The Impact of Geopolitical Risk on Systemic Risk Spillover in Commodity Market: An EMD‐Based Network Topology Approach

Abstract

Since the financialization of commodities, portfolio investments have become an important tool for investors to diversify risks. However, due to the nonlinear fluctuations brought about by extreme events, investors face more difficulties in the choice of risk portfolio. We adopt empirical mode decomposition and STVAR model, along with the basis data of optimized original sample interval. In addition, we retain the mature research of multiscale systemic risk under frequency and divide the dimension of systemic risk into two states. When frequency is combined with states, the risk spillover center undergoes subversive changes, particularly in the longest term, and metals become the risk spillover center, substituting the energy commodity, on the condition that the compositions of extreme value add persuasive power to the perspective of long term. We proposed that the joint fluctuation of agricultural commodities and energy commodities makes the former become another important risk spillover point. For investors, holding period and portfolio both need to be considered.

Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic

“…We can observe that commodities are highly volatile during the COVID-19 period, with an annualized mean returns ranging from −2.633% for heating oil to 62.987% for crude oil, while in normal times, the annualized returns range is much narrower from −5.629% for gasoline to −2.153% for gold. The summary statistics of the return series in Table 1 indicate that crude oil has the highest standard deviation of 83.915%, followed by gasoline of 71.715%, with these two being the riskiest assets in normal times as well, as also highlighted in the past literature (see, Rehman et al, 2019 ; Ding and Zhang, 2021 ). This is not surprising given the deplorable impact of the COVID-19 pandemic on energy prices ( Dutta et al, 2020 ; Bouri et al, 2021a ).…”

“…Interestingly, gold has been replaced by soybeans as the second safest asset, a result consistent with the GAS-2F estimations, and mildly agreeing with Ji et al (2020) on the role of soybeans as safe haven asset in times of the COVID-19 pandemic. In similar manner, Ding and Zhang (2021) and Luo et al (2022) claim that agricultural products, as daily necessities, are stable and remain less volatile compared to other commodity futures during the COVID-19 pandemic. Unsurprisingly, the riskiest assets for investment, as found by all three models in Table 6 , are energy commodities and, in particular, crude oil and gasoline.…”

“… Iqbal et al (2022) also discover that agricultural futures can offer some hedge against the extreme left tails of traditional equity assets during COVID-19, whereas energy commodities exhibit an exactly opposite behavior in those scenarios. Ding and Zhang (2021) find that agricultural commodities have remained stable and less volatile during the early stages of COVID-19 pandemic. Bouri et al (2021b) find strong and moderate levels of volatility connectedness among energy and metals, and moderate connectedness levels within the group of agricultural commodities, precisely, gold followed by soybeans have the lowest standard deviation.…”

“…Traditionally, precious metals, such as gold, and its larger family, commodities, are considered to maintain or appreciate in value during times of market turbulence ( Bouri et al, 2020 ). The potential for gold to act as a safe haven asset is broadly recognised in the past literature (see, for example, Baur and Lucey, 2010 ; Baur and McDermott, 2010 , 2016 ; Ciner et al, 2013 ; Hood and Malik, 2013 ; Reboredo, 2013 ; Areal et al, 2015 ; Beckmann et al, 2015 ; Bredin et al, 2015 ; O'Connor et al, 2015 ; He et al, 2018 ; Rehman et al, 2019 ; Shahzad et al, 2019a , b ; Bouri et al, 2020 ; Ming et al, 2020 ; Ding and Zhang, 2021 ; Kumar and Padakandla, 2022 ; Naeem et al, 2022 ). However, the safe haven property of gold is found to vary across different stock markets.…”

Application of Multifractal Analysis in Estimating the Reaction of Energy Markets to Geopolitical Acts and Threats

“…GPR's connection with other commodity markets has been studied via numerous analytical methods. These include the fixed-effect regression model [40], random-effect regression model [41], quantile regression [42,43], linear and non-linear probabilistic models and feasible generalized least-square estimator [44,45], time-frequency-based wavelet analysis [46], decomposition and the STVAR model [47] or Bayesian graphical structural VAR [48], among others. However, multifractal aspects of GPR with energies as well as other financial markets have been largely ignored in these studies.…”