Volatility Spillovers in Commodity Futures Markets: A Network Approach

Jian YangJ.P. Morgan Center for Commodities, University of Colorado Denver, USA

Zheng LiSchool of Finance, Tianjin University of Finance and Economics, Tianjin, China

Hong MiaoCollege of Business, Colorado State University, USA

August 16, 2021, 4th JPMCC International Symposium

Introduction The financialization of commodity markets has greatly expanded and heightened thedebate over increases in commodity price volatility.

The view that (unrelated to fundamentals) the financialization of commoditiesincreased the linkages (return, volatility or both, or even higher moments) amongcommodities is popular.

Some researchers (e.g., Kilian & Murphy, 2014; Kilian & Lee, 2014; Knittel &Pindyck, 2016) support the opposing view: the financialization of commodities may haveonly a limited effect; often in context of a specific commodity (oil)

Introduction Christoffersen et al. (2019): during the post financialization period (2004-2014),pairwise correlations between individual commodity futures volatility are much higherand much more integrated with significant common factors commodity futures’ returns appear much more segmented with almost no significant

common factors.

The popular view on the financialization effect immediately implies that theconcerned increases in commodity price volatility should be across the board and along-lasting phenomenon by its nature. Should be revealed by the network structure of commodity volatility spillovers.

Introduction We combines the recent advances in network analysis to investigate volatilityspillovers of 25 major commodity futures markets from 2006 to 2019. We extend only afew comprehensive studies on the topic (e.g., Diebold et al., 2017; Xiao et al., 2020): the first study to comprehensively explore commodity volatility spillovers across allmajor commodity futures with the perspective of low frequency, medium frequency, andhigh frequency; Agri. commodity volatility has different frequency components (Karali& Power, 2013). investigate potential different determinants of volatility spillovers in differentfrequency domains. propose a framework based on a LASSO-VAR to model a high-dimensionalnetwork with various frequency perspectives.

Methodology Baruník and Křehlík (2018) is an extension of Diebold and Yilmaz’s (2014) approachto measure the magnitude of spillover/connectedness effect in short-, medium- and long-term cycles.

Demirer et al. (2018) incorporate the least absolute shrinkage and selection operator(LASSO) technique to VAR models in high-dimensional environments.

This study: extending the LASSO-VAR technique to the Baruník and Křehlík (2018)approach; a high-dimensional frequency domain connectedness framework

Data January 1, 2006, to December 31, 2019. Data source: Bloomberg 25 commodity futures from three different

future exchanges: the Chicago Mercantile Exchange Group (CME), the Intercontinental Exchange (ICE), and the London Metal Exchange (LME).

The daily realized volatility of continuous nearby futures contracts.

Category Group Commodity Exchange

Agriculture

Grains

Corn CMESoybean meal CMESoybean oil CMESoybeans CME

Wheat CME

LivestockFeeder cattle CMELean hogs CMELive cattle CME

Softs

Cocoa ICECoffee ICECotton ICELumber CME

Orange juice ICESugar ICE

EnergyEnergy

Crude oil CMEGasoline CME

Heating oil CMENatural gas CME

MetalsIndustrial Metals

Aluminum LMECopper CMELead LME

Nickel LMEZinc LME

Precious MetalsGold CME

DataA set of 13 economic factors to investigate the possible determinants of thedynamic volatility connectedness

Variable Variable Description Data Source FrequencyVariables reflecting changes in the conditions of the broader economyCPI Growth of Consumer Price Index Federal Reserve Bank of St. Louis MonthlyIP Growth of Industrial Production Federal Reserve Bank of St. Louis MonthlyM2 Growth of M2 Money Supply Federal Reserve Bank of St. Louis MonthlyCFNAI Chicago Fed National Activity Index Federal Reserve Bank of St. Louis MonthlyUSD Return of Dollar Index against major currencies Federal Reserve Bank of St. Louis DailyADS Aruoba-Diebold-Scotti Business Conditions Index Federal Reserve Bank of Philadelphia DailyVariables reflecting changes in the financial market conditions

DYieldDefault yield spread (Difference between Moody’s Baa and Aaa Corporate Bond Yields)

Federal Reserve Bank of St. Louis Daily

10y3mTerm Spread (10-year government bond yield minus 3-month T-bill yield)

Federal Reserve Bank of St. Louis Daily

DReturnDefault return spread (difference between the long-term corporate and the long-term government bond returns)

