Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach

Sun, Xiaolei; Liu, Chang; Wang, Jun; Li, Jianping

doi:10.1016/j.irfa.2020.101453

Cited by 68 publications

(33 citation statements)

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“…To this end, we adopt a bivariate GARCH( p , q ) model, which is well recognized for its potential to handle the stylized facts observed in our series, including heavy tails, volatility clustering, and nonlinear dependence. There is a plethora of research that relies on variants of GARCH models to explain the stochastic behavior of time series, and to explore the price and volatility information spillovers between assets (e.g., Ahmed and Huo, 2021 ; Andersen and Bollerslev, 1997 ; Hamao et al, 1990 ; Hou et al, 2019 ; Izquierdo and Lafuente, 2004 ; Symitsi and Chalvatzis, 2018 ; Sun et al, 2020 ; Yu et al, 2019 ). To determine the appropriate number of autoregressive lags (i.e., ARCH terms) and moving average lags (i.e., GARCH terms), various models with combinations of p = 1, 2, and 3 and q = 1, 2, and 3 are examined.…”

Section: Econometric Frameworkmentioning

confidence: 99%

Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic

Elgammal

Ahmed

Alshami³

2021

Resources Policy

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This study sets out to provide fresh evidence on the dynamic interrelationships, at both return and volatility levels, between global equity, gold, and energy markets prior to and during the outbreak of the novel coronavirus. We undertake our analysis within a bivariate GARCH( p , q ) framework, after orthogonalizing raw returns with respect to a rich set of relevant universal factors. Under the COVID-19 regime, we find bidirectional return spillover effects between equity and gold markets, and unidirectional mean spillovers from energy markets to the equity and gold counterparts. The results also suggest the presence of large reciprocal shock spillovers between equity and both of energy and gold markets, and cross-shock spillovers from energy to gold markets. Most probably driven by the recent oil price collapse, energy markets appear to have a substantial cross-volatility spillover impact on the others. Our results offer implications for policymakers and investors.

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Section: Econometric Frameworkmentioning

confidence: 99%

Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic

Elgammal

Ahmed

Alshami³

2021

Resources Policy

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“…The Copula approach can connect marginal distributions using different Copula functions to study the risk spillovers among financial markets (He and Gong, 2009;Liu et al, 2011;Ghorbel and Trabelsi, 2014;Li and Yan, 2015;Gong et al, 2018;Usman et al, 2019;Sun et al, 2020;Yang et al, 2020). Among them, Ghorbel and Trabelsi (2014) used the Copula model to study the risk spillover between the stock market and oil prices in the USA and found that the stock market had a more significant impact on oil price volatility.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The GARCH-Copula-CoVaR model can be considered a combination of the GARCH model and the Copula method. Using this model, Li and Yan (2015) studied the changes in spillover risk between the Shanghai Stock Exchange and Hong Kong Stock Exchange before and after the implementation of the Shanghai-Hong Kong Stock Connect, and Sun et al (2020) examined the extreme risk of international commodities on maritime markets. Moreover, Gong et al (2018) used the Copula model to study the correlation between liquidity risk and the Shanghai and Shenzhen 300 index futures.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Introduction 1.1 Background With widespread globalization of the world economy, the co-movement of financial markets has intensified and exhibited the characteristic of high correlations. This co-movement manifests the fact that one market can transfer risk to other markets and exert an adverse influence on them (Diamandis, 2009;Aloui et al, 2011;Ayusuk and Sriboonchitta, 2014;Luo et al, 2015;Yavas and Rezayat, 2016;Aziz and Ansari, 2017;Fan et al, 2020;Sun et al, 2020). Risk spillovers can aggravate financial instability, lead to potentially large financial losses and destroy the balance of the world economy.…”

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confidence: 99%

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Examining the evidence of risk spillovers between Shanghai and London non-ferrous futures markets: a dynamic Copula-CoVaR approach

Shen

Tang

Xing

et al. 2020

IJOEM

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PurposeThis paper aims to examine the evidence of risk spillovers between Shanghai and London non-ferrous futures markets using a dynamic Copula-CoVaR approach.Design/methodology/approachWith daily data, the marginal distributions and optimal Copula functions are determined using the kernel estimation method and squared Euclidean distance test. The conditional value-at-risk and the conditional value-at-risk spillover rate are computed from the Copula estimated parameters based on the Copula-CoVaR model. Also, the dynamic correlation coefficient between the two futures markets is investigated.FindingsThe empirical results are as follows: overall, the risk spillover effect exerted by the London Metal Exchange on the Shanghai Futures Exchange is more significant than vice versa. Moreover, the degree of risk spillovers exerted by the London Metal Exchange on the Shanghai Futures Exchange for zinc and copper are more significant when they are depressed in the London Metal Exchange. Moreover, the dynamic of the correlation between the Shanghai and London futures markets is attributed to be largely due to changes in the global economy.Research limitations/implicationsThe Copula-CoVaR model used in this paper is suitable for measuring the risk spillovers between two different markets, while the risk spillovers across multiple markets or the consideration of multiple risk factors cannot be accurately captured using this framework. Multiple state variables to capture time variation in the conditional moments of return series will be a topic in future research.Practical implicationsThe results provide theoretical support for risk management and monitoring of the non-ferrous futures markets.Originality/valueThe ability of the Copula function to accurately describe a nonlinear relationship and tail correlation is harnessed to measure the risk spillovers, explore the degree and direction of risk spillovers and identify the source of risk spillovers. The global economy is incorporated as a macro factor to explore its inner connection with the dynamic of risk spillovers in the non-ferrous metal futures market.

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“…Bai and Lam [20] investigated dynamic interdependence between liquefied petroleum gas (LPG) freight rate and oil price employing a conditional copula-GARCH approach and found that the price linkage between crude oil and Baltic LPG freight rate is relatively weak and mostly positive. Sun et al [21] addressed the extreme risk transmission from the commodity market to the maritime market employing a GARCHcopula-CoVaR approach. They found evidence regarding risk spillovers from oil to the maritime markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach

Choi¹,

Yoon²

2020

Preprint

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Changes in crude oil price affect the shipping freight market in three different channels. This study explores the dependence structure between oil prices and maritime freight rates to identify the strongest channel. Therefore, it investigates the relationship between oil prices and three major maritime freight rates; the Baltic Dry Index (BDI), the Baltic Dirty Tanker Index (BDTI), and the Baltic Clean Tanker Index (BCTI). We employ the decomposition method, not studied in the existing literature. The copula approach identifies the time-varying effects and asymmetry in the tail dependence structure between oil prices and freight rates. The main results of this analysis are as follows. The decomposed components display different conditional dependence patterns, and asymmetry is revealed in the upper and lower tail dependence. In the long run, we find more dependence in extreme periods like the financial crises. In short-run fluctuations, we find the dependence increases in an economic boom. The implications of the results suggest that dependence can vary over time and may change depending on extreme events, implying that the complementary strategies of the long run and short run should be different.

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Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach

Cited by 68 publications

References 68 publications

Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic

Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic

Examining the evidence of risk spillovers between Shanghai and London non-ferrous futures markets: a dynamic Copula-CoVaR approach

Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach

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