Received: 14 March 2020
|
Revised: 18 April 2021
|
Accepted: 19 April 2021
DOI: 10.1002/fut.22217
RESEARCH ARTICLE
Analyzing the frequency dynamics of volatility spillovers
across precious and industrial metal markets
Tangyong Liu1
| Xu Gong2,3
| Boqiang Lin2,3
1
Institute for Advanced Studies in
Finance and Economics, Hubei
University of Economics, Wuhan, China
2
School of Management, China Institute
for Studies in Energy Policy, Xiamen
University, Xiamen, China
3
Innovation Laboratory for Sciences and
Technologies of Energy Materials of
Fujian Province (IKKEM), Xiamen
University, Xiamen, China
Correspondence
Xu Gong, School of Management, China
Institute for Studies in Energy Policy,
Xiamen University, Xiamen,
361005 Fujian, China.
Email: xugong@xmu.edu.cn
Abstract
This paper investigates the volatility spillovers across precious and industrial
metal markets over the period 1993–2019 based on the DY and BK methods.
Results are summarized as follows: (1) while volatility spillovers across industrial metals are higher than across precious metals, the opposite occurs
during crisis periods where precious metals cause net volatility spillovers to
industrial metals; (2) volatility spillovers of the two metal groups show different dynamics in the short‐, medium‐ and long‐term components, especially
in the short‐ and medium‐term components.
KEYWORDS
frequency spillover, industrial metal, metal market, precious metal, volatility spillover
Funding information
National Natural Science Foundation of
China, Grant/Award Numbers: 71701176,
72071166; Science and Technology
Projects of Innovation Laboratory for
Sciences and Technologies of Energy
Materials of Fujian Province (IKKEM),
Grant/Award Number: RD2020060101
1 |
INTRODUCTION
Commodity markets, especially those for precious metals, play an important role in the global financial system. The
price fluctuations of precious metals matter to investors for portfolio diversification and management. Extensive studies
have confirmed that precious metals (especially gold) are good hedging and safe‐haven assets in extreme financial
environments (Baur & Lucey, 2010; Baur & McDermott, 2010; Chai et al., 2021; Hood & Malik, 2013; Lucey & Li, 2015).
Moreover, in the past decades, industrial metals (as an alternative asset class) have become a part of asset portfolio
allocation. Due to the rapid urbanization and industrialization in the emerging economies (e.g., China and India), the
global demand for industrial metals grows rapidly since 2000. The combination of the increased demand and inelastic
supply pushed up the industrial metal prices (Figuerola‐Ferretti et al., 2015), attracting a large number of investors into
the industrial metal markets, thus leading to the financialization of industrial metals (Cheng & Xiong, 2014). As
Watkins and McAleer (2008) pointed out, the assets that track the performance of industrial metals are demanded
more by market participants other than manufacturing companies, such as banks, investment funds, and speculators.
The first purpose of this paper is to investigate the volatility spillovers across the metal market, including four
precious metals (gold, silver, palladium, and platinum) and six industrial metals (aluminum, copper, lead, nickel, tin,
J Futures Markets. 2021;41:1375–1396.
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© 2021 Wiley Periodicals LLC
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and zinc) markets. With deregulation, securitization, globalization, and rapid development of information technology,
the different financial markets are increasingly integrated. Price volatility in one market, due to the irrational herd
effect and incomplete information, usually has spillover effects on another market (King & Wadhwani, 1990; Orlowski
& Sywak, 2019; Wen et al., 2018; Xiong et al., 2020; Y. Xu et al., 2017; X. Yang et al., 2019, 2021). Strohsal and Weber
(2015) regard volatility spillovers as a proxy for the spread of valuable information among linked markets. Wu et al.
(2011) pointed out that the volatility spillovers between different markets provide insights into price volatility and its
connection. In particular, the fact that volatility is transmitted within and across commodity markets is often viewed as
evidence of financialization and integration (see Bekaert et al., 2005; Gozgor et al., 2016; Jin et al., 2012; Nicola
et al., 2016; Okorie & Lin, 2020). By determining the direction and scale of volatility spillovers, we can learn more about
the information efficiency of the metal market, which is closely related to price discovery (see Figuerola‐Ferretti &
Gilbert, 2005; Figuerola‐Ferretti & Gonzalo, 2010; Kumar & Arora, 2018; Li et al., 2021).
The second purpose is to reveal the differences in the volatility spillovers between precious and industrial metals
and study the directional volatility spillovers between the two metal groups. Due to their different exposures to
macroeconomic factors and different hedging properties, precious and industrial metals are often regarded as two
separate assets (see Erb & Harvey, 2006; Gary & Rouwenhorst, 2006; Roache & Rossi, 2010). It can be anticipated that
the volatility spillovers of precious and industrial metals may show distinctive characteristics, especially during some
special periods (e.g., the 2008 global financial crisis). However, the two metal groups still share many common features
that prevent them from being regarded as completely independent markets. For instance, Labys et al. (1998) found that
macroeconomic shocks may simultaneously affect precious and industrial metals. Agyei‐Ampomah et al. (2014) found
that industrial metals (especially copper) can also be regarded as hedging and safe‐haven assets against losses in
sovereign bonds. The commonalities of the two metals may cause information transmission between them.
Methodologically, our research is based primarily on the spillover framework of Diebold and Yilmaz (2009, 2012).
They proposed a simple and intutive measure of volatility spillovers, namely spillover index, based on the forecast error
variance decompositions of the vector autoregressive (VAR) model (hereinafter referred to as the DY method). More
recently, there are also several studies analyzing the volatility spillovers across metal markets using the DY method
(e.g., Batten et al., 2015; Ciner et al., 2018; Lucey et al., 2014; Mensi et al., 2017). However, different from previous
studies, we investigate the precious and industrial metal markets together. This enables us to construct the intra‐ and
inter‐group volatility spillovers. The intra‐group volatility spillovers help us examine the information transmission
within a metal group, while the inter‐group spillovers tell us how information transmits between the two metal groups.
More importantly, we also use the frequency domain method of Baruník and Křehlík (2018) (hereinafter referred to as
the BK method). This is movitated by the following two aspects. The first is that standard economic theory states that
there are differences between short‐ and long‐term shocks, and they may have different effects on the metal market.
The second is that there are different types of investors (e.g., day traders, hedge funds, large institutional investors) who
respond differently to market performance (X. Wang & Wang, 2019). It can be anticipated that the volatility spillovers
may show different characteristics at different frequencies.
Our findings are briefly summarized below. First, we found that the volatility spillovers across industrial metals are
higher than across precious metals. This is interpreted to mean that, the industrial metal market has a stronger
connectedness than the precious metal market. Second, we found that the volatility spillovers of the two metals show
different dynamics during the 1997 Asian financial crisis and the 2008 global financial crisis periods; the volatility
spillovers of precious metals increase sharply, and are higher than that of industrial metals. This can be considered as
good evidence for differences in hedging properties between the two metal groups. Third, we found that precious
metals act as the net information transmitter, especially during crises. This suggests that the information of precious
metals can be used to predict the price volatility of industrial metals. Lastly, we found that the differences between the
volatility spillovers of precious and industrial metals are mainly reflected in the short‐ and medium‐term components,
while the long‐term components exhibit similar dynamics. This implies that the two types of metal markets can be
affected by some common long‐term factors. Furthermore, we found that the volatility spillovers in the metal market is
correlated with the US monetary policy, and the financialization has enhanced the commonality between precious and
industrial metals.
