Cross market predictions for commodity prices

Ding, Shusheng; Zhang, Yongmin

doi:10.1016/j.econmod.2020.06.019

Cited by 31 publications

(17 citation statements)

References 34 publications

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“…Due to its high complexity, cross-border effects, random relationships, and nonlinear causal models with critical points, the negative impact of systemic risk has attracted more attention from scholars [29]. Asymmetric changes mean that there are different channels of infection, but considering the advantages of macroeconomic variables that can adjust commodity price fluctuations and enhance its stability [30][31][32], research from a macro perspective has been broadened [33,34]. From simple regression and DCC-GARCH model to even copula function, scholars measured the contagion effect of systemic risk in commodity market covered by macro factors [35][36][37].…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…Hence our study is motivated to understand the transmission channel of commodity prices on the South African economy by assessing the effects of foreign exchange on industrial commodity prices. We first conduct a cointegration test which has been widely used in the literature (see Ding & Zhang, 2020) to examine the long linear relationship among the series. Secondly, we test the presence of an asymmetric relation in the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Does Exchange Rate Impact Commodity Prices: Evidence From a Small Open Economy 

Guei¹,

Choga²

2021

Preprint

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The paper assesses the dynamics of exchange rate and the terms of trade on industrial commodity prices. We investigate the linear and asymmetric effects of exchange rate on commodity prices using an error correction model (ECM) and a threshold autoregressive (TAR) model that allows to estimate the dynamics of different regimes. Further we employ a structural vector autoregressive (SVAR) model to examine the impact of an external shock on the terms of trade using quarterly data over the period 1992q1 to 2019q4.The results suggest that an exchange rate above or equal 4.6 has a positive and significant impact on commodity prices. Specifically, if real exchange rate is above the threshold 4.6, the price of gold, copper, and nickel increases by 0.9 %, 1.1%, and 2.7%, respectively. We also find that a 1% increase in real interest rate is associated with a 0.003% fall in the terms of trade. JEL classification: E31, E43, F3

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“…On the supply side, this persistence is induced by correlated shocks, while on the demand side persistence is generated from working stocks that induce intertemporal correlations. However, price jumps may be instigated by speculative demand by producers' anticipation of stock-outs (Ding & Zhang, 2020;Hau et al2020;Fowowe 2016;Helmberger and Weaver 1982). The depreciation of the dollar value and speculations in future markets are further factors that influence agricultural commodity price movement (Robles et al, 2009, andTrostle, 2008).…”

Section: Fig 1: Crude Oil Price Transmission Effectmentioning

confidence: 99%

Dynamics of Crude Oil Price Change and Global Food Commodity Prices

Onour

2021

FER

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Purpose: This study investigates the effect of crude oil price fluctuations (price change as well as volatility) on wheat, sugar, corn, and fertilizers price changes. Methods: The study employs Markov switching dynamic regression, Dynamic Conditional Correlation (DCC), and Generalized Autoregressive Conditional Hetrosekadicity (GARCH) on monthly data covering the period from January 1988 to April 2018. Results: The findings of the research support evidence of two states. State 1, pertains to the low volatility of crude oil price, and state 2 belong to the case of the high volatility of crude oil prices. Our results indicated that at state 1, an increase in crude oil prices leads to a decline in food commodity prices, while in state 2, an increase in crude oil price levels causes an increase in food commodity prices. Results of Dynamic Conditional Correlation (DCC) GARCH estimates indicate the coefficients of oil price levels are significant and positively associated with the conditional volatility of the four commodity prices. Implications: The findings of the research imply that volatility in global food commodity prices is not due to oil price volatility but due to the oil price levels attained at extreme points. Originality: The paper investigates the impact of different volatility levels of crude oil prices on global food commodity prices.

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Cross market predictions for commodity prices

Cited by 31 publications

References 34 publications

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

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

Does Exchange Rate Impact Commodity Prices: Evidence From a Small Open Economy

Dynamics of Crude Oil Price Change and Global Food Commodity Prices

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Cross market predictions for commodity prices

Cited by 31 publications

References 34 publications

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

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

Does Exchange Rate Impact Commodity Prices: Evidence From a Small Open Economy&nbsp;

Dynamics of Crude Oil Price Change and Global Food Commodity Prices

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Does Exchange Rate Impact Commodity Prices: Evidence From a Small Open Economy