Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model
By Qiang Ji, Elie Bouri, David Roubaud, Syed Jawad Hussain Shahzad. Energy Economics, 2018
263 citations recorded by the source corpus.
Unlike previous studies, we employ a relatively newer modelling technique — a time-varying copula with a switching dependence — to characterise the conditional dependence between energy and agricultural commodity markets in a more realistic way. Because the dependence may switch between positive and negative correlation regimes over time, a dependence-switching copula more appropriately and realistically captures a dependence structure than a single copula regime. Our findings indicate that the lower tail dependence is much stronger in a bearish regime than in a bullish regime, highlighting the importance of systematic risk spillovers during extreme downward movements. Furthermore, the significant risk spillovers from energy to agricultural commodities are verified by measuring the conditional value-at-risk (CoVaR) and delta CoVaR. Finally, some useful implications are summarized for investors' portfolios and risk avoidance.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/72c61e3ec973b6fc531d8b587c8b27e0cffb6015
How researchers see this paper
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In Economics and Econometrics
- Rangan Gupta
- Elie Bouri
- David Gabauer
- Oğuzhan Çepni
- David Roubaud
- Afees A. Salisu
- Andrew Urquhart
- O. Adekoya
In Finance
Citations in the corpus
- Return connectedness across asset classes around the COVID-19 outbreak (2020, background): “, 2012; Zhang and Broadstock, 2018), copulas (Philippas and Siriopoulos, 2013; Ji et al., 2018), or conditional value-atrisk (Ji et al.”
- Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models (2019, unknown): “…literature, forecasting volatility has gained interest, oftentimes from a risk-2 management perspective (Klein & Walther, 2016, Ji & Fan, 2016) or in terms of quantifying spillovers between markets (Nazlioglu et al., 2013, Todorova et al., 2014, Ji et al., 2018, Klein, 2018, Ma et al., 2019).”