In general, this paper makes two major contributions. On the one hand, we construct an extension of the DY
framework, which can be utilized to analyze the information transmission between two groups of markets. More
precisely, we divide the entire variance decomposition matrix of the VAR model into four sub‐blocks and
computed the intra‐ and inter‐group volatility spillovers (see Table 4, for more details). The intra‐group spillovers
can be regarded as the conditional spillovers within one group, which excludes the influence of the other group.
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The inter‐group spillovers reflect the directional spillovers between the two groups. This framework can also be
applied to other studies, such as the volatility spillovers between stock and metal markets. On the other hand, we
empirically analyze the volatility spillovers across precious and industrial metal markets, both in time and
frequency domains. The results will help to deepen the understanding of information transmission in the metal
market, as well as the difference and interrelationship between the two metal groups.
The remainder of this paper proceeds as following. In Section 2, we briefly review the previous related literature.
Section 3 presents the DY and BK methods in detail. Section 4 describes the data used in this paper. Section 5 conducts
the empirical analysis. The final section concludes.
2 |
L I TE R A TUR E R E V I E W
Metal commodity markets play a major role in the organization of economic activity. They facilitate price
discovery and provide a means of transferring risk or hedging (Figuerola‐Ferretti & Gonzalo, 2010). In particular, over the past decades, commodity futures have become a popular asset class for portfolio investors (e.g.,
banks, investment funds), just like stocks and bonds, which is referred to as the financialization of commodity
markets (Cheng & Xiong, 2014; Tuo & Zhang, 2020). Hillier et al. (2006) showed that the inclusion of precious
metals in an equity portfolio can reduce the systemic risk of investment and accrue diversification benefits,
particularly in periods of elevated equity market volatility. Given the practical implications for understanding
and forecasting market movements, the statistical properties of metal prices have been extensively studied in the
existing literature (e.g., Figuerola‐Ferretti & Gilbert, 2008; Figuerola‐Ferretti & McCrorie, 2016; Figuerola‐
Ferretti et al., 2015; Gao & Liu, 2014; Gong & Lin, 2018a; Khalifa et al., 2011; Liu et al., 2014; Wen et al., 2019).
For instance, Figuerola‐Ferretti et al. (2015) examined the nonstationary behavior of industrial metal prices, and
assessed whether observed mildly explosive periods can be attributed to the supply and demand fundamentals.
At the same time, the greater diversification opportunities that arise with globalization, and the rapid
development of information technology, have led to an increasing integration of different financial markets. A
large number of studies have examined the relationship between different metal markets using various
econometric methods. For instance, Pagano et al. (2009) studied the volatility spillovers across three major
international copper futures markets using the DCC‐GARCH model. They found bidirectional volatility spillovers between the London Metal Exchange (LME) and NYMEX and between the LME and SHFE markets. Based
on the intra‐day data, Todorova et al. (2014) examined the volatility spillovers between five industrial metals
(aluminium, copper, lead, nickel, and zinc) using a heterogeneous autoregressive (HAR) model. They found
strong volatility spillovers across industrial metal markets, especially in the long‐term. Reboredo and Ugolini
(2015) studied the asymmetric spillovers between precious metal markets utilizing a vine copula model. They
concluded that the gold market was very little integrated with the platinum and palladium markets, which
suggests that precious metals do not behave as a single asset class.
On the basis of the variance decompositions of the VAR model, Diebold and Yilmaz (2009, 2012) provided a
new method to study the volatility spillovers between different financial markets, namely spillover index. The
advantage of the DY framework is that it can describe the direction of volatility spillovers, and can analyze the
dynamics use a simple rolling window method (see Ji et al., 2018; Okorie, 2021; Z. Yang & Zhou, 2017; Yarovaya
et al., 2016). Particularly, according to the Fourier transforms of the impulse response functions (IRFs), Baruník
and Křehlík (2018) proposed an extension of the DY framework in the frequency domain (see Balli et al., 2019;
Liang et al., 2020; Lovcha & Perezlaborda, 2020; Maghyereh et al., 2019; Tiwari et al., 2018; Uddin et al., 2019; X.
Wang & Wang, 2019; Y. Wang et al., 2020; Xia et al., 2020). Several studies analyzed the volatility spillovers in
the metal markets based on the DY framework. For example, Lucey et al. (2014) investigated the volatility
spillovers between the London, New York, Tokyo, and Shanghai gold markets. They concluded that Shanghai
remains isolated as a market. Batten et al. (2015) studied the volatility spillovers across four precious metal
markets (gold, silver, palladium, and platinum). The results demonstrated that the precious metal markets are
only weakly integrated. Uddin et al. (2019) investigated the asymmetric volatility spillovers across precious
metals. The results showed that negative and positive shocks cause asymmetric volatility spillovers. Ciner et al.
(2018) studied the volatility spillovers between industrial metal markets. They found a high degree of volatility
spillovers and stated that industrial metals can be considered as a separate investment class.
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In summary, the aforementioned research mainly focused on the volatility spillover within one metal group. In
other words, they studied precious metals and industrial metals separately. Limited literature analyzed the volatility
spillovers across precious and industrial metal markets together, especially the analysis built on the frequency spillover
method. In a sense, our research fills the gaps.
3 |
METHOD OL OGY
Based on the VAR model of Sims (1980), Diebold and Yilmaz (2009, 2012) constructed a spillover index system
(including total spillovers, directional spillovers, net spillovers, and net pairwise spillovers). This spillover
framework provides a simple and intuitive measure of interdependence between different financial markets, and
avoids the controversial issues related to the definition of contagion (Y. Wang & Guo, 2018). Futhermore, basd
on the Fourier transforms of the IRFs, Baruník and Křehlík (2018) proposed the frequency spillover, which can
be regard as an extension of the DY framework in the frequency domain. In the following, we will present the
DY and BK methods in detail.
3.1
| The DY spillover index
According to Diebold and Yilmaz (2009, 2012), the spillover effect from the ith variable ( yi ) to the jth variable ( yj )
is defined as the share of the forecast error variances in forecasting yj due to the shocks to yi (i ≠ j ). The spillover
index can be obtained from the forecast error variances decomposition (FEVD) matrix of the VAR model.
Considering a covariance stationary VAR (p) model:
yt = Φ0 + Φ1yt −1 + Φ2 yt −2 + ⋯+ Φp yt − p + εt , εt ∼ N (0, Σ)
(1)
where yt = (y1t , y2t , …, ykt )′ is a k‐dimensional observed vector, such as realized volatility (RV). Φ0 is the k × 1 intercept
vector, and Φ1, Φ2, …, Φp are k × k VAR coefficients. Σ is the k × k covariance matrix of random disturbance (εt ). The
2
2
2
, σ22
, …, σkk
.
diagonal elements in Σ (i.e., variances) are σ11
To calculate the IRF, we transform Equation (1) to its vector moving average (VMA) representation:
∞
yt =
∑Bi εt −i
(2)
i =0
The k × k coefficients matrices Bi is calculated by the recursion:
Bh = Φ1Bh −1 + Φ2 Bh −2 + ⋯+ Φp Bh − p
(3)
where B0 = Ik and Bj = 0 if j < 0. The standard variance decompositions requires orthogonal innovations, which
depends on the ordering of the variables (see Sims, 1980). Following Diebold and Yilmaz (2012), we use the
generalized variance decompositions based on the generialized impulse response of Pesaran and Shin (1998),
which is invariant to the ordering. We use θij (H ) to denote the H‐step‐ahead forecast error variances
decompositions:
H −1
σ −1
jj
θij (H ) =
∑ (e′i Bh Σej )2
h =0
H −1
(4)
∑ (e′i Bh ΣB′h ei)
h =0
where σjj is the standard deviation of the jth variable. ei (ej ) is a selection vector, where the ith ( jth) element is 1
and the others are 0.
