Global financial crisis and rising connectedness in the international commodity markets

Accepted Manuscript Global financial crisis and rising connectedness in the international commodity markets

Dayong Zhang, David C. Broadstock PII: DOI: Reference:

S1057-5219(18)30458-7 doi:10.1016/j.irfa.2018.08.003 FINANA 1239

To appear in:

International Review of Financial Analysis

Received date: Revised date: Accepted date:

27 May 2018 24 July 2018 8 August 2018

Please cite this article as: Dayong Zhang, David C. Broadstock , Global financial crisis and rising connectedness in the international commodity markets. Finana (2018), doi:10.1016/ j.irfa.2018.08.003

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ACCEPTED MANUSCRIPT Global financial crisis and rising connectedness in the international commodity markets Dayong Zhanga and David C. Broadstockb

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Abstract This paper documents a dramatic change in the nature of connectedness in global commodity prices following the 2008 global financial crisis. We show that co-dependence in price-changes among seven major commodity classes goes from a pre-crisis average of 14.82% to a strikingly larger average of 47.87% in the period following the crisis, and which has endured until now. Dynamic swings in price co-movements of such a scale present a clear concern for financial investors and are of immediate interest to a wider policy-maker audience. Of particular interest is the empirical behavior of the food commodity price index, whose contribution to the system dynamics rises from less than 20% in the period up to 2008, to more than 80% after. To dispel any concern that these finding may be method-specific, we demonstrate their invariance to modeling procedure by providing analogous-results using a pairwise Granger causality analysis, as well as different sub-sampling choices.

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JEL: O13, Q02, Q11 Keywords: Connectedness; Commodity; Financial crisis.

(corresponding author) Research Institute of Economics and Management, Southwestern University of Finance and Economics, 555 Liutai avenue, Chengdu, China. 611130, +86-28-87092878, [email protected]. b School of Accounting and Finance, The Hong Kong Polytechnic University, Hong Kong. [email protected]

Acknowledgements: We thank the National Natural Science Foundation of China (NSFC) for continued financial support under grant number 71573214. Comments and suggestions from three anonymous referee and the guest editor are appreciated.

ACCEPTED MANUSCRIPT Global financial crisis and rising connectedness in the international commodity markets 1. Introduction

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In 2008 the global financial community bore witness to the true extent to which international markets are connected. It was not merely the case that the US sub-prime

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mortgage crisis generated ripples that were felt in other markets. Rather it was the

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case that markets had in fact become so intricately connected to each other, that it was

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impossible in one market to effectively shield against risks faced in another. This ‘connectedness’ across global financial markets, though a source of competitive gain,

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had proved itself also as a mechanism for propagating risk.

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Questions are now emerging as to how we can adequately quantify the systematic risks that come attached with connectedness, the answers will play an important role

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in redefining traditional market mechanisms to accommodate the new financial landscape that exists today. The concept of systemic risk is complicated in nature.

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Billio et al (2011) define systemic risk in financial markets as the risk that “threatens the stability of or public confidence in the financial system”. Smaga (2014) meanwhile reviews a collection of literature related to this topic and suggests that ‘systemic risk’ is risk that results in significant (material) imbalances and impairment in the functioning of the financial system. In this paper, we follow Diebold and Yilmaz (2009) to use the estimated connectedness of a system as a proxy measure for the level of systemic risk. The method

ACCEPTED MANUSCRIPT essentially permits us to test how pervasive risk is throughout a financial system, in response to episodes of price uncertainty from a specific source e.g. whether price variability within a given commodity (class) creates material instabilities that permeate through to the remainder of the wider commodities market in a

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non-trivial fashion.

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A number of market analysts and scholars have begun to examine empirical patterns of financial market connectivity and its consequences to equity and stock markets –

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see Section 2 for examples of related literature. However, little attention has so far

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been placed towards studying connectivity in global commodity markets. It is no stretch of the truth to claim there have been interesting things happening in

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international commodity markets during recent years. For example, there has been a

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dramatic collapse in oil prices (and a sustained period of probable ‘underpricing’), as well as some notable fluctuations in precious metals–which not only face downward

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price pressure arising from a correlation with oil prices, but also upward pressure

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arising from a ‘flight-to-gold’ amidst deepening global economic uncertainty - with global growth having put the brakes on. As a commodity, oil is yet more interesting to financial market analysts, since there is growing concern that it has become financialized (Cheng and Xiong, 2014; Zhang, 2017). While academic opinion remains mixed, with evidence both for and against financialization hypotheses, the existence of the debate alone is very telling. Concerns remain over the endemic risks that can potentially be transmitted through commodity markets.

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In light of these concerns, and due to the need for firms, investors and wider market participants to remain best informed, the big question towards which this paper directs itself is whether commodity price uncertainty is driven, at least in part, by network

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connectedness? To address this overarching research question the study will need to

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also consider the following related questions: First, how much are international

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commodity markets–as measured through price movements–connected with each other? Second, among the different commodity classes, which (if any) have played the

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most important role (i.e. with strongest connectedness) in the international commodity

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market? Consistent with the special emphasis and prominence it receives in the literature, we will give extra attention to determining whether the unique role of oil

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has strengthened over time or otherwise. Third, what role has the 2008 global

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financial crisis played in shaping the patterns of connectivity between commodity classes? Or, more generally, does the structure of international commodity markets

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exhibit any patterns of major change over time? Lastly we consider whether the

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observed impacts to commodity price connectivity stemming from the global financial crisis are reversible, or if instead they are symbolic of a new state of global commodity markets?

