Trading behaviour connectedness across commodity markets: Evidence from the hedgers’ sentiment perspective

Ji, Qiang; Bahloul, Walid; Geng, Jiang-Bo; Gupta, Rangan

doi:10.1016/j.ribaf.2019.101114

Cited by 40 publications

(15 citation statements)

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“…On the other hand, global market integration and the financialization of commodity markets have led to increased price volatilities and speculation, which serves as the channel for the transmission of risk and return spillovers across different commodity classes. Since the interconnections among different commodities hold significant implications related to business cycle analysis, asset allocation, and risk management, therefore a large body of literature has documented the causal relationships between different commodity markets (e.g., Rehman et al, 2018 ; Zhang and Broadstock, 2018 ; Kang et al, 2019 ; Ji et al, 2020 ; Mandacı et al, 2020 ; Tiwari et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 on the quantile connectedness between energy, metals and agriculture commodities

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 on the quantile connectedness between energy, metals and agriculture commodities

et al. 2022

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“…Thus six factors are selected from macroeconomics and capital markets to a broader geopolitical dimension which can capture the different responses of commodity volatility to the changes of these factors. According to existing literature, six macro factors are chosen including economic policy uncertainty (EPU; Balcilar et al, 2016 ; Bilgin et al, 2018 ), the economic surprise index (ESI; Maveé et al, 2016 ), default spread (DEF; Bhardwaj et al, 2015 ; Ordu et al, 2018 ), the investor sentiment index (SI; Bahloul, 2018 ; Ji et al, 2020b ), the volatility index (VIX; Silvennoinen and Thorp, 2013 ; Bilgin et al, 2018 ), and the geopolitical risk index (GPR; Antonakakis et al, 2017 ; Plakandaras et al, 2019 ). They are from three dimensions, namely, macroeconomics, capital market, and geopolitical risk.…”

Section: Data and Empirical Resultsmentioning

confidence: 99%

Macro factors and the realized volatility of commodities: A dynamic network analysis

Zhang

et al. 2020

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This paper explores the relationship between macro-factors and the realized volatility of commodity futures. Three main commodities—soybeans, gold and crude oil—are investigated using high-frequency data. For macro factors, we select six indicators including economic policy uncertainty (EPU), the economic surprise index (ESI), default spread (DEF), the investor sentiment index (SI), the volatility index (VIX), and the geopolitical risk index (GPR). These indicators represent three dimensions from macroeconomics and capital markets to a broader geopolitical dimension. Through establishing a dynamic connectedness network, we show how these macro factors contribute to the volatility fluctuations in commodity markets. The results demonstrate clearly distinctive features in the reaction to macro shocks across different commodities. Crude oil and gold, for example, are more reactive to market sentiment, whereas DEF contributes the most to the realized volatility of soybeans. Macroeconomic factors and geopolitical risks are more relevant to crude oil volatilities compare to the other two. Our empirical results also reveal the fact that the macro influence on the realized volatility of commodities is time varying.

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“…Mensi et al [23] employed the bivariate APARCH model to capture volatility spillover effects between the U.S. and BRICS stock markets. Except for the GARCH models, some other conventional econometric methods are used for volatility spillover effects studies, such as the ARMA model [32], Markov regime-switching model [33,34], and VAR framework [35][36][37][38]. However, a large number of parameters have to be estimated in these models when it comes to more than two or three financial assets.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

Chai

Zhang

et al. 2020

Complexity

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Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

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Trading behaviour connectedness across commodity markets: Evidence from the hedgers’ sentiment perspective

Cited by 40 publications

References 50 publications

Impact of COVID-19 on the quantile connectedness between energy, metals and agriculture commodities

Impact of COVID-19 on the quantile connectedness between energy, metals and agriculture commodities

Macro factors and the realized volatility of commodities: A dynamic network analysis

Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

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