Because Σkj =1θij (H ) ≠ 1, to match the traditional variance decompositions, we normalize the generalized variance
decomposition matrix by the row sum:
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θij (H )
θ¯ij (H ) =
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(5)
N
∑ θij (H )
j =1
Σkj =1θ¯ij (H )
where
obtained by:
= 1, and
Σik, j =1θ¯ij (H )
= N . Basing on θ¯ij (H ), the total volatility spillover across the system can be
N
∑
SP (H ) =
θ¯ij (H )
i, j =1, i ≠ j
N
⋅100 =
∑ θ¯ij (H )
1
N
N
∑
θ¯ij (H ) ⋅ 100
(6)
i, j =1, i ≠ j
i, j =1
The total spillover index effectively describes the complex spillover relationships into a single value, which measures the average contribution of the spillover effect to the total forecast error variance. Also, we can conduct the
directional spillover indexes. We summarize the various spillover indexes in Table 1.
3.2
| The BK frequency spillovers
The spillover index of Diebold and Yilmaz (2009, 2012) is an effective measure that provides aggregate information over
economic cycles. However, it cannot reflect how shocks affect the system in different time horizons (e.g., short‐term or
long‐term). To address this issue, Baruník and Křehlík (2018) proposed the frequency domian method, which can
examine the volatility spillovers in different frequency bands. The core idea of the BK method is to consider the spectral
representation of variance decompositions based on frequency responses to shocks rather than impulse responses to
shocks. According to Equation (2), we obtain the IRF (Bh ). Then, we can define the frequency impulse responses at
frequency ω as
∞
Bω =
∑ Bh e−iωh
(7)
h =0
where ω ∈ (−π , π ), with i = −1 . In the empirical analysis, the Bω can be estimated in a finite horizon (H′) using
standard discrete Fourier transforms (DFT).
The generalized causation spectrum over frequency ω is set as
Ψij (ω) =
2
σ −1
jj (e′i Bω Σej )
(8)
(e′i Bω ΣB′ω ei )
where Σ is the covariance matrix of εt in Equation (1). σjj is the jth diagonal element of Σ. ei (ej ) is a selection vector. The
cross‐spectral density on the frequency band d is set as
Ωd = Σω ∈ d Bω ΣB′ω
(9)
where d = (a, b) , with a, b ∈ (−π , π ) and a < b.
TABLE 1
Volatility spillover indexes
Indexes
Description
Calculation
Total spillover
Volatility spillover across all variables
SP (H ) =
N
1
N
∑
θ¯ij (H ) · 100
i, j =1, i ≠ j
Directional spillovers
Volatility spillovers received by variable i from all other variables
N
∑
Fromi (H ) =
θ¯ij (H ) ⋅ 100
j =1, j ≠ i
volatility spillovers transmitted from variable i to all other variables
N
Toi (H ) =
∑
θ¯ji (H ) ⋅ 100
j =1, j ≠ i
Net volatility spillovers from variable i to all other variables
Neti (H ) = Toi (H ) − Fromi (H )
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Following Baruník and Křehlík (2018), the weighting function can be defined as
Γi (ω) =
ei′ Bω ΣBω′ ei
ei′ Ωd ei
(10)
The variance decompositions on the frequency b and d can be calculated by
Θij (d ) =
∑ Ψij (ω) Γi (ω)
(11)
ω∈d
Then, similar to the prevous DY method, we normalize the Θij (d ) by the row sum:
Θ̄ij (d ) =
Θij (d )
N
(12)
∑ ∑ Θij (d)
j =1 d ∈ D
where ∩d ∈ D d = ϕ and ∪d ∈ D d = (−π , π ). After that, the total volatility spillover on the frequency band d can be
calculated by
N
∑
SP (d ) = 100⋅
Θ̄ij (d )
i, j =1, i ≠ j
N
(13)
∑ ∑ Θ̄ij (d)
i, j =1 d ∈ D
According to different settings of frequency band d , we can decompose the DY spillovers into different components,
such as high‐, medium‐ and low‐frequency components. Baruník and Křehlík (2018) pointed out that the dynamics of
frequency spillovers may reflect the investors' belief about the persistent (permanent or temporary) of shocks on the
real economy. A shock with long‐term effects will have higher power at low frequencies, and in the cases, it results in
an increase in the low‐frequency (or long‐term) volatility spillovers. For the stock market, the long‐term shocks may be
attributed to permanent changes in expectations about future dividends. For the metal market, the long‐term shocks
may be related to the changes in demand and supply fundamentals, such as global economic growth, mining technology development, and so on. The dynamics of high‐frequency (or short‐term) volatility spillovers may be attributed
to market noise (or stochastic volatility). For example, the price decline in one metal market may trigger investors to
sell in other metal markets, due to the irrational herd effect, incomplete information, and other reasons (see Liang
et al., 2020).
4 |
DATA
We will examine the volatility spillovers across 10 major metal markets, including four precious metals (gold, silver,
palladium, and platinum), and six industrial metals (aluminum, copper, lead, nickel, tin, and zinc). Selected 10 metals
play an important role in the global commodity markets. Their price volatility has attracted widespread attention, not
only from financial investors but also from enterprises. We use LME spot price data and London Bullion Market
Association precious metal price data. The sample period ranges from November 5, 1993, to September 13, 2019 (see
Figure 1). The raw data can be available from Refinitiv Datastream.1
Following Wen et al. (2016), Gong and Lin (2017, 2018b, 2021), Hsu and Murray (2007), Jian et al. (2018), Qiu et al.
(2019), Dai et al. (2020), Liu and Gong (2020) and Tang et al. (2021), we use the realized volatility (RV) as an agent
variable of unobserved market volatility. The weekly realized volatility (RVt ) is calculated by
M
RVt =
∑rti2
(14)
i =1
1
We have also estimated volatility spillovers under nearest‐to‐maturity NYMEX and LME futures prices for precious and industrial metals, respectively. Results, available upon request are highly
consistent with those reported for spot prices.
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F I G U R E 1 Daily closing prices
(1993 = 100). This figure plots the London
Bullion Market Association precious metal
(spot) prices and the London Metal
Exchange industrial metal (spot) prices,
normalized by the price in November 1,
1993. Source: Refinitiv Datastream [Color
figure can be viewed at
wileyonlinelibrary.com]
where rti is the ith daily return in week t . M is the number of trading days (normally, M = 5). The daily return rt i is
calculated by rti = 100 ⋅ (ln pti − ln pt , i −1 ), where pt i is the ith daily price in week t and pt , i −1 is the previous day price.
Following Diebold and Yilmaz (2012), we convert the weekly realized volatility as annualized volatility and perform a
logarithmic transformation. The final data is calculated by
σˆt = ln(100 ⋅
52⋅RVt )
(15)
We obtained a total of 1350 observations on the volatility of each metal. Figure 2a,b plot the weekly realized
volatilities. It can be observed that all volatility series appear to be highly persistent, and are significantly different in
different periods. For instance, the volatility is high in the period 2007–2009, when the global financial crisis broke out.
Also, we can observe that the volatility of different metals changes simultaneously, which suggests volatility spillovers.
Table 2a,b provide summary statistics of log volatility. We can notice that the mean volatility (log) of gold is 1.06,
which is the least volatile among precious metals, and palladium is the most changeable (1.36). In particular, we find
that gold's volatility holds the highest SD and the lowest skewness. For industrial metals, we find that aluminium and
tin are the least volatile (1.22), while nickel is the most volatile (1.42). Q statistics of all volatility series reject the null
hypothesis of no autocorrelation, suggesting that the volatility has significant serial autocorrelations. J‐B statistics show
that most volatility series are nonnormal distributed (except for palladium and lead) and the ADF tests show that all
volatility series are stationary.