To provide answers to the above questions we adopt the methodological framework of Diebold and Yilmaz (2009, 2012, 2014). In their earliest work, Diebold and Yilmaz (2009) outlined an elegantly simple framework for exploring the degree of

ACCEPTED MANUSCRIPT connectedness between different markets. The simplicity of their method derives from the fact that it builds on the already widely accepted vector auto-regression (VAR) methodology, thus offering a small learning curve to analysts. The elegance follows from the almost trivial re-interpretation of forecast error variance decomposition that

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permits the VAR results to be interpreted in a whole new way, that is much more

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amenable to policy relevant discussion. In this paper, similar to applications

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elsewhere, we apply VAR models to commodity price data within a rolling-windows regression design, from which Diebold-Yilmaz measures of connectedness can be

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obtained which are variable over time. This approach will, importantly, allow us to

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examine how the connectivity between commodity prices has been evolving over time. Network graphing methods are used to give a visual characterization of how sectors

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are connected with each other.

In any study it is important to ensure that the conclusions are not incidental i.e. due

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to sample choices, methodological approaches etc. Data issues are not a primary

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source of concern in our study, instead it is more important to focus on whether our conclusions would have materially differed had we approached the same data/research questions with a different empirical methodology. To this end we examine what implications can be derived from a Granger-causality based approach to the analysis of systemic risk, as outlined by Billio et al. (2012). In brief, while some (expected) numerical differences occur, the qualitative results of the study are unaffected by (or are invariant to) our modeling choice. Perhaps the most valuable contribution to

ACCEPTED MANUSCRIPT highlight is the surprisingly large role of food commodities within the evolved structure of commodity markets which emerged following the 2008 global financial crisis, which lead to the valuable discussions and future research directions.

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The order of the paper is as follows: In Section 2 we review recent literature that is

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related to our study. Section 3 outlines the adopted methodology, while the data are

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summarized in Section 4. The main results of the analysis are presented in Section 5.

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We conclude the paper in Section 6.

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2. Literature review

In this part of the paper we set the context of our study against existing related

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literature. There is a vast body of literature examining aspects of commodity pricing,

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and it is not the intention here to give an exhaustive review. Rather, the objective of our review is to establish the salience of the research question(s) and to verify that

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there is a sufficient gap in the literature to be able to claim with plausible confidence

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that our results are making a relevant, and timely, contribution.

Increasing fluctuation of commodity prices since the turn of the century has triggered a boom in literature studying the behavior of commodity prices. Arezki et al. (2014) and Fernandez et al. (2018), for example, use the analogy of a “roller coaster” ride to refer to recent commodity price changes. Among the main commodities, oil prices have experienced the most dramatic changes in recent years, spawning a special

ACCEPTED MANUSCRIPT interest in oil prices among empirical researchers. Record levels in food commodity prices have also raised clear concerns and insecurity across the world (Tadesse et al., 2014). Understanding the patterns and driving forces of such price movements in the

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international commodity market requires more effort by scholars.

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One of the three key questions in a recent editorial piece by Arezki et al. (2014)

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concerns whether commodity prices are increasingly financialized. This issue has been considered by a number of researchers in recent years. The concept and

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existence of financialzation has been well established in energy markets (Zhang,

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2017), and commodity markets in general (Cheng and Xiong, 2014). Once it is recognized that commodities have now one of the core asset classes used by

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financial investors (i.e. financialized), our understandings of the operation of

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commodity markets begins to differ significantly. Other than the standard demand and supply conditions, speculation and the demand for portfolio

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rebalancing by financial practitioners can contribute huge impacts to the

prices.

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international commodity markets and cause potentially higher volatility in

Considering some of the research on portfolio construction, Oztek and Ocal (2017) use GARCH type models to study the correlation between stock markets and prices in two commodity classes, namely agricultural commodities and precious metals, in a dynamic way. Their findings show that including commodities into investment

ACCEPTED MANUSCRIPT portfolios can offer higher gains especially during calm periods, or in other words when financial markets are bearish. Ait-Youcef (2018) explicitly investigates the concept of financialization in commodity markets by testing whether index investment can have consequences to commodity prices. Using a Threshold

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Autoregressive Quantile Regression (TQAR) model, he finds that stock markets do

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affect agricultural price movements, and the relationship is significant during periods

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with extreme movements, such as the 2008 global financial crisis period. This result is consistent with Zhang (2017), who also finds that global stock markets affect oil price

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movements significantly during the period after the global financial crisis.

Another important question raised by Arezki et al. (2014) concerns the adoption of

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new techniques to understand and forecast price movements in commodity markets.

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Factor models, for example, are used intensively to identify co-movements of commodity prices and also to understand the driving forces behind price changes.

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Popular factors that may cause commodity prices to change include energy price

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shocks (Ji and Fan, 2012, Wang et al. 2014, Cabrera and Schulz, 2016); foreign exchange rates (Zhang et al., 2016, Bodart et al., 2012); macroeconomic variables (Hammoudeh et al., 2015, Smiech et al., 2015); financial/derivatives markets (Mellios et al., 2016, Berger and Uddin, 2016)

Byrne et al. (2013) use nonstationary panel models incorporating a factor augmented vector-autoregressive (FAVAR) framework to reveal evidence of a common factor

ACCEPTED MANUSCRIPT that drives co-movement in commodity prices. West and Wong (2014) use monthly prices of energy, metal and agricultural commodities within a factor model, and find evidence of price reverting to the factor. Beckmann et al. (2014) question whether monetary policy can help in explaining global commodity price changes. The impact

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of global liquidity has also been found to affect commodity markets significantly and

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have time-varying features. Yin and Han (2015) decompose commodity returns into

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three components in global level, sectoral level and commodity level. Their dynamic

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latent factor model shows that a global factor has become more important since 2004.

