Dynamic volatility spillover effects between oil and agricultural products

Yip, Pick Schen; Brooks, Robert Darren; Xuan, Hung; Nguyen, Duc Khuong

doi:10.1016/j.irfa.2020.101465

Cited by 78 publications

(31 citation statements)

References 42 publications

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“…There is a vast literature on the pricing, connectedness, and volatility spillovers on commodity markets. The majority of the studies that include agricultural and livestock commodities, focuses on examining the linkages between returns or volatilities of these commodities, and return or volatility of crude oil or energy prices (e.g., Kaltalioglu and Soyas, 2011;Serra, 2011;Du and McPhail, 2012;Reboredo, 2012;Nazlioglu et al, 2013;Koirala et al, 2015;Cabrera and Schulz, 2016;Kang et al, 2017;Zhang and Broadstock, 2018;Yahya et al, 2019;Dahl et al, 2019;Tiwari et al, 2020;Yip et al, 2020). The evidence found is mixed, and several studies document significant relationship between oil and agricultural commodities while so many others have shown the opposite.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era

2021

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This paper explores the dynamic return and volatility connectedness for the three most relevant agricultural commodity markets (Soft, Grain and Livestock) and the Coronavirus Media Coverage Index (MCI) extracted from RavenPack. In concrete, we apply the fresh TVP-VAR methodology proposed by Antonakakis and Gabauer (2017) during the sample period between January 22, 2020 and July 31, 2020, that is in the context of the COVID-19 pandemic crisis. Interesting results are found in this research. First, dynamic total return and volatility connectedness fluctuates over time, reaching a peak during the heart of the global pandemic crisis, initially in the dynamic total return connectedness and later in the volatility one. Second, in the dynamic connectedness TO the system, we observe significant differences between the agricultural commodity markets in the level of the return connectedness measure. However, in the dynamic volatility connectedness TO, there are very few differences between some elements of the system, highlighting the Coronavirus MCI as one of the major transmitters TO the system at some points in the sample. This Coronavirus MCI appears as the less relevant receiver FROM the system, not only in terms of dynamic return connectedness, but also in volatility. Finally, regarding the net dynamic total connectedness, the Coronavirus MCI shows the highest values in return and volatility. Next, in order of highest to lowest net dynamic return and volatility connectedness we find the Grain commodity market, then the Soft market and finally the Livestock market. This last commodity market shows a negative net dynamic connectedness throughout the entire sample period analysed, exhibiting negative peaks in return at the beginning of the pandemic's epicentre and in volatility in the middle of the COVID-19 crisis.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era

2021

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“…They reported a significant volatility spillover from the US to ASEAN equity markets. The paper of Yip et al (2020) researched dynamic implied volatility spillover effect from oil to agricultural markets in the period after the global financial crisis 2008-2009. They used the generalized spillover indices within a fractionally integrated VAR model to capture the dynamic patterns of the volatility spillover effects alongside the Markov Switching Autoregressive model to extract the regimes of oil.…”

Section: Literature Review and Related Studiesmentioning

confidence: 99%

Nonlinear bidirectional multiscale volatility transmission effect between stocks and exchange rate markets in the selected African countries

Živkov

Kuzman

Panić

2020

Economic Research-Ekonomska Istraživanja

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“…Recent empirical studies confirmed the evidence that Islamic stock indices are driven by futures oil prices, especially emerging stock market (Mongi, 2019). Yip et al (2020) support the same finding of the volatility spillover from oil to agriculture products. Insofar as Islamic finance recognizes the attachment to real economic sectors such as commodities, industrials, basic and raw materials.…”

Section: Methodology and Datamentioning

confidence: 52%

Modeling the volatility of DJIM equity indices: a fundamental analysis using quantile regression

Arfaoui

Rejeb

2020

IMEFM

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Purpose This paper aims to investigate the behavior of volatility of Islamic equity indices toward fundamental risk factors. It focuses on the degree and structure of sensitivity to commodity price changes, global risk perception and term premium and whether crises and fragility periods have shaped the degree and structure of this sensitivity. Design/methodology/approach Quantile regression incorporating structural changes and GARCH-class model are used to establish how sensitivities are varying across volatility distribution depending on global events. The data are daily series of return indices, over the period spanning from January 1, 2001 until January 22, 2018. Findings The results show significant sensitivity to fundamental factors. The sensitivity is identified for different regional indices and intensified across quantiles. Speculation has shaped the structure of sensitivity at normal time, but correction holds at time of crisis. The results reveal that even if they share common features, commodities cannot be considered as homogeneous asset class. Indeed, the exact relationship cannot be observed at normal time in presence of speculation and information delay. However, at time of financial fragility and periods of crisis, the sensitivity is assigned with the plausible sign. Practical implications The obtained results present several policy implications as well for academics, portfolio managers and policy-makers. It opens new research paths for academic research, it helps in investment decisions, provides lessons for portfolio diversification, both for price discovery and hedging. The results serve as well to implement effective macroeconomic stabilization policies and even fiscal policies to counteract any inflationary impact of fundamental price changes on investors and Islamic banks. Originality/value This paper contributes to empirical literature by dealing with the sensitivity of Islamic equity indices to commodity prices and term premium along with the effect of investor sentiment. It pays attention to the financial stability of Islamic stock markets by investigating the sensitivity at normal time, during fragility periods and periods of crisis. It considers the financialization process of commodity markets and includes the term premium to control for rational expectations on term structure of interest rates and the VIX (Volatility index) as global risk perception to control for safety and risk aversion.

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Dynamic volatility spillover effects between oil and agricultural products

Cited by 78 publications

References 42 publications

Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era

Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era

Nonlinear bidirectional multiscale volatility transmission effect between stocks and exchange rate markets in the selected African countries

Modeling the volatility of DJIM equity indices: a fundamental analysis using quantile regression

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