Table 3 shows the Pearson correlations. We can see that all pairwise correlations are significantly positive at
the 1% level. The correlation between gold and silver (0.58) is the highest, indicating the close relationship
between these two metals. Also, we find that silver is highly correlated with industrial metals, which is consistent
with the fact that silver also has many industrial uses (e.g., electrical contact materials). Furthermore, we regard
the entire correlation matrix as four blocks and calculated the average correlations for each block (see Table 3).
The results show that the average correlation of industrial metals (0.45) is slightly higher than that of precious
metals (0.42) and the intra‐group average correlations (0.43) are much higher than the intergroup average correlation (0.27). The above correlation analysis can give one a general understanding of the connection between the
different metal volatilities.
5 |
EMP I RICA L RESU LT S
In this section, we will use the DY and BK methods to study the volatility spillovers within and between precious and
industrial metal markets. First of all, following Diebold and Yilmaz (2009, 2012), we discuss the volatility spillovers in
the full sample. After that, based on rolling window estimation, we consider the dynamics of the volatility spillovers. In
particular, by the frequency method of Baruník and Křehlík (2018), we decompose the time‐domain DY spillovers into
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F I G U R E 2 (a) Realized volatility of
precious metals (log). (b) Realized volatility
of industrial metals (log) [Color figure can be
viewed at wileyonlinelibrary.com]
(b)
the frequency‐domain BK spillovers (including high‐, medium‐, and low‐frequency components), and discuss some
meaningful results which can help understand the metal markets more. Lastly, we discuss the relationship between the
US monetary policy and the volatility spillovers, and whether the financialization of commodity markets has enhanced
the commonality between precious and industrial metals.
5.1
| Full‐sample analysis
In this section, we treat the 10 metal markets as a whole. On the basis of the 10 (log) realized volatility series, we build a
10‐variable VAR model. The lag length (p) of the VAR model is set to 2, according to the Akaike information criterion
(AIC) and the Bayesian information criterion (BIC). It should be pointed out that Figuerola‐Ferretti and Gilbert (2008)
found that metal volatility exhibits long memory characteristics. This suggests that the VAR(2) model may be
insufficient to capture the long persistence. The VAR model with higher lags may be better, but it might cause
over‐parameterization problems. Based on the empirical autocorrelation function (ACF), we find that the estimated
residuals of VAR(2) are almost white noise. This means that the VAR(2) model roughly captures the volatility
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T A B L E 2a
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Descriptive statistics (precious metals)
Gold
Silver
Palladium
Platinum
Mean
1.06
1.31
1.36
1.20
Median
1.08
1.31
1.36
1.20
Max
1.90
2.25
2.28
2.05
Min
0.14
−0.08
0.60
0.21
SD
0.26
0.25
0.25
0.23
−0.39
−0.10
0.04
−0.11
3.68
3.91
3.05
3.75
Skewness
Kurtosis
J‐B
59.36***
48.24***
0.53
34.67***
Q(1)
347.75***
209.22***
261.33***
289.47***
ADF
−5.05***
−5.55***
−10.14***
−6.58***
Note: J‐B is the normality test of Jarque and Bera (1980). Q (1) is the autocorrelation test of Ljung and Box (1978) for 1th order. ADF is the unit root test.
***Significance at 1% level.
T A B L E 2b
Descriptive statistics (industrial metals)
Aluminium
Copper
Lead
Nickel
Tin
Zinc
Mean
1.22
1.28
1.36
1.42
1.22
1.31
Median
1.22
1.27
1.36
1.42
1.22
1.31
Max
1.84
2.06
2.06
2.17
2.09
2.04
Min
0.21
0.51
0.38
0.41
0.19
0.32
SD
0.21
0.23
0.24
0.22
0.28
0.25
−0.07
0.13
−0.05
−0.13
−0.15
−0.15
3.53
3.42
3.06
3.58
3.23
3.17
Skewness
Kurtosis
J‐B
16.70***
13.81***
0.85
23.04***
7.91**
6.61**
Q(1)
178.22***
281.80***
261.75***
105.78***
280.71***
299.45***
ADF
−10.94***
−8.16***
−3.99***
−5.47***
−4.97***
−5.02***
Note: J‐B is the normality test of Jarque and Bera (1980). Q (1) is the autocorrelation test of Ljung and Box (1978) for 1th order. ADF is the unit root test.
***Significance at 1% level; **Significance at 5% level.
persistence and confirms the appropriateness of the model. Besides, to set the optimal forecasting ahead horizon (H),
we estimate the results where H = 1, 2, …, 50. It can be found that the estimated total spillover index increases with
the increase of H, but almost reaches the maximum when H is equal to 20. Therefore, we set H to 20 (weeks). It is
worth noting that we experiment with other settings (i.e., different p and H) and find that the empirical results are
highly consistent.
Table 4 displays the full‐sample volatility spillover table, including the variance decomposition matrix and the
spillover indexes. Furthermore, we divide the 10 metals into two metal groups (i.e., precious and industrial metals), and
calculate the intra‐ and inter‐group spillovers. More precisely, we divide the whole variance decomposition matrix into
four blocks. The first block is the 4 × 4 matrix in the top left corner, which reflects the spillovers within precious metals
(i.e., intra‐group spillovers). The second block is the 4 × 6 matrix in the top right corner and the third block is the 6 × 4
matrix in the bottom left corner. These two blocks reflect the directional spillovers across the two metals (i.e., inter‐
group spillovers). The fourth block is the 6 × 6 matrix in the bottom right corner, which reflects the spillovers within
industrial metals. It should be noted that the calculations of inter‐group spillovers are different from the intra‐group
spillovers. Take the second block as an example: the values in column “Sub‐from” are the sum of all elements in each
row, without removing the diagonal element; similarly, the values in “Sub‐to” are column sums; the values in
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Gold
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Correlation matrix
Gold
Silver
Palladium
Platinum
Aluminium
Copper
Lead
Nickel
Tin
Zinc
1
0.58***
0.31***
0.49***
0.25***
0.30***
0.27***
0.29***
0.29***
0.38***
1
0.25***
0.35***
0.31***
0.36***
0.35***
0.26***
0.36***
0.37***
1
0.52***
0.17***
0.23***
0.19***
0.21***
0.17***
0.25***
1
0.22***
0.22***
0.20***
0.23***
0.21***
0.31***
1
0.51***
0.43***
0.39***
0.34***
0.50***
1
0.51***
0.46***
0.38***
0.56***
1
0.42***
0.44***
0.57***
1
0.38***
0.48***
1
0.43***
Silver
Palladium
Platinum
Aluminium
Copper
Lead
Nickel
Tin
Zinc
1
***Significance at 1% level.
“Sub‐net” are calculated by “Sub‐to” in the second block minus “Sub‐from” in the third block; and the values of “Sub‐
total” are the sum of “Sub‐from” (or “Sub‐to”), which reflects the gross spillovers from industrial to precious metals.
We first focus on the total volatility spillovers. To be more intuitive, we reorganize the values of “Sub‐total” in
Table 4 into Table 5. The “Total” value is 485.91. Then, the total spillover across ten metal markets can be calculated by
485.91/10 = 48.591, which implies that on average 48.59% of the forecast error variances are caused by the spillover
effect from other metal markets. The high degree of volatility spillovers suggests that the 10 metal markets as a whole
are highly connected. Information (or shocks) can be effectively transmitted between different metal markets. Furthermore, we consider the total volatility spillovers within precious and industrial metals. According to Table 5, the
total intra‐group spillover of precious metals is calculated by 110.73/4 = 27.68, and that of industrial metals is 231.01/
6 = 38.50. It can be found that the total volatility spillover across industrial metals (38.50%) is higher than across
precious metals (27.68%), but both are lower than the total volatility spillover (48.59%) across the entire metal market.