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Various other time series approaches have also been used in recent studies. For example, dynamic conditional correlation (DCC) models (Cabrera and Schulz, 2016,

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Mensi et al., 2014, Ohashi and Okimoto, 2016, Berger and Uddin, 2016) have been

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used to accommodate heteroscedasticity in the underlying price movements and time-varying correlations. Acknowledging the problem of interaction and the

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endogenous nature of the interplay between commodity prices and economic

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indicators, the VAR model has also been used in some recent studies. For example, Wang and McPhail (2014) use a structural VAR model to study the connections between US energy prices, economic output, exports and agricultural commodity prices. They find energy price shocks and agricultural productivity shocks can help in explaining price volatility of the US agriculture commodities. Issler et al. (2014) use the vector error correction model (VECM) to study the short-run and long-run co-movement in metal commodity prices. Their results show that incorporating

ACCEPTED MANUSCRIPT common-cycle restrictions can help in producing more accurate forecasts of commodity prices.

Turning briefly towards the main methodology adopted in our present study, since the

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seminal paper by Diebold and Yilmaz (2009) the concept of dynamic connectedness

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has been widely studied. The method, outlined in detail below, has proved an

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extremely powerful tool for studying the linkages across markets. It’s key advantages being that it has greater power in describing connectivity between system variables,

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and hence notions of systemic risk, while maintaining all of the advantages that

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standard VAR systems have for coping with complex endogeneity in system variables. Zhang (2017) for example uses this method to investigate the role of oil price shocks

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to international stock markets and finds that oil prices have become more

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financialized since the 2008 global financial crisis. Yip et al. (2017) extend the basic spillover index framework to a fractionally integrated VAR model to study dynamic

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linkages between commodities and commodity currencies for data observed at a

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higher frequency such as daily, or intraday. The method has also been used in some recent studies, for example, Yang and Zhou (2016), Zhang et al., (2018). It is shown to be simple but very useful, though we are not claiming the method is superior to others and thus a Granger causality base approach is used later to check its robustness.

In summary, our brief review of the literature points towards a growing interest in

ACCEPTED MANUSCRIPT uncovering connectivity in global commodity prices, and in using modern time-series econometric techniques to permit potentially time-varying relationships to be tested for. The literature in this area is not young as such, yet is still at a relative stage of infancy and for obvious reasons. Commodity markets and trading mechanisms are

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continuing to evolve, and the interplay between various commodity markets should be

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evolving. Recent methodological advances also make it easier to explore the

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time-varying relationships. Here, to contribute much needed evidence to this area of research, we therefore extend the Diebold Yilmaz (2009, 2012, 2014) framework

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towards the analysis of global commodity markets

3. Methodology

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The notion of network connectedness stems from a recent methodological innovation

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(Diebold and Yilmaz, 2014), which fuses standard network theory with mainstream dynamic econometric methods, here the vector auto-regression (VAR) model. The

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method provides a novel way of exploring the dynamics of a system via repackaging

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variance decomposition results of the VAR model. It shows the relative importance of factors clearly without being troubled by the typical significance issues can be applied to investigate many policy relevant topics. 3.1 The Diebold-Yilmaz Spillover Index and connectedness measures In a typical macroeconomic system, all variables are considered endogenous and continuously interacting with one another. Often the expected complexity of the underlying macroeconomic model driving the relations makes it difficult to develop a

ACCEPTED MANUSCRIPT tractable/precise theoretical model for the underlying system that can be estimated structurally.

Sims

(1980)

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the

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accommodate/resolve this type of econometric issue, recognizing that a simple system framework can still lead to useful insights and accurate descriptions of economic

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become the work-horse of mainstream macro-economics.

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system dynamics in the presence of complex endogeneity. The VAR system has

The VAR model, with many parameters to be estimated, is too complicated to

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interpret in a standard way i.e. by direct inspection of the coefficient values alone.

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Instead, one of two popular methods are often used to interpret a VAR model: first is the use of so-called impulse response function (IRF) and second is the analysis of the

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forecast error variance decomposition (FEVD). Both these tools are forward looking,

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for instance the IRF tells a story of how a shock to one variable is met by responses from other components of the system. The FEVD on the other hand provides

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information as to the proportion of variation in future values of a system variable that

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is explained by other variables in the system. For the purpose of modelling inter-connectivity within the system, FEVD is the suitable method as the IRF is difficult to aggregate.

Diebold and Yilmaz (2009) provide a relatively simply yet extremely innovative twist to the standard VAR interpretation tools, repackaging the FEVD results in such a manner that they are capable of producing a snapshot of network connectedness. The

ACCEPTED MANUSCRIPT steps involve placing aggregations of standard FEVD’s into what is known as a ‘connectedness matrix’ whose off-diagonal elements explain the proportion of future prices that were due to endogenous interactions with other variables in the system e.g. effects that spilled over from another part of the system. Thus the connectedness

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matrix can be thought of as offering a snapshot of systemic risk. The diagonal

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elements essentially summarize how the future values of a variable are driven by

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changes in itself. A more complete description will be given below after providing a

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more formal description of a standard VAR system.

