The impact of geopolitical uncertainty on energy volatility

Liu, Yang; Han, Liyan; Xu, Yang

doi:10.1016/j.irfa.2021.101743

Cited by 77 publications

(22 citation statements)

References 73 publications

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“…us, it is reasonable that GPR Treat has more profound influences on the long-term volatility of China's stock market. is pattern can also be observed by the impact of GPR Act has on various financial assets such as stock returns [4] and WTI volatility [59].…”

Section: E Heterogeneous Effects Of Different Categorical Gprmentioning

confidence: 83%

“…Saudi Arabia has the world's largest oil reserves and production and is one of China's major energy importers. Previous literature shows that GPR can have a positive impact on crude oil prices [59]. As mentioned in [67], China's dependence on imported oil keeps increasing since 2012, and in 2013, China was announced by the US Energy Information Administration (EIA) that it had become the largest net importer of crude oil among the world's economies.…”

Section: E Heterogeneous Effects Of Gpr Indices From Differentmentioning

confidence: 99%

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Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model

Yang

Zhang

et al. 2021

Discrete Dynamics in Nature and Society

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Previous studies have found that geopolitical risk (GPR) caused by geopolitical events such as terrorist attacks can affect the movements of asset prices. However, the studies on whether and how these influences can explain and predict the volatility of stock returns in emerging markets are scant and emerging. By using the data from China’s CSI 300 index, we provide some evidence on whether and how the GPR factors can explain and forecast the volatility of stock returns in emerging economies. We employed the GARCH-MIDAS model and the model confidence set (MCS) to investigate the mechanism of GPR’s impact on the China stock market, and we considered the GPR index, geopolitical action index, geopolitical threat index, and different country-specific GPR indices. The empirical results suggest that except for a few emerging economies such as Mexico, Argentina, Russia, India, South Africa, Thailand, Israel, and Ukraine, the global and most of the regional GPR have a significant impact on China’s stock market. This paper provides some evidence for the different effects of GPR from different countries on China’s stock market volatility. As for predictive potential, GPRAct (geopolitical action index) has the best predictive power among all six types of GPR indices. Considering that GPR is usually unanticipated, these findings shed light on the role of the GPR factors in explaining and forecasting the volatility of China’s market returns.

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Section: E Heterogeneous Effects Of Different Categorical Gprmentioning

confidence: 83%

Section: E Heterogeneous Effects Of Gpr Indices From Differentmentioning

confidence: 99%

Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model

Yang

Zhang

et al. 2021

Discrete Dynamics in Nature and Society

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“…Scenario 2 accounts for a 25% increase in gas and electricity prices, and Scenario 3 represents a 30% increase in electricity prices, and a sudden increase of 65% in gas prices related to a 40% increase in imported gas and a 50% fall in stored gas. This third scenario is used to capture the current energy crisis in Europe (Ambrose, 2021;Liu, Han and Xu, 2021).…”

Section: 4mentioning

confidence: 99%

“…Despite undeniable reductions in carbon emissions associated with green technologies, there are a number of risks that need to be reviewed by councils, who are responsible for the day-to-day oversight over energy generation, energy consumption, emissions capture, policy and engagement. Factors that influence these risks include the employment of green technologies that are less mature than the conventional ones (Newbery, 2016), as well as short-term volatility of energy prices (Liu, Han and Xu, 2021). Here, we present a decision support tool that can inform a local council about immediate effects of changes in policy and energy markets on energy infrastructure as well as producing cost projections crucial for short-term planning.…”

mentioning

confidence: 99%

A Bayesian Decision Support System in Energy Systems Planning

Volodina¹,

Sonenberg²,

Challenor³

et al. 2022

Preprint

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Gaussian Process (GP) emulators are widely used to approximate complex computer model behaviour across the input space. Motivated by the problem of coupling computer models, recently progress has been made in the theory of the analysis of networks of connected GP emulators. In this paper, we combine these recent methodological advances with classical state-space models to construct a Bayesian decision support system. This approach gives a coherent probability model that produces predictions with the measure of uncertainty in terms of two first moments and enables the propagation of uncertainty from individual decision components.This methodology is used to produce a decision support tool for a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. In particular, we demonstrate how to couple information from an energy model, a heating demand model, and gas and electricity price time-series to quantitatively assess the impact on operational costs of various policy choices and changes in the energy market.

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“…In the past ten years, the global bulk commodity market has shown the following three trends: Major agricultural products fluctuated sharply at a relatively high level, especially undergoing a rapid rise from 2010 to 2011. Energy is showing a trend of upward volatility, and GPR seems to play an indispensable role in it, even being more pushing than other macro indicators [64]. However, after the gradual fading of the 9/11 incident and the breakthrough of the bottleneck in 2006, the metal just ushered in a rise.…”

Section: Risk Spillovermentioning

confidence: 99%

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

Ding¹,

Zhang²

2021

Complexity

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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.

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The impact of geopolitical uncertainty on energy volatility

Cited by 77 publications

References 73 publications

Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model

Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model

A Bayesian Decision Support System in Energy Systems Planning

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

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