The difference in the total volatility spillover of precious and industrial metals can be attributed to their different economic
fundamentals. For industrial metals, they are mainly used as raw material (or input) for industrial production. On the demand
side, different industrial metals face common influencing factors (e.g., global economic growth). Besides, the supply of
industrial metals is inelastic in the short‐ and medium‐term (see Figuerola‐Ferretti et al., 2015).2 The combination of common
demand and inelastic supply may cause the comovement in the prices of different industrial metals, leading to higher volatility
spillovers between them. For precious metals, the fundamentals of supply and demand are heterogeneous. For instance,
demand for gold is dominated by investment demand (36% of total), while demand for silver is mainly due to industrial
requirements (40%) and jewelry (45%) (see Hillier et al., 2006). Demand for platinum and palladium arises from the construction of catalytic converters for automobiles (more than 50%). On the other hand, the supply of the four precious metals
also stems from dissimilar sources. For instance, the supply of gold is dominated by mine production, sale of official gold
serves, scrap recycling, and disinvestment. The supply of silver mainly emanates from mining production, scrap recycling,
disinvestment, government sales, and producer hedging. Concentrated natural resources of platinum and palladium are rare.
They are produced from a mixture of six platinoid elements (see Antonakakis & Kizys, 2015). Due to the heterogeneity of the
fundamentals, the prices of different precious metals often show different movements, resulting in low volatility spillovers
between them.
Next, we examine the directional volatility spillovers. Within the precious metals, we find that gold produces the
highest spillovers (34.12%) and net spillovers (3.57%) to the other three. This is in agreement with the fact that gold
plays a leading role in the precious metal group. As an important safe‐haven asset, gold attracts more attention from
investors. Within the industrial metals, we observe that zinc plays a leading role, transmitting the largest spillovers
(53.68%) and net spillovers (13.52%) to the other five.3 The result that zinc plays a leading role in the industrial metal
2
As shown in Figuerola‐Ferretti et al. (2015), a new mine will take 10 years to construct and major extension of an existing mine will take around 5 years.
3
The result is consistent with the study of Ciner et al. (2018). They also find zinc transmits the largest net volatility spillover (12.64%) to other industrial metals.
Platinum
Sub‐from
17.24
5.53
11.35
34.12
3.57
2.74
4.09
3.17
3.73
5.30
6.33
25.36
8.62
59.48
12.19
Silver
Palladium
Platinum
Sub‐to
Sub‐net
Aluminium
Copper
Lead
Nickel
Tin
Zinc
Sub‐to
Sub‐net
To others
Net
9.11
57.93
8.84
32.01
5.55
7.25
3.00
5.26
6.32
4.62
0.27
25.92
6.05
3.71
51.18
16.16
44.57
1.47
−5.70
−0.49
12.79
3.02
2.03
2.50
1.44
1.73
29.38
0.11
10.45
2.04
1.23
1.74
1.47
2.24
2.08
1.96
−5.81
1.74
31.77
56.91
15.49
5.55
10.73
18.92
12.41
64.92
2.86
3.65
17.07
191.35
Sub‐total: 80.62
16.94
15.81
10.97
11.34
14.38
11.18
Sub‐total: 110.73
29.81
24.73
25.65
30.55
−12.86
37.34
−7.81
31.21
7.33
3.24
5.98
6.13
8.53
49.80
−5.05
6.13
1.45
0.99
2.27
1.41
4.31
59.02
7.62
47.94
10.49
6.47
9.37
10.30
45.29
11.32
−3.31
11.07
1.71
2.29
4.65
2.42
−4.66
−12.69
37.17
−9.29
−1.95
49.28
29.61
6.49
5.02
50.13
5.61
6.67
5.82
−3.4
7.56
1.98
1.45
1.99
2.15
Nickel
40.65
10.20
6.95
7.74
46.06
9.43
6.33
−2.71
8.64
1.41
1.78
3.39
2.05
Lead
−6.50
39.32
−2.09
27.92
5.65
54.18
5.89
7.61
3.88
4.89
−4.41
11.40
2.16
0.99
5.81
2.44
Tin
15.33
72.43
13.52
53.68
42.90
8.34
9.93
12.94
11.81
10.66
1.81
18.75
4.58
2.85
5.06
6.27
Zinc
−17.07
294.56
311.63
Sub‐total: 231.01
Total: 485.91
−
57.10
45.82
49.87
53.94
54.71
50.20
40.16
30.01
38.90
42.60
40.32
39.02
174.28
Sub‐total: 63.55
–
43.09
35.08
48.82
47.28
From others
13.29
10.35
23.17
16.74
Sub‐from
Note: The ijth entry is the forecast variance contribution to metal i from metal j. The column “From others” are off‐diagonal row sums. The row “To others” are off‐diagonal column sums. The “Net” column is “To
others” minus “From others.” The “Total” is the sum of “From others” (or “To others”). The “Sub‐from,” “Sub‐to,” “Sub‐net,” and “Sub‐total” are calculated using the elements in the submatrix. For bold values,
under the DY spillover framework, we cannot obtain the significance of the spillover index.
52.72
Copper
Aluminium
Palladium
Gold
Silver
Industrial metals
Precious metals
Volatility spillover table
Gold
TABLE 4
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TABLE 5
ET AL.
Intra‐ and intergroup total volatility spillovers
Volatility spillovers
From‐precious metals
From‐industrial metals
From others
To‐precious metals
110.73
63.55
174.28
To‐industrial metals
80.62
231.01
311.63
To others
191.35
294.56
Total: 485.91
Net
17.07
−17.07
Note: The ijth entry is the total spillovers from group j to group i. For example, the number (63.55%) in Row 1, Column 2 indicates the total spillover from
industrial metals to precious metals.
group is somewhat unexpected because aluminum and copper are more important industrial metals by value (see
Figuerola‐Ferretti et al., 2015). One possible reason may be related to the fact that zinc is often used in the alloy (e.g.,
zinc–aluminum alloy) and comined with other metals (e.g., lead), resulting in a close relationship between the price of
zinc and other metals.4 Therefore, from the perspective of network topology, zinc can be regarded as a central node
among the industrial metal system, which plays an important role in information transmission (see Diebold &
Yılmaz, 2014). This is in line with the dominant role of zinc in the spillover relationship. Another reason may be due to
the widespread use of zinc‐based battery technology in electric vehicles, which led to the sharp rise in zinc prices after
2015 and attracted more attention from market participants. In addition, Table 4 shows that silver produces the largest
volatility spillover (32.01%) to industrial metals. This verifies the fact that there is a close interconnection between
silver and industrial metals. It can be explained by the industrial uses of silver. The prices of silver and industrial metals
may be affected by the same factors, such as the global economic situation.
In summary, built on the full‐sample analysis, we have some insights about the volatility spillovers across metal
markets. The most important is that the volatility spillovers of industrial metals are higher than that of precious metals.
This suggests that industrial metals have a stronger cointegration relationship than precious metals. Besides, we find
that gold plays the leading role in precious metals, while zinc in industrial metals.