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For an N-variable VAR(p) model we have:

(1)

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𝒀𝑡 = 𝑪0 + ∑𝑝𝑖=1 𝑪𝑖 𝒀𝑡−𝑖 + 𝜀𝑡

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Where Y is a vector containing the N variables in the system that are assumed to be endogenously related to each other. 𝑪0 is a N × 1 vector of constants, while 𝑪𝑖 is

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terms.

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N × N matrix of coefficients for each lag, and ε~(𝟎, 𝚺) is the vector of i.i.d error

Equation (1) shows that each variable is explained by the lagged value of all other variable and itself. The total number of parameters to be estimated in this VAR model is 𝑁 × (𝑝𝑁 + 1), and the proliferation of parameters in these systems make them difficult to interpret in a standard way. Estimation of the VAR model is however straightforward and it has been shown that the ordinary least square (OLS) method

ACCEPTED MANUSCRIPT can be applied to each equation to obtain coefficients that will result in accurate (valid) IRF and FEVD results.

After estimating the VAR model, the FEVD approach to analysis is applied by first

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decomposing the H-period-ahead forecast error, where the contribution of variable j to

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variable i is normally denoted as 𝜃𝑖𝑗𝐻 . The value of 𝜃𝑖𝑗𝐻 converges to a constant

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number when H goes beyond the VAR lag p, and in general practitioners should pay careful attention to the VAR lag-length selection typically using various information

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criterion. Following Diebold and Yilmaz (2009), the FEVD values can be written in to

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a connectedness matrix. However, the original idea presented in Diebold and Yilmaz (2009) has a major shortcoming since FEVD results are known to be sensitive to the

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ordering of the variables in a VAR model. Accordingly, the results obtained from a

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system may vary for one setup of the system compared to another setup with variables in a different order. To remedy this issue, Diebold and Yilmaz (2012) suggest to use

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the generalized FEVD method due to Koop et al. (1996) and Pesaran and Shin (1998).

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This modification ensures robustness of the results to the variable ordering problem. Following this important econometric refinement, the spillover framework has found wide application in the investigation of systemic risk and sectoral spillovers (Antonakakis et al, 2014; Zhang, 2017; Zhang et al., 2018).

Diebold and Yilmaz (2012) concentrate attention on three summary measures of network connectedness and spillovers. These are: the connectedness based on

ACCEPTED MANUSCRIPT spillovers emerging from variable i and going to all other variables in the system ‘From’; the sum of spillovers going to variable i from all other variables in the system ‘To’; and finally the balance of connectedness, or net directional connectedness, that exists between variable i and the remainder of the system ‘NDC’. These measures can

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be written as follows: ̃𝐻 𝐹𝑟𝑜𝑚𝑖 = ∑𝑁 𝑗=1 𝜃𝑖𝑗 , for j ≠ i

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̃𝐻 𝑇𝑜𝑖 = ∑𝑁 𝑗=1 𝜃𝑗𝑖 , for i ≠ j

(2)

(3)

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𝑁 ̃𝐻 ̃𝐻 𝑁𝐷𝐶𝑖 = ∑𝑁 𝑗=1 𝜃𝑗𝑖 − ∑𝑗=1 𝜃𝑖𝑗 , for i ≠ j

(4)

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Here 𝜃̃𝑗𝑖𝐻 denotes pairwise contributions defined in combination with a generalized

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FEVD. Finally, the strength of systemic risk is obtained using the relationship:

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𝑁 ̃𝐻 𝑆𝑌𝑆 = 𝑁 ∑𝑁 𝑖=1 ∑𝑗=1 𝜃𝑖𝑗 , for i ≠ j

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Which gives a straightforward to interpret measure of systemic risk, that ranges between 0 and 1, respectively denoting the cases where no system risk exists to the

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case in which all risk is driven by system interactions/dynamics. Diebold and Yilmaz

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(2014) further explore the idea of connectedness using network theory and suggest using network graphs to illustrate how the system interacts. For all pairs of variables in the system, it is possible to show their relative contribution (pairwise net contribution) by constructing the value 𝑃𝑁𝐶𝑖𝑗 = 𝜃̃𝑖𝑗𝐻 − 𝜃̃𝑗𝑖𝐻 . A positive value of 𝑃𝑁𝐶𝑖𝑗 indicates that variable j explains more than it gains from variable i, or net contributor. An arrow can be drawn from variable j to variable i in this case. All pairs in this system can then be visually connected into a network showing how the system works.

ACCEPTED MANUSCRIPT Such a network diagram quickly summarizes where risk exists, and in which direction it flows.

3.2 Connectedness measures based on pairwise Granger causality

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Billio et al (2012) introduce an alternative, and arguably simpler, way to measure

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systemic risk in financial markets. Their method is based on pairwise Granger

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Causality measures. Granger causality is a simple statistical test procedure to examine whether the lagged values of one variable are useful in explaining the future values of

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another variable. For example, consider the relationship between variables 𝑦𝑖 and

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𝑦𝑗 with p=2 lags. We can run the following regression:

(6)

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𝑦𝑖𝑡 = 𝑐0 + 𝑐1 𝑦𝑖𝑡−1 + 𝑐2 𝑦𝑖𝑡−2 +𝑑1 𝑦𝑗𝑡−1 + 𝑑2 𝑦𝑗𝑡−2 + 𝜖𝑡

A joint significance test on 𝑑1 = 𝑑2 = 0 can be used to test whether 𝑦𝑗 Granger

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causes 𝑦𝑖 . With this simple idea of causality in mind, in a VAR system with N

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variables, there are up to a total of 𝑁 × (𝑁 − 1) pairs of Granger casual relationships that can be tested for. If there is no Granger causality relationship found in the system, then all variables can be considered independent; the more (significant) causal relationships exist in the system, the higher systemic connectivity (or systemic risk) there is in this system. Billio et al. (2012) therefore suggest to use the share of significant Granger causal relationships as a proxy for systemic risk.

