Dynamic connectedness in commodity futures markets during Covid-19 in India: New evidence from a TVP-VAR extended joint connectedness approach

被引:21
作者
Mishra, Aswini Kumar [1 ]
Arunachalam, Vairam [2 ]
Olson, Dennis [3 ]
Patnaik, Debasis [1 ]
机构
[1] BITS Pilani KK Birla Goa Campus, Dept Econ & Finance, Sancoale, India
[2] Univ Missouri, Sch Accountancy, Columbia, MO USA
[3] Oregon State Univ Cascades, Dept Business Adm, 1500 SW Chandler Ave, Bend, OR 97702 USA
关键词
Commodity futures market; COVID-19; pandemic; TVP-VAR; Dynamic connectedness; Joint connectedness; CRUDE-OIL; VOLATILITY TRANSMISSION; DEPENDENCE STRUCTURE; SYSTEMIC RISK; ENERGY; GOLD; FINANCIALIZATION; UNCERTAINTY; SPILLOVERS; ECONOMICS;
D O I
10.1016/j.resourpol.2023.103490
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents a unique time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach to quantify the connectedness and transmission mechanism of shocks of nine commodities futures returns (namely; Gold and Silver from the category of precious metals; Copper, Lead, Zinc, Nickel and Aluminium from the category of base or industry metals; Natural Gas and Brent Crude Oil from energy sector) obtained from Multi Commodity Exchange of India Limited (MCX) from January 1, 2018 to December 31, 2021. This paper employs Balcilar et al. (2021)'s TVP-VAR extended joint connectedness approach, which combines the TVP-VAR connectedness approach of Antonakakis et al. (2020) with the joint spillover approach of Lastrapes and Wiesen (2021), to investigate the dynamic connectedness among the select commodity futures of interest. Our findings show that system-wide dynamic connectedness varies over time and is driven by economic events. The pandemic shocks appear to have an impact on system-wide dynamic connectedness, which peaks during the COVID-19 pandemic. Crude oil and zinc are the primary net shock transmitters, whereas gold and silver are the primary net shock receivers. We also discovered that the role of aluminum in shock transmitters and shock receivers changed during the course of the investigation. Pairwise connectivity, on the other hand, shows that Zinc, Copper, Nickel, and Crude oil are the key drivers of gold price changes, explaining the network's high degree of interconnectivity. During the study period, it was also discovered that silver has a significant influence on gold. Furthermore, in comparison to natural gas, gold's spillover activity is still relatively modest (on a scale), indicating that gold is less sensitive to market innovations.
引用
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页数:12
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共 66 条
[21]   How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study [J].
Caloia, Francesco Giuseppe ;
Cipollini, Andrea ;
Muzzioli, Silvia .
ENERGY ECONOMICS, 2019, 84
[22]  
Cembalest M., 2020, EYE MARKET
[23]   Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment [J].
Choi, Kyongwook ;
Hammoudeh, Shawkat .
ENERGY POLICY, 2010, 38 (08) :4388-4399
[24]   On the links between stock and commodity markets' volatility [J].
Creti, Anna ;
Joets, Marc ;
Mignon, Valerie .
ENERGY ECONOMICS, 2013, 37 :16-28
[25]  
DAGOSTIN.RB, 1970, BIOMETRIKA, V57, P679, DOI 10.1093/biomet/57.3.679
[26]   Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture [J].
Dahl, Roy Endre ;
Oglend, Atle ;
Yahya, Muhammad .
JOURNAL OF COMMODITY MARKETS, 2020, 20
[27]   Should investors include commodities in their portfolios after all? New evidence [J].
Daskalaki, Charoula ;
Skiadopoulos, George .
JOURNAL OF BANKING & FINANCE, 2011, 35 (10) :2606-2626
[28]   Commodity and equity markets: Some stylized facts from a copula approach [J].
Delatte, Anne-Laure ;
Lopez, Claude .
JOURNAL OF BANKING & FINANCE, 2013, 37 (12) :5346-5356
[29]   On the network topology of variance decompositions: Measuring the connectedness of financial firms [J].
Diebold, Francis X. ;
Yilmaz, Kamil .
JOURNAL OF ECONOMETRICS, 2014, 182 (01) :119-134
[30]   Better to give than to receive: Predictive directional measurement of volatility spillovers [J].
Diebold, Francis X. ;
Yilmaz, Kamil .
INTERNATIONAL JOURNAL OF FORECASTING, 2012, 28 (01) :57-66