5.2
| Dynamic analysis
During our sample period (1993–2019), many major changes occurred. Some are presented as continuous evolution,
such as economic globalization, the development of electronic technology, the rise of hedge funds, and the financialization of commodity markets. Others are described as bursts, such as the outbreak of the 1997 Asian financial crisis
and the 2008 global financial crisis. The full‐sample analysis, although providing a useful summary of “average”
behavior, is likely to miss the potentially important patterns, especially the dynamic characteristics of volatility spillovers. To adress this issue, we move to dynamic spillover analysis. Following the usual approach (e.g., Diebold &
Yilmaz, 2012), we obtain the dynamic spillover indexes through a simple rolling window method. The window size is
set to 100 weeks (almost 2 years). This paper mainly focus on the dynamics of total volatility spillover, which can
measure the connectedness among metal markets and can reflect the evolution of systemic risk (see Baruník &
Křehlík, 2018; Diebold & Yılmaz, 2014).
Figure 3 plots the total volatility spillover among all 10 metal markets, and two intra‐group volatility spillovers. We
can see that the volatility spillovers across metal markets are quite different in different periods (i.e., time‐varying).
Taking the“All” spillover series as an example, we can roughly identify three major cycles. The first cycle is taken from
1996 to 2002. The total spillover index decreases slowly from 45% to 35%, a reduction of 10%. The second cycle is from
2003 to 2012, within which the total spillover index increase from 35% to 65%. The third cycle is from 2013 to 2019.
During this period, the total spillover index decreases from 65% to 30%. At first glance, two intra‐group spillovers show
cyclical patterns similar to the “All” spillovers. However, if we observe carefully, we can find that there are several
differences between the two intra‐group spillovers, which give us some important insights about the difference between
precious and industrial metals.
4
We find that the average correlation between the zinc and other metals is the highest (0.51) among the six industrial metals.
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F I G U R E 3 Total volatility spillovers.
This figure plots the total (intra‐group)
volatility spillovers calculated by the rolling
window VAR method. “All” denotes all ten
metals (including precious and industrial
metals). “Precious” denotes precious metals.
“Industrial” denotes industrial metals. The
rolling window is 100 weeks [Color figure
can be viewed at wileyonlinelibrary.com]
The most obvious differences in the two intra‐group spillovers are shown in the periods of the Asian financial crisis
(1997–1998) and the global financial crisis (2007–2009). During these two crisis periods, the intra‐group spillovers of
precious metals increase sharply, while that of industrial metals change in the opposite direction (i.e., decrease). More
interestingly, we find that only during these two crisis periods, the “Precious” spillovers are higher than the
“Industrial” spillovers. The results can be explained by differences in the hedging properties of the two metals. Precious
metals (especially gold) are generally considered to be safe‐haven assets and can hedge against the uncertainty of other
financial markets (e.g., stock markets). During and after the financial crisis, the hedging demand for precious metals
increases sharply which may push up the price and volatility.5 For risk diversification, investors tend to hold positions
in multiple precious metal markets at the same time. In the face of shocks, they may take similar actions in different
precious metal markets, which will cause the prices of different metals to change together, resulting in high volatility
spillovers (or connectedness). In addition, investors may hold positions both in stock and metal markets. When the
stock market suffers a sharp drop, traders in stock markets may face borrowing constraints and sundry pressure to
liquidate risky positions, which cause them to exit from precious metal markets. Since investors tend to act similarly in
four precious metal markets together, this increases information flows and hence, causes upward movements in
volatility spillovers.
Also, we find that the two intra‐group spillovers show different dynamics during 2003–2004; the volatility spillovers
of industrial metals increased within this period, while that of precious metals declined (see Figure 3). This may be
related to the emergence of industrial metals as a new asset, in other words, the financialization of industrial metals.
Due to the rapidly increasing demand from emerging economies (e.g., China and India), the industrial metal prices
rose sharply after 2003, which attracted a large number of investors to participate in the industrial metal market. Since
different industrial metals show many similarities, investors usually treat them as the same asset class. To diversify
risks, investors may participate in different industrial metal markets at the same time, especially portfolio investors
(e.g., banks, mutual funds, and commodity index traders). In particular, investors may carry out arbitrage among
different industrial metals. This makes the different industrial metal prices are highly integrated, and information can
be effectively transmitted across different markets, resulting in high volatility spillovers. Our results verify the view that
the financialization of commodities may lead to high linkages between different commodity prices (see Cheng &
Xiong, 2014). Our results are also consistent with Figuerola‐Ferretti et al. (2015). They found that four industrial metals
(i.e., copper, nickel, lead, and tin) show explosive growth in 2003, indicating a high degree of uncertainty (or systemic
risk) in the industrial metal system.
Figure 4 shows the two inter‐group volatility spillovers, which can reflect gross information transmission between
precious and industrial metals. We can obverse that, unlike the intra‐group spillovers, there are no obvious cyclical
patterns for the two inter‐group volatility spillovers. In most cases, they fluctuate in the range of 50%–100% and are very
5
As shown in Figuerola‐Ferretti and McCrorie (2016), the investment in gold increase significantly during and after the 2008 global financial crisis.
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F I G U R E 4 Directional volatility
spillovers. This figure plots directional
spillovers between precious and industrial
metals. “Precious to industrial” denotes the
total forecast variance contribution from
precious metals to industrial metals, while
“Industrial to Precious” denotes that from
industrial metals to precious metals. “Net”
equals “Precious to industrial” minus
“Industrial to Precious” [Color figure can be
viewed at wileyonlinelibrary.com]
F I G U R E 5 Conditional and
unconditional volatility spillovers. This
figure plots the conditional and
unconditional spillovers. Conditional
spillovers are the intra‐group spillovers
calculated by a 10‐variables VAR model
(including two metal groups), while
unconditional spillovers are total spillovers
calculated by one metal group [Color figure
can be viewed at wileyonlinelibrary.com]
close. Similar to the intra‐group spillovers, the inter‐group spillovers also show interesting dynamics during the
financial crises. We can see that the “Precious to industry” spillover is higher than the “Industry to precious” spillover
in periods 1997–1998 and 2007–2009. In other words, precious metals transmit net spillovers to industrial metals during
crises. Under the framework of Diebold & Yilmaz (2009, 2012), this implies that the volatility (or information) is mainly
transmitted from precious to industrial metals. Analogous to the differences in the intra‐group spillovers, the above
results can also be explained by the different hedging properties of the two metals.
Lastly, we compare our intra‐group spillovers (i.e., conditional spillovers) to the unconditional spillovers studied in
the previous literature (e.g., Batten et al., 2015; Ciner et al., 2018). The unconditional spillovers are estimated separately
by a smaller VAR model, including only one metal group. More precisely, we estimate the unconditional spillovers
across precious metals in a 4‐variables VAR model which contains four precious metals only. Similarly, we can get the
unconditional spillovers of industrial metals with a 6‐variables VAR model. Figure 5 displays the conditional and
unconditional total spillovers. Not surprisingly, the dynamics of conditional and unconditional spillovers are nearly the
same. However, we can identify significant differences between them. For instance, the unconditional spillovers are
higher than the conditional spillovers, especially after 2004. The extra part is due to the volatility spillovers from the
other metal group. This suggests that the volatility spillovers may be overestimated if we consider the precious or
industrial metals separately.
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F I G U R E 6 Frequency volatility
spillovers. This figure plots the frequency
volatility spillovers. H, high‐frequency
(1–2 weeks); L, denotes low‐frequency
(9–100 weeks), M, middle‐frequency
(3–8 weeks) [Color figure can be viewed at
wileyonlinelibrary.com]
Overall, we find that the volatility spillovers of precious and industrial metals are different in some periods,
especially during crises. This is great evidence to verify that there are differences in the hedging properties between the
two metal groups.