ACCEPTED MANUSCRIPT 4. Data For this study we use monthly data on commodity prices taken from the World Bank commodity price indices. These data are provided at a monthly frequency, from January 1960. To exclude the effects of the collapse of the Breton Woods System as

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well as the major oil market crises in the 1970s, our estimation sample starts from

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January 1982. The sample ends in June 2017, which leaves us 426 observations (425

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for returns, which start from February 1982).

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Our analysis concentrates on six broader commodity indices plus one special

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commodity, crude oil price (average spot prices). Crude oil price, as a proxy for energy in general, has often been considered a strategically important factor of

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production in modern economic systems, and can thus create a broad spectrum of

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impacts to different aspects of the global economy (e.g. Cashin et al., 2014). Oil prices are often featured in studies that explore relationships between key commodity

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markets. For example, Avalos (2014) investigates whether oil prices drive food prices,

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Algieri and Leccadito (2017) investigate risk contagion between commodity markets and the whole economy, where both energy and non-energy commodity prices are considered. We choose not to use other energy prices e.g. natural gas prices, for the following reasons: first, crude oil has an international market and its benchmark prices, such as Brent crude or the WTI price, are globally recognized. For natural gas on the other hand, the physical trade markets/hubs are regionally segmented (Zhang et al., 2018). Further to this, natural gas

ACCEPTED MANUSCRIPT prices/contracts for trade are often indexed to crude oil prices, meaning that natural gas prices are unlikely to provide additional information beyond that already reflected in the oil price. While there are several recent studies suggesting a potential separation between natural gas prices and crude oil prices,

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they have been throughout our sample period and still are highly correlated with

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each other.

The other commodity indices include: Beverage, Fertilizers, Food, Metal, Precious

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metal (Precious in short), Raw materials (Raw in short). Using indices instead of

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individual commodity prices allows us to narrow focus on the core market dynamics between major commodity types, rather lessening the risk of losing focus by

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attempting to explain a greater range of idiosyncratic dynamics of more marginal

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interest. The data series are plotted in Figure (1), while descriptive statistics are reported in Table 1. All return series reported are in percentage points. Among all

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seven indices, precious metal has the highest average return for the full sample period.

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Oil stands out among the series, having the most volatile price changes of all, while its average return is the second lowest. All return series are non-normally distributed with high level of kurtosis.

(Insert Figure 1 here) (Insert Table 1 here)

ACCEPTED MANUSCRIPT Pairwise correlations are presented in a heat map, see Figure (2), to give a visual reference as to how these commodities are linked with each other. Brighter colors in the series, e.g. the yellows observed for Metals prices, reflect a stronger role in this system, symbolizing a higher correlation for that element of the correlation matrix.

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Among all series, Metals have the highest average correlation with the other series

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(mean value equals 0.293), whereas Fertilizers have the lowest average correlation

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with the rest of the variables in the system (mean value equals 0.144).

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(Insert Figure 2 here)

5. Empirical results

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In this section we present and discuss the results from the econometric modeling. First

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we illustrate the full-sample connectedness. After this we proceed to describe the results obtained using a rolling-windows implementation of the methodology. We feel

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the rolling windows approach to be much more informative, capturing the changing

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trends in connectedness and being more robust to the presence of structural breaks in the data. Towards the end of this section we will also implement the pairwise Granger causality approach due to Billio et al (2012) to demonstrate that our key findings are largely invariant to modeling assumptions.

5.1 Full sample connectedness We begin our analysis by considering results obtained from a 7-variable VAR,

ACCEPTED MANUSCRIPT including returns for: Beverage, Fertilizers, Food, Metals, Precious metal, Raw materials and Oil. The lag order of the system is determined using the Bayes-Schwarz information criterion (BIC). Total connectedness for the full sample is estimated at 24.58%. This number means that around one quarter of the variation in the

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system is due to inter-connections among individual variables and the remaining

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three quarters of variations are self-contributions. The dynamics of each of the

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commodity categories are mainly explained by themselves and not due to spillovers from other commodities, which indicates that the global commodity prices are largely

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(albeit not completely) independent, with the majority of price formation occurring on

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one quarter of total price changes.

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the basis of within sector sources. The interaction in this system accounts for around

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Regarding the directional nature of connectedness in the system, Figure (3) plots a network diagram of the pairwise connectedness patterns suggested by the full-sample

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analysis. Each node in this plot represents a separate commodity price (index). The

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arrows are set based on relative contribution between any two indices, i.e. if index A contributes more than it gains from B, then an arrow is drawn from A to B and vice versa. The commodity price index with the highest NDC (Metals) will be marked by red with all lines connecting with this index also shown in red, whereas the commodity class with the lowest NDC (Fertilizers) is shown with blue coloring. The plot

takes

a

hierarchical

structure,

placing

nodes

with

more

outward

ACCEPTED MANUSCRIPT connections/arrows higher on the y-axis.1 It can be seen that Metal lead Food, and then Precious , Oil, Raw and then Beverages, while Fertilizers sit at the bottom of the chain and are the largest net receiver of spillovers in the system.

