futures prices of copper and aluminum. Li and Zhang (2009) adopted a Markov switching VECM to investigate the long-run relationship between SFE and LME copper futures prices. By employing a structural vector autoregressive (VAR) model, Li and Zhang (2013) examined a causal relationship between SFE and LME copper futures prices. These prior studies have revealed the long-run or short-run price relationship with a mix of conclusions due to differing empirical methods and datasets.

This paper attempts to extend the empirical studies, with the intensity and direction of return and volatility spillovers between SFE and LME from 2007 to 2016. First, we apply the spillover index model of Diebold and Yilmaz (2012) to measure the return and volatility spillover indexes across three nonferrous metals, namely aluminum, copper and zinc, in both SFE and LME. To our best knowledge, this is first study to apply this method to address the spillover effect between SFE and LME nonferrous metal markets. Second, we also use a rolling window approach to detect the dynamics of the return and volatility spillovers, to the extent that the two recent crises, i.e., the 2008–2009 global financial crisis (GFC) and the 2010–2012 European debt crisis (EDC), may directly affect return and volatility structures across nonferrous metal futures. Finally, we calculate the net spillover impact to identify the pure ‘source’ or ‘recipient’ of spillovers during the recent financial crises (Wang et al., 2016, Yarovaya et al., 2016b).

The remainder of this study is organized as follows. Section 2 explains the study methodology. Section 3 describes the data and conducts some preliminary analyzes. Section 4 discusses the empirical results. Section 5 provides concluding remarks.

Section snippets

Econometric modeling framework

We apply the generalized VAR (GVAR) methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012), to analyze the directional spillovers across commodity futures markets. Following Diebold and Yilmaz (2012), we assume a covariance stationary VAR (p) as: yt=i=1pψiyt1+ɛtwhere yt is N × 1 vector of endogenous variables, Φi are N × N autoregressive coefficient matrices and ɛt is a vector of error terms that are assumed to be serially uncorrelated. As the

Data and summary statistics

This study considers the three nonferrous metal futures, namely aluminum (Al), copper (Cu), zinc (Zn), traded on SFE and LME. We collect data on the daily high, low, open and close prices from 1st August 2007 to 29th April 2016. Data are drawn from the database of Thomson Reuters and consist of the nearest month futures, on a continuous rolling basis. Table 1 presents the descriptive statistics of returns and volatilities. We see that the distributions for all returns and volatilities are

Return and volatility spillover indexes

Table 2 reports the total spillover index matrix of returns and volatilities, respectively. The (i, j)th entry in each panel is the estimated contribution to the forecast-error variance of variable i coming from innovations to market j. The row sums excluding the main diagonal elements (termed ‘From others’) and the column sums (termed ‘To others’) report the total spillovers to (received by) and from (transmitted by) each return and volatility.

We find that the total spillover for returns

Conclusions

This paper has examined the dynamic return and volatility linkages between SFE and LME nonferrous metal futures by employing the spillover index model of Diebold and Yilmaz (2012). Our empirical results are summarized as follows. First, we find that LME has a greater impact on SFE futures returns and volatilities. Second, the dynamic spillover trends are more pronounced in the aftermath of the recent financial crises, revealing the strong influences of the GFC on both return and volatility

Acknowledgment

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A3A2042747).

References (20)

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