5.3
| Frequency analysis
As Baruník and Křehlík (2018) pointed out, investors operate on different horizons, which may cause the volatility
spillover to exhibit different dynamics in different frequencies. Agents with short horizons, such as day traders or
hedge funds, are more concerned about the short‐term performance of markets and make decisions largely based on
transitory phenomena like sporadic events and psychological factors. Hence, their reactions to shock occur principally
in the short‐term, which causes the information to transmit more rapidly. Other agents, such as large institutional
investors, are more interested in the long‐term performance of markets, and their responses to economic and financial
shocks mostly materialize in the long‐term, that is, take more time to absorb shocks and give feedback (X. Wang &
Wang, 2019). Therefore, it can be anticipated that the volatility spillovers may exhibit different patterns at different
frequencies. In this section, we consider the frequency methodology of Baruník and Křehlík (2018). This paper defined
the high‐frequency band as 1–2 weeks, the middle‐frequency band as 3–8 weeks (2 months), and the low‐frequency
band as 9–100 weeks (almost 2 years). The rolling window is set to 100 weeks. It should be noted that we have
experimented with other settings and found no material changes in the empirical results. For brevity, we mainly
concentrate on the total spillovers. Figure 6 shows the results.
We can observe that the long‐term component is the smallest and most stable; the medium‐term component is
almost the same as the short‐term component, except for some special periods. In particular, we find that both precious
and industrial metals, the short‐term component is the highest among the three, which suggests that the volatility
spillover are mainly caused by short‐term shocks (or market noise).6 For comparison, we plotted the same frequency
components in a graph (see Figure 7). It can be found that the short‐ and medium‐term components of the two metals
are quite different, especially during crisis periods. However, their long‐term components are quite close and show
similar dynamics. Below, we will discuss this in more detail.
We first examine the dynamics during the Asian financial crisis (1997–1998). From Figure 6 (upper part), we can
see that the short‐term spillovers across ten metal markets are much higher than the long‐term spillovers, and the two
frequency spillovers change in opposite directions. The short‐term spillovers increased sharply, while the long‐term
spillovers decrease slowly. According to Baruník and Křehlík (2018), the above dynamics suggest that investors believe
that the uncertainty in the metal markets is less persistent, which will lead to a short‐term rise in systemic risk. This is
6
Our result is in line with Y. Wang et al. (2020). They analyze the volatility spillovers across four global commodity futures markets (including gold, wheat, WTI crude oil, and copper) and find that
the high‐frequency component is the largest.
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F I G U R E 7 Frequency volatility
spillovers (reorganized). This figure is a
reorganization of Figure 6. The same
frequency components are placed in a
subgraph for comparison. H, denotes
high‐frequency (1–2 weeks); L, denotes
low‐frequency (9–100 weeks), M, denotes
middle‐frequency (3–8 weeks) [Color figure
can be viewed at wileyonlinelibrary.com]
consistent with the fact that, although the Asian financial crisis had a great impact on the economy of most Asian
countries (e.g., Thailand, Indonesia, and South Korea), its impact on the global economy is limited; especially, the US
economy is almost unaffected by the crisis. In addition, we observe that the frequency spillovers of the two metals
change in the same direction, which is different from the dynamics of the DY spillovers shown in Figure 3. Also, we
find that the short‐term spillovers of precious metals are much higher than that of industrial metals. This means that
the Asian financial crisis produced a stronger short‐term impact on precious metals.
Another interesting dynamics is presented in the period 2002–2006. During this period, the volatility spillovers of
industrial metals are much higher than that of precious metals, and the frequency spillovers of the two metals show
different dynamics. In particular, we find that there is a turning point around 2004. During 2002–2004, the short‐term
spillovers of industrial metals increased sharply, while that of precious metals declined, and the long‐term spillovers of
the two metals show a downward trend. After 2004, the short‐term spillovers of precious metals climbed sharply and
catching up with that of industrial metals in 2006. More importantly, we find that the long‐term spillovers of the two
metals turned into an upward trend during 2004–2006. The result suggests that the investors' beliefs changed significantly in 2004. Before 2004, investors considered the uncertainty is less persistent. However, after 2004, investors
believe the shock is permanent and will produce long‐term risks. The factors that prompted investors to modify their
beliefs require further research. The US monetary policy may have played an important role. The financialization of
commodity markets can also serve as a cause.
Next, we focus on the dynamic spillovers around the 2008 global financial crisis. Figure 3 shows that the total
spillovers reached more than 60% in 2006, indicating a high degree of connectedness between different metal markets.
We contend that this may be related to increasing index investment in commodity markets (see Tang & Xiong, 2012).
The spillovers of industrial metals peaked in 2006 and then dropped sharply. In particular, we find that the price
movements of different metals diverged after 2006 (see Figure 1). For instance, the price of zinc turned to fall after
2006, while the price of tin rose until 2008. This may be related to the emergence of bubbles in some industrial metal
markets.7 To avoid risks, investors (especially long‐term investors) may choose to exit from these metal markets,
causing prices to fall. After the outbreak of the crisis (i.e., 2007), the spillovers of precious metals increase sharply. More
interestingly, we find that both the spillovers of precious and industrial metals exhibited a sharp rise in 2008, which
may be related to the bankruptcy of the Lehman Brothers, leading to an abrupt decline in the stock market. The sudden
collapse of the stock market brought liquidity risk. As stated by Figuerola‐Ferretti and McCrorie (2016), to facilitate
margin calls, investors in the securities market were forced to exit from the metal market. This may lead to sharp
declines in metal prices and high volatility spillovers. After 2009, with the introduction of various stimulus policies, the
crisis was eased. However, the extremely loose monetary policy raised concerns about future inflation, coupled with the
7
For instance, Gilbert (2010) found the prices of alumnium and copper exhibited bubble behaviors in early‐2006. Figuerola‐Ferretti et al. (2015) found bubbles in the prices of copper, nickel in late‐
2005, and lead in mid‐2006.
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outbreak of the European debt crisis, leading to the increased hedging demand for metals. We can see that until 2013,
the volatility spillovers remained high.
After 2013–2014, the spillovers also show interesting dynamics. From Figure 3, we can see that the spillovers
peaked in 2013 and then maintained a gradual downward trend.8 We argue that this result may be related to the
tapering announcements in 2013 and 2014. These lead to a surge in the US Treasury yields and an appreciation of
the dollar in a context of lower growth in the Chinese economy and falling commodity prices. In addition, we find that
the spillovers between precious metals increase during 2016–2017, which may be related to the 2015–2016 stock market
sell‐off, especilly in China. Figure 7 shows a more interesting story. It can be found that, after 2013–2014, the spillovers
of two metals at all frequencies are very close. We contend that this may be signaling that there is an increasing number
of investors taking simultaneous positions in both metal markets. The investors arbitrage between precious and
industrial metal markets, resulting in information being homogeneously transmitted between the two metal markets.
In general, the dynamics of BK spillovers tell a more detailed story than the DY spillovers. The results show that the
volatility spillovers across metals markets are mainly attributed to short‐term shocks. Also, we find that the differences
in the volatility spillovers of the two metals are mainly shown in the short‐ and medium‐term, while the long‐term
spillovers exhibit quite similar dynamics.
5.4
| Discussion
In the preceding sections, we found the volatility spillovers in the metal market exhibit a clear cyclical pattern. More
interestingly, we observed that the long‐term spillovers of precious and industrial metals show similar dynamics,
suggesting there may be common factors affecting the two metal markets. Considering that industrial and precious
metals are all denominated in US dollars, and the United States is the most important economy and global financial
center, we believe that the US monetary policy and the consequent value of the dollar may be important common
drivers.