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(Insert Figure 3 here)

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Complementary evidence of these spillover structures can also be taken from Table (2) which summarizes the connectedness matrix and scores for the measures ‘To’, ‘From’

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and ‘NDC’ as described above. Metal price changes make the highest contribution

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‘To’ system price dynamics (46.64%) and also take the highest observed value for NDC (14.49%). It receives the most information from others with the highest ‘From”

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lowest NDC (-18.99%).

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score (32.15%), whereas beverage receives the least (17.95%). Fertilizers have the

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(Insert Table 2 here)

In general, the full sample results are consistent with the patterns of association between the variables that is shown in the correlation heat map, where Metal played a pivotal role in the global commodity markets. Although Oil has generally been considered a critical resource and its price shocks (changes) are important to affect many macro-economic variables or financial markets, it shows no strong integration

1

It is worth to note that the node with highest NDC does not necessarily rank on the top.

ACCEPTED MANUSCRIPT with other commodities.

5.2 Rolling windows analysis An analysis using the full sample of data provides a useful preliminary assessment of

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the relationships and connectedness between commodity prices. However, as Diebold

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and Yilmaz (2009) among others argue, rolling-windows based estimation may help

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to reveal important dynamics, and at the same time provide robustness against possible non-stationarity and structural instability of the variables in the system. We

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therefore re-estimate the VAR system and obtain rolling-window estimates of

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connectedness, holding the window size fixed at one third of the sample size2. The main result is presented in Figure (4), and the change in trend pre- and

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post-September 2008 highlights a fundamental re-structuring of global commodity

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price dynamics. Prior to September 2008, the level of connectedness had an average

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of 14.82%. The number increases up to an average of 47.87% after the financial crisis.

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(Insert Figure 4 here)

One might attempt to attribute the observed dramatic changes in connectedness to the collapsing of commodity prices witnessed at the same time of the global financial crisis. There may be merit, and truth even in this logic, yet the more recent

2

We would like to thank an anonymous referee for commends received in relation to the window size.

We tested alternative choices for window size such as (1/4) of the total sample, the results remain qualitatively unchanged with only minor numerical differences.

ACCEPTED MANUSCRIPT commodity price declines from 2014 do not seem to result in the same instabilities. This offers indirect evidence to suggest that collapsing prices alone are not the root cause of dramatic changes in connectedness. Nonetheless it is fair to say that the 2008 global financial crisis has had a clear role to play in shaping the international

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commodity market, not simply providing a temporary negative shock, but the market

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has become clearly more connected. The sharp change in the rolling windows graph

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suggests that commodity market experiences a structural change. It is important to note that such stark differences are not clearly predictable from the raw data (refer

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back for example to Figure 1) and could not be anticipated in advance of the

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econometric work.

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Figures (5) and (6) examine the nature of directional connectedness across the

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rolling-window estimates, respectively illustrating the level of connectedness ‘from’ and ‘to’ other commodities. Not only do most of the indices gain more information

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from the system after the global financial crisis, their contributions to the system also

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varies significantly around the 2008 period of change.

(Insert Figures 5 and 6 here)

A couple of interesting features can be established from viewing these graphs. First, although Metal are still one of the most important commodities, its leading role has been replaced by Food following the financial crisis. Second, fertilizer price remains

ACCEPTED MANUSCRIPT the smallest contributor of spillovers to the system. Its contribution even fell slightly in the post-crisis period.

The rolling-windows estimation results are summarized in Table (3) and Table (4),

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providing further confirmation of the observations discerned from the graphs. Table

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(3) gives an overall description of the level of connectedness and reveals that Precious

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is most ‘balanced’ type of commodity. Its gain from/contribution to the system are roughly equal and have the lowest/second lowest variation of all series, depending on

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the sub-sample. Moreover, Oil, although expected to have a significant role in the

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international commodity market, rank second last among all indices in terms of its contribution to the system. There are a couple of possible explanations for the

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weak role of oil prices: first, the important role of oil prices in affecting

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macroeconomic dynamics (for example, Ji et al., 2015) or financial market dynamics (Broadstock et al., 2012; Liu et al., 2013) is because that crude oil is a

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critical industrial input factors. It does not necessarily apply to commodity

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markets and may be a receiver instead (for example, Ji and Fan, 2016); the second reasoning is that oil price mechanism is different from other commodities.

(Insert Tables 3 and 4 here)

Table (4) is more informative over the changes before and after the global financial

ACCEPTED MANUSCRIPT crisis. Using September 2008 as the splitting point3, it is clearly visible that almost all commodities gain/contribute significantly more information from/to the system with the unique exception of Fertilizers, which has on average a 3.24% lower contribution to the system after the financial crisis. Among all commodities, the contribution of

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Food has experienced the highest increase (72.10%), which also makes Food the

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biggest contributor of price risk in the post-crisis period. Raw, on the other hand, has

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among all commodities for receiving spillovers.

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gained an average of 39.66% more information from the system and ranked first

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(Insert Table 4 here)

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The findings regarding the dominant role of food prices is quite surprising yet

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reasonable if one looks at the recent developments in international food commodity markets. Global food commodity prices have been rising persistently and dramatically

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in the first ten years of the 21st century, and reached a historical record in December

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2010 (Hochman et al., 2014; Cuesta et al., 2014). The increasing demand for biofuels in the United States and Europe is considered an important driving factor for the food crisis – with the implication being that the displacement of crops for biofuels is reducing supply of land for food, and in turn food supply itself, acting to drive

3

With respect to the chosen break-date: On September 15, 2008, the financial services firm, Lehman

Brothers filed for Chapter 11 bankruptcy protection. It triggered the largest one-day drop of Dow Jones Industrial Average following the 9/11 attack in New York in 2001. This date is often considered as the key event of the 2008 global financial crisis and has been used elsewhere in the literature, see for example Zhang (2017), when working with monthly data.