BloombergDaily

TEDTED Spread (difference between the 3-month LIBOR rate and 3-Month T-bill Yield)

Federal Reserve Bank of St. Louis Daily

Variables reflecting equity and bond market volatilityMOVE1M Bank of America 1-month bond volatility index Bloomberg DailyVIX VIX Index CBOE Daily

Full Sample Results -- System-wideTABLE 3 Summary statistics of full-sample total connectedness and within connectedness

Obs.Mean (%)

S.D. (%)

Median(%)

Min. (%)

Max. (%)

Total connectedness in the time domain 3275 42.67 5.57 42.48 31.45 63.39

Total connectedness on high-frequency band 3275 24.28 2.16 24.35 17.15 43.46

Total connectedness on medium-frequency band 3275 8.00 1.34 7.91 5.42 12.90

Total connectedness on low-frequency band 3275 10.38 5.01 9.30 3.25 39.22

within connectedness on high-frequency band 3275 37.23 3.65 37.08 28.80 58.95

within connectedness on medium-frequency band 3275 45.05 5.52 45.32 32.56 62.93

within connectedness on low-frequency band 3275 59.08 9.25 59.04 35.63 85.21

Full Sample Results -- Individual CommoditiesMeans of From-index, To-index and Net connectedness at high, medium and low frequency bands

Commodity

Average of From-index Average of To-index Mean Average of Net-index MeanHigh Medium Low

Total RankHigh Medium Low

Total RankHigh Medium Low

Total RankFreq Freq Freq Freq Freq Freq Freq Freq Freq

Corn 31.55 10.87 12.77 55.19 6 36.57 11.33 14.9 62.8 6 5.02 0.46 2.14 7.62 5Soybean meal 34.63 9.98 11.01 55.62 5 35.7 10.06 12.43 58.19 7 1.08 0.08 1.42 2.57 10Soybean oil 28.94 7.48 8.64 45.06 13 24.37 7.55 8.98 40.89 14 -4.57 0.07 0.34 -4.16 16Soybeans 38.83 10.44 11.86 61.13 2 48.5 14.01 16.48 78.98 1 9.67 3.57 4.62 17.86 1

Wheat 27.39 8.34 11.23 46.96 12 24.75 8.35 11.48 44.58 12 -2.64 0.01 0.25 -2.38 12Feeder cattle 20.52 6.59 9.02 36.13 17 20.35 5.71 6.62 32.68 17 -0.17 -0.88 -2.39 -3.45 15

Lean hogs 11.48 4.49 6.99 22.96 24 8.69 3.3 4.44 16.43 23 -2.78 -1.19 -2.55 -6.52 20Live cattle 23.05 6.24 7.27 36.56 16 20.89 6.18 7.06 34.13 16 -2.17 -0.06 -0.21 -2.43 13

Cocoa 14.05 4.88 7.23 26.16 20 10 3.43 5.45 18.88 21 -4.06 -1.45 -1.78 -7.28 24Coffee 14.69 4.85 6.51 26.04 21 10.45 3.54 5.83 19.82 20 -4.24 -1.31 -0.67 -6.23 19Cotton 16.55 6.13 9.46 32.14 18 12.5 4.51 7.88 24.89 18 -4.05 -1.62 -1.58 -7.25 23

Lumber 12.23 3.95 5.89 22.06 25 7.47 2.59 3.71 13.77 25 -4.75 -1.36 -2.18 -8.29 25Orange juice 12.96 4.25 5.85 23.06 23 8.41 3.06 4.62 16.09 24 -4.55 -1.19 -1.23 -6.97 22

Sugar 14.25 5.44 8.08 27.77 19 10.85 4.04 6.33 21.22 19 -3.4 -1.4 -1.75 -6.56 21Crude oil 32.43 11.61 17.51 61.54 1 41.82 13.32 18.72 73.87 2 9.4 1.71 1.21 12.32 2Gasoline 34.6 10.2 14.12 58.93 4 36.83 11.68 16.16 64.67 4 2.22 1.48 2.04 5.74 6

Heating oil 36.85 10.44 13.06 60.35 3 39.79 12.4 16.19 68.38 3 2.93 1.97 3.13 8.04 4Natural gas 10.87 3.97 8.62 23.46 22 7.78 3.01 7.71 18.5 22 -3.09 -0.95 -0.91 -4.96 17Aluminum 22.98 8.75 11.05 42.78 15 21.02 7.54 8.71 37.28 15 -1.96 -1.21 -2.34 -5.5 18