The impact of the US monetary policy on metal prices has been investigated in many studies (e.g., Agbeyegbe, 1989;
Baffes & Savescu, 2014; Cabrales et al., 2014; Frankel, 2006). For instance, Baffes and Savescu (2014) pointed out that
low‐interest rates could exert upward pressure on metal prices by (1) promoting industrial production, which may lead
to an increase in current (and future) demand for metals; (2) reducing inventory costs (including warehousing and
financing costs), to increase the firms' desire to hold metal inventory; (3) inducing demand for metal futures contracts
by portfolio managers of investment funds; (4) leading to the depreciation of the US dollar.9 Furthermore, the expected
rise in metal prices will attract more investors to build positions in the metal market. Investors (especially speculators)
tracking market performance can improve the information efficiency, which is corresponding to high volatility spillovers in this paper. The tightening of the US monetary policy may have the opposite effect.10
We mainly investigate the dynamics of long‐term volatility spillovers, which are are usually due to changes in
economic fundamentals (see Liang et al., 2020). As shown in Figure 7 (bottom part), both precious and industrial
metals, long‐term spillovers remain at a high level in 1996, and then show a downward trend. This can be explained by
the sharp rise in the interest rates and the dollar index (see Figure 8). High interest rates and the appreciation of the
dollar could put downward pressure on metal prices, and inhibit investment in the metal market (especially long‐term
investment), thus reducing the efficiency of information transmittion (i.e., volatility spillovers). The Asian financial
crisis broke out in mid‐1997, we can see that the short‐term spillovers rose sharply, driven by the increasing hedging
demand for precious metals. However, we can see that the long‐term spillovers are reduced, which may be related to
high interest rates inhibiting long‐term investment.
The turning point appeared in 2004, and then the spillovers turned to an upward trend. It can be found that this
pattern may also be related to the US monetary policy. After 2001, the Federal Reserve drastically reduced interest
rates. At the same time, the dollar began to depreciate after 2002. In particular, the real interest rate fell below zero in
8
This can also be found in Ciner et al. (2018), Uddin et al. (2019), and Y. Wang et al. (2020).
9
It is worth mentioning that low‐interest rates may also reduce the cost of capital associated with increased investment and current (and future) metal supply. However, since the metal supply is
inelastic in the short and medium‐term (see Figuerola‐Ferretti et al., 2015). As a result, lower interest rates are expected to lead to higher metal prices.
10
However, this does not mean to say that there must be a causal relationship between the US monetary policy and the volatility spillovers in the metal market. The US monetary policy and metal
prices can be affected by some common factors. For instance, a global economic boom could boost both interest rates and metal demand. Besides, rises in metal prices may cause higher inflation
expectations, which may lead to a monetary policy tightening and increases in interest rates (see Akram, 2009; Hammoudeh & Yuan, 2008).
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F I G U R E 8 The US interest rate and
dollar index (1993–2019). “Fed rate” denotes
the daily US Federal funds rate. “Real
interest rate” is the monthly 1‐year
US Treasury yields minus the consumer
price index growth rate. “Dollar index”
denotes the monthly US dollar index.
Source: Refinitiv Datastream [Color figure
can be viewed at wileyonlinelibrary.com]
TABLE 6
Pearson correlations between the two metal groups' volatility spillovers
1995–2003
2004–2019
1995–2019
Total spillovers
0.055
0.319***
0.358***
High‐frequency
−0.023
0.332***
0.396***
Middle‐frequency
−0.345***
0.443***
0.484***
0.730***
0.700***
0.752***
Low‐frequency
***Significance at 1% level.
2002. Loose monetary conditions pushed up the metal prices and promoted investment in the metal market, which may
increase the volatility spillovers. For later periods, we can also find that there is a significant correlation between the
long‐term spillovers and the US monetary policy. For instance, the real interest rates and the dollar index rebound in
2012 and then maintained an upward trend, while the long‐term spillovers exhibited an downward trend.
Lastly, we discuss whether financialization has enhanced the commonality between precious and industrial metals.
Cheng and Xiong (2014) state that the net exposures of commodity index traders (CITs) grew dramatically after‐2004,
leading to the so‐called financialization of commodity markets. More precisely, the CITs treat commodity futures as an
asset just like stocks and bonds, and invest in instruments related to basic indices, such as the Goldman Sachs
Commodity Index (GSCI). The expansion of the positions (long or short) by CITs may cause price comovement for
different commodities. Based on Tang and Xiong (2012) and Cheng and Xiong (2014), we set 2004 as the breakpoint of
financialization and divide our sample into before‐2004 and after‐2004. Table 6 shows the correlations between the
spillovers of the two metals. It can be found that the correlations changed significantly after 2004. Taking the total
spillovers for an example, the correlation before 2004 is very small (0.055) and insignificant. But after 2004, it increases
to 0.319, which is significantly positive at the 1% level. The above results indicate that financialization has enhanced the
commonality between the two metals.
In summary, we found that there is a correlation between the volatility spillovers in the metal market and the US
monetary policy (and the consequent value of the dollar). Moreover, we found that the commonality between precious
and industrial metals is strengthened by the financialization of the commodity markets after 2004.
6 |
CONCLUSIONS
Metals are major commodities in the world. Gold and other precious metals (silver, palladium, and platinum) are
known as good hedging and safe‐haven assets in financial markets, while industrial metals are important sources of
raw materials for the majority of the manufacturing and mining industries. This paper examines the volatility spillovers
LIU
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1393
across precious and industrial metals over the period 1993–2019, based on the Diebold & Yilmaz (2009, 2012) and
Baruník and Křehlík (2018) methods. In general, we found that the volatility spillover across industrial metals is higher
than across precious metals. However, the situation is very different during the crisis periods. The empirical results
show that the volatility spillovers of the precious metals are higher than that of the industrial metals, during the periods
of the Asian financial crisis (1997–1998) and the global financial crisis (2007–2009). Also, precious metals contribute
net volatility spillovers to industrial metals during crisis periods. Further, from the frequency spillovers, we find that
the unique dynamics in the volatility spillover of two metals are mainly shown in the short‐ and medium‐term
components, while the long‐term components are almost similar.
Understanding the volatility spillovers between metal markets is vital to market investors, metal traders, and
industrial manufacturers. By identifying the direction and size of the volatility spillovers, one can anticipate price
movements in relatively less liquid commodities that incorporate information less rapidly than others. The application
of the interaction between time‐varying volatility and influential events improves volatility forecasting and enhances
market participants' ability to accurately measure the price risk in metal markets. For the metal market participants,
our results of directional spillovers show the market with low liquidity and how shocks propagate between markets.
Price movements, in relatively less liquid commodities, could be anticipated, which can be used for derivatives
management and risk hedging. For example, portfolio managers could utilize these outcomes for predicting metals
volatility. Financial institutions could make use of the linkage results to forecast metals market trends and improve
their hedging performances. Also, volatility connects strongly in periods with economic events. Policymakers can use
this information for making policy about supply and price adjustment. In summary, our empirical results can address
issues concerning hedging, portfolio, and derivatives management.
ACKNOWLEDGMENTS
This paper is supported by the National Natural Science Foundation of China (Nos. 71701176 and 72071166), Fundamental Research Funds for the Central Universities (No. 2072019029), and Science and Technology Projects of
Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) (No.
RD2020060101).
DATA A VAILABILITY S TATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
ORCID
http://orcid.org/0000-0003-2990-2780
Tangyong Liu
http://orcid.org/0000-0002-0862-2766
Xu Gong
Boqiang Lin
http://orcid.org/0000-0002-1308-400X
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How to cite this article: Liu, T., Gong, X., & Lin, B. (2021). Analyzing the frequency dynamics of volatility
spillovers across precious and industrial metal markets. Journal of Futures Markets, 41, 1375–1396.
https://doi.org/10.1002/fut.22217