ACCEPTED MANUSCRIPT equilibrium prices higher. Al-Maadid et al. (2017) also find a clear structural change in the food-energy nexus and discuss the role of 2008 global financial crisis in this relationship.

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5.3 Robustness checks: pairwise Granger causality approach

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The main results presented so far have been obtained using the recent methodological

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contributions of Diebold and Yilmaz (2009, 2012, 2014). Here we demonstrate that our results remain when using alternative methods to characterize the estimated

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spillovers such as Granger causality based approach introduced by Billo et al. (2012)

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to examine the relationships across multiple time series.

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(Insert Figure 7 here)

We take first the Granger causality based approach to modeling systemic risk as

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introduced by Billio et al. (2012). In Figure (7) we plot the rolling-window estimates

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of the share of significant Granger causal relationships (at a 10% level of significance) in all potential pairs. The solid line in this plot reflects the connectedness measures obtained using the Diebold-Yilmaz approach, for comparison, and the dashed line shows the Granger causality based results using the Billio et al. (2012) approach. The Granger causality based approach produces visibly more volatile dynamics over time when compared against Diebold-Yilmaz spillovers, though the identified trend remains broadly similar. On the basis of these results we therefore continue to observe

ACCEPTED MANUSCRIPT the same underlying economic implications.

5.4 Robustness checks: sub-sample analysis We also take time to verify the robustness of our conclusions to alternative

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sub-sampling choices. Given the findings of a clear structural break in the 2008 global

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financial crisis period. It is further necessary to confirm our initial results using

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sub-sample analysis. Using sub-samples to re-estimate the model has also benefits

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avoiding the overlapping structure of rolling-windows approach.

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We use September 2008 as the sample break point, which is also consistent with the timing of the bankruptcy of Lehman Brothers on major onset of the global financial

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crisis. The pairwise net connectedness nodal graphs for the two sub-samples are

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provided in Figure (8). The Fertilizers is consistently the biggest net receiver in the system, food has replaced metal to be the most important commodity or information

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leader in the system after the financial crisis. And yet again, Oil is ranked among the

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lowest group in both samples. In this vein, we verify that our results obtained using rolling regressions are broadly consistent with a relatively simpler sub-sample based splitting of the data across the pre- and post-financial crisis periods.

(Insert Figure 8 here) 5.5. Implications of the results for food policy We briefly consider the implications of our analysis to global food policy issues. One

ACCEPTED MANUSCRIPT of the major findings of this paper is a clear rising importance of food commodity prices in the global commodity market. Given the focus of our paper is ultimately concerned with documenting changing patterns within the global commodity market from a perspective of financial risk, and therefore keep this section suitably brief.

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The International Food Policy Research Institute (IFPRI) outlines five strategic focus

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areas as priorities for ongoing research and food policy debate. Among these five

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areas of focus is ‘Building inclusive and efficient markets, trade systems and food industry’, with a separate focus area being upon ‘strengthening institutions and

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governance’. One way to view the results of our analysis are that they imply a

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dramatically changed pattern of commodity market efficiency, which in turn raises questions over the capacity of existing institutions and governance structures. To

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elaborate, increasing spillovers are consistent with growing inefficiencies in the

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market place, where factors beyond the underlying supply and demand conditions, are seemingly creating and potentially sustaining food price distortions. Perhaps the first

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order question is to establish whether the existing national and global institutions have

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recognized these changes, and what they reveal about the potential need for an expanded capacity (in terms of skills and/or staff numbers) among the institutions that govern global food markets. Further work needs to address this and related issues more carefully. Rather, it is the compelling and seemingly irrefutable evidence that food has boomed in importance in this market that causes us to need to pause and reflect. Yet this should not be done cursorily, and requires different data and analyses developed with specific research

ACCEPTED MANUSCRIPT questions in mind.

6. Conclusions In this study we document a striking increase of the level of connected risk in global

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commodity markets. Of chief interest is the growing dependence between food

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commodity prices and international oil prices. Using the Diebold and Yilmaz (2009,

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2014) method for measuring connectedness, we investigate how international commodity markets are linked across time. The average level of connectedness

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estimated over the full sample of data is modest, at only 21.74%, meaning that the

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remaining 78.16% of variation is due to the variation of the variables themselves. However, our assessment of the situation becomes tremendously more informative

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once we allow time varying properties to be considered via a rolling-windows

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approach to estimation. A clear structural break of the connectedness, and hence systemic risk in commodity prices, can be observed. Average connectedness rises

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from around 15% prior to the global financial crisis up to 50% in the period after 2008.

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There is a much more pervasive nature of systemic risk in global commodity prices, and the fact that such a step-change in risk can occur in such a short period of time should be a cause of concern for market participants and policy makers alike.

Fertilizers appear to be the category of commodity prices that are in general least exposed to systemic risk, whereas the food price index replaced metals to be the most important commodity class after the crisis. The results are robust to using the Billo et

ACCEPTED MANUSCRIPT al. (2012) Granger causality approach, and also confirmed by subsample analysis. We can effectively claim that the international commodity markets have gone through a major change after the financial crisis. The findings here provide important evidence to practitioners in the financial market i.e. commodity investors, as well as to policy

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makers needing to understand the dynamics of international commodity markets and

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pre-empt periods of excessive price instability which can often induce localized but in

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some cases severe economic consequences.