Copper 29.17 10.25 15.48 54.9 7 34.13 13.07 16.64 63.84 5 4.96 2.82 1.17 8.94 3Lead 26.97 10.29 10.67 47.93 11 27.21 10.26 10.55 48.02 11 0.25 -0.03 -0.12 0.09 11

Nickel 23.11 10.32 11.58 45.01 14 23.4 8.96 9.33 41.7 13 0.29 -1.36 -2.25 -3.31 14Zinc 30.4 11.31 11.36 53.07 8 33.09 11.78 11.95 56.83 9 2.69 0.48 0.59 3.75 8Gold 29.16 9.09 10.84 49.09 10 29.53 9.56 12.94 52.03 10 0.37 0.47 2.1 2.94 9

Full Sample Results -- Individual Commodities the same set of commodities play the most active roles in both receiving (from-index) andsending (to-index) volatility shocks: The rank correlation between the from-index rankingand the to-index ranking is 0.993. The net connectedness ranking is almost the same.

Two commodities (soybeans, crude oil) have the largest from-index and to-index values.

if one commodity has a high from-index/to-index in volatility connectedness , it also tendsto play a similar role across all the high, medium, and low frequencies.

the traditional commodities groupings are in general quite accurate: there is clearclustering in grains, precious metals, and softs. In the livestock group, lean hogs behavesdifferently. In the energy group, natural gas is far away from other oil products.

Full Sample--Group and Category AggregationTABLE 5 Average directional connectedness among different commodity groupsPanel A: High-Frequency Band

Group Grains Livestock Softs EnergyIndustrial

metalsPrecious metals From

Grains 116.16 5.43 13.39 9.96 10.13 6.26 45.17Livestock 7.51 29.27 6.72 4.20 4.83 2.52 25.78

Softs 21.86 7.64 18.98 12.78 15.60 7.86 65.75Energy 8.40 2.52 6.94 77.62 11.67 7.60 37.14

Industrial metals 10.05 3.53 9.38 13.10 84.13 12.44 48.49Precious metals 5.90 1.54 4.28 8.56 12.49 25.77 32.76

To 53.73 20.66 40.70 48.60 54.72 36.67Panel C: Low-Frequency Band

Group Grains Livestock Softs EnergyIndustrial

metalsPrecious metals From

Grains 30.25 2.91 8.28 6.04 4.87 3.18 25.27Livestock 4.89 6.05 3.54 4.06 3.08 1.66 17.22

Softs 11.85 3.38 9.33 6.81 7.91 3.73 33.68Energy 6.56 2.15 4.56 27.45 8.00 4.59 25.86

Industrial metals 6.87 2.52 5.69 9.24 27.44 8.39 32.70Precious metals 3.85 1.11 2.42 5.20 5.89 5.82 18.48

To 34.03 12.07 24.49 31.33 29.75 21.55

Full Sample--Group and Category AggregationTABLE 5 Average Directional connectedness among different commodity groupsPanel D: Total Connectedness

Group Grains Livestock Softs EnergyIndustrial

metalsPrecious metals From

Grains 177.08 10.47 26.39 19.46 19.02 11.54 86.87Livestock 15.34 42.67 12.61 10.01 9.92 5.10 52.98

Softs 41.42 13.67 34.48 24.02 29.53 14.11 122.75Energy 18.19 5.63 13.95 127.16 24.57 14.78 77.12

Industrial metals 21.33 7.54 18.97 27.79 141.63 26.44 102.06

Precious metals 12.10 3.26 8.27 16.99 23.00 38.24 63.62To 108.38 40.57 80.18 98.27 106.04 71.97

Panel E: Total Positive Net TO Connectedness

Group Grains Livestock Softs EnergyIndustrial

metalsPrecious metals

Grains 1.27Livestock 4.87 4.37 2.38 1.84

Softs 15.03 1.06 10.08 10.56 5.85Energy

Industrial metals 2.31 3.22 3.44Precious metals 0.56 2.21

Dynamic system-wide volatility connectedness

FIGURE Dynamic total connectedness in the time and frequency domains (1-yr rolling window)

0

10

20

30

40

50

60

2007-1-3 2009-1-2 2011-1-3 2013-1-2 2015-1-2 2017-1-3 2019-1-2 2019-12-31

Time Domain High-Frequency Band Medium-Frequency Band Low-Frequency Band

Dynamic system-wide volatility connectedness

• The total connectedness ranges between 31.45% and 63.42% from the end of 2006 to theend of December 2019.