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The 2008 global financial crisis had profound impacts to the international

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economic system. One of the major arguments in the recent relevant literature is something called “financialization” (i.e., Cheng and Xiong, 2014). Our results

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provide evidence of a structural change in the global commodity markets.

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Although it is too early for us to claim that this change is permanent and due to commodity financialization, the evidence in this paper seems to be consistent

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with the financializatin literature that market fundamentals are different now. It

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might be necessary to revisit this issue again when more data is available, but at least for now, the change in the market structure does seem to be a long last case.

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ACCEPTED MANUSCRIPT Table and Figures Table 1. Descriptive statistics of returns Std. Dev.

Skewness

Kurtosis

Jarque-Bera

0.022 0.124 0.105 0.191 0.258 0.135 0.065

4.572 4.538 3.107 4.897 4.164 2.522 8.571

0.826 -0.357 -0.039 -0.801 0.012 0.311 -0.390

7.785 8.851 7.480 8.202 5.185 5.915 7.028

453.848*** 615.256*** 355.457*** 524.746*** 84.524*** 157.340*** 298.061***

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Beverage Fertilizer Food Metal Precious Raw Oil

Mean

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Note: Returns are calculated by taking log-difference to the price indices and then multiply by 100, which gives the numbers in percentage points. *** denotes 1% level of significance.

ACCEPTED MANUSCRIPT Table 2. Summarize of connectedness matrix for full sample Fertilizer

Food

Metal

Precious

Raw

Oil

From

Beverage Fertilizer Food Metal Precious Raw Oil

82.05% 1.97% 3.78% 4.08% 3.48% 1.71% 1.17%

0.05% 74.61% 1.67% 1.72% 1.10% 0.20% 1.66%

4.67% 10.85% 73.61% 7.74% 4.82% 4.74% 2.58%

4.98% 5.50% 10.77% 67.85% 8.37% 7.05% 9.96%

4.74% 3.60% 4.19% 7.97% 75.19% 3.81% 3.49%

1.84% 1.31% 4.28% 3.25% 4.07% 77.88% 4.40%

1.67% 2.15% 1.69% 7.39% 2.96% 4.61% 76.74%

17.95% 25.39% 26.39% 32.15% 24.81% 22.12% 23.26%

To NDC

16.19% -1.76%

6.40% -18.99%

35.40% 9.01%

46.64% 14.49%

27.80% 2.99%

19.16% -2.96%

20.48% -2.78%

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Note: Each element in the matrix such as X(i, j) shows how much variable j explains the variation of variable i, thus horizontal aggregation of the matrix (excluding diagonal elements) gives the value “From”; whereas the vertical summation produces the value “To”. NDC equals “To” minus “From”, thus a negative value means that variable is a net receiver from the system.

ACCEPTED MANUSCRIPT Table 3. Summary of rolling-windows estimation

From To Net

Beverage

Fertilizer

Food

Metal

Precious

Raw

Oil

Mean

22.46% 15.63% 23.73% 20.77% 1.27%

24.43% 16.60% 10.39% 2.26% -14.04%

27.54% 17.33% 41.19% 35.54% 13.65%

31.31% 18.16% 43.18% 22.62% 11.87%

28.04% 11.39% 27.53% 11.71% -0.51%

27.34% 20.29% 22.76% 16.12% -4.58%

28.96% 18.06% 21.29% 13.33% -7.67%

Std. Dev.

5.56%

17.98%

18.69%

5.39%

3.17%

6.87%

6.75%

Mean Std. Dev. Mean Std. Dev.

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Note: windows size is set to be 1/3 of total observation (142)

ACCEPTED MANUSCRIPT Table 4. Differences of connectedness before the global financial crisis and after To Pre-crisis

Post-crisis

Difference

Pre-crisis

Post-crisis

Difference

8.24% 11.60% 14.28% 26.62% 20.24% 11.13% 11.63%

49.75% 8.37% 86.38% 70.99% 39.78% 42.30% 37.50%

41.51%*** -3.24%*** 72.10%*** 44.38%*** 19.54%*** 31.16%*** 25.87%***

10.95% 12.08% 14.70% 17.97% 20.04% 12.54% 15.47%

41.78% 45.17% 49.09% 53.72% 41.47% 52.20% 51.62%

30.83%*** 33.09%*** 34.39%*** 35.76%*** 21.44%*** 39.66%*** 36.15%***

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Beverage Fertilizer Food Metal Precious Raw Oil

From

Note: sample division is around September/2008. *** denotes 1% level of significance based on the cross sample z 𝑠12

+

𝑠22 𝑛2

).

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𝑛1

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test-statistic (z = (𝜇1 − 𝜇2 )⁄√

ACCEPTED MANUSCRIPT Figure 1. Time series plot of returns Beverage

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Figure 2. Pairwise correlation heat map

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Figure 3. A nodal connectedness plot of full sample

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Figure 6. Rolling windows estimate of connectedness “To” to the system

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Figure 8. Sub-sample connectedness before and after 09/2008

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Highlights:  A dynamic approach is used to estimate the links among commodity markets.  Significant rising of connectedness has been found after the global financial crisis.  Food became the most influential commodity class is the system after the crisis.  Oil prices do not have strong impacts as normally expected/found.