• The high commodity volatility connectedness is linked to the financial crisis and theturmoil of the US financial markets. Most of the dramatic shifts of trend in the totalcommodity volatility connectedness apparently could be attributed to some internationalevents related to crude oil.

Dynamic system-wide volatility connectedness • Generally confirm most of the full-sample average results in a time-varying dynamiccontext. During the 14-year sample, the total volatility connectedness and the low frequencyvolatility connectedness on commodity markets mostly move to the same direction and with asimilar magnitude. The correlation between the total and the low frequency: 0.89, the correlation between thetotal and the high frequency: 0.32. The fluctuation of the total volatility connectedness is mainly driven by the low frequencyvolatility connectedness at the long horizon.

Determinants of commodity volatility connectednessIndependent

VariablesHigh Frequency Medium Frequency Low Frequency

(1) (2) (3) (4) (5) (1) (2) (3) (4) (5) (1) (2) (3) (4) (5)

CPI vol.-0.206 -0.223 0.007 -0.023 0.297*** 0.357*** 0.298***

(-1.34) (-1.47) (0.14) (-0.50) (2.95) (3.48) (2.96)

IP vol.0.051 0.133 0.050 0.074* -0.334*** -0.232** -0.337***

(0.33) (1.01) (0.98) (1.70) (-3.41) (-2.16) (-3.60)

M2 vol.0.119 0.135 -0.056 -0.042 -0.217** -0.314*** -0.215**

(0.83) (0.99) (-1.17) (-0.95) (-2.45) (-3.34) (-2.47)

CFNAI-0.540 -0.605 -0.147 -0.037 -0.149 1.038** 1.473*** 1.042**

(-1.02) (-1.12) (-0.78) (-0.21) (-0.81) (2.55) (3.49) (2.56)

USD vol.0.168 -0.511 0.063 -0.235 0.659 1.100* 0.658(0.18) (-0.55) (0.19) (-0.74) (0.99) (1.84) (0.99)

ADS-0.447 0.654 0.278 -0.450 0.137 -2.585*** -2.502*** -2.586***

(-0.45) (0.56) (0.75) (-1.47) (0.35) (-3.01) (-3.06) (-3.02)

DYield-2.701 -2.669 0.063 -0.558 -0.484 -0.112 0.745 -0.130(-1.19) (-1.20) (0.08) (-0.78) (-0.64) (-0.08) (0.53) (-0.10)

10Y3M1.051 0.985 0.235 0.122 -0.012 0.079(1.13) (1.00) (0.72) (0.37) (-0.02) (0.15)

DReturn1.412* 4.008** 4.104*** 1.155** 1.531*** 1.602*** -0.749 0.182 -0.716(1.66) (2.47) (2.61) (2.33) (2.92) (3.13) (-0.73) (0.18) (-0.67)

TED-2.065 -1.645 -1.044** -1.276** 1.594 2.836*** 1.541(-1.31) (-0.88) (-2.11) (-2.17) (1.59) (2.59) (1.48)

MOVE1M0.020 0.009 0.002 0.004 0.008 -0.005 0.001 -0.006(0.72) (0.32) (0.25) (0.42) (0.87) (-0.29) (0.05) (-0.30)

VIX0.053 0.004 -0.029 0.032 -0.009 -0.010 0.087 -0.008(0.68) (0.05) (-0.99) (1.24) (-0.33) (-0.19) (1.51) (-0.16)

VRP0.017** 0.010 0.001 -0.004 -0.026*** -0.024*** -0.026***

(2.01) (0.80) (0.34) (-1.00) (-3.21) (-2.94) (-3.22)Obs. 3900 3900 3900 3900 3900 3900 3900 3900 3900 3900 3900 3900 3900 3900 3900

R2 0 140 0 140 0 142 0 140 0 143 0 236 0 233 0 237 0 233 0 238 0 264 0 259 0 256 0 254 0 264

Summary and Implications

Commodity as a broad asset class from the volatility spillover perspective:Commodity specialists on a particular commodity should also watch for volatility or marketsituations in other commodity markets. They should pay more attention to other commoditiesin the same group in the short term, but also to other commodities in other groups(particularly soybeans and crude oil) in the longer term. The financialization of commodities might have limited effects on explaining thefluctuation of commodity price volatility and spillovers, particularly for the long termcomponent: Commodity volatility connectedness across groups is primarily driven by theirlinkages at the low frequency and significantly driven by broad economic conditions.