Dependence risk analysis in energy, agricultural and precious metals commodities: a pair vine copula approach

Kumar, Satish; Tiwari, Aviral Kumar; Raheem, Ibrahim D.; Ji, Qiang

doi:10.1080/00036846.2019.1705240

Cited by 22 publications

(9 citation statements)

References 53 publications

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“…The results of Živkov et al (2019b) , Melichar and Atems (2019) , Su et al (2019) , Kang et al (2019) , Chen et al (2019) and Živkov et al (2019b) can be extended in this segment of the findings, where they found a strong connectedness between crude oil prices and agricultural commodity markets in the periods of increased market turbulence. Besides, our results also extend the findings of Kumar et al (2019), Albulescu et al (2020) , Mensi et al (2017) , Kumar et al (2020) , Kang et al (2019) , Tiwari et al (2018) and Tiwari et al (2021) with the estimates of reaction of the relationship of crude oil prices to agricultural commodity markets. They argue a significant nexus between the agriculture and crude oil markets in terms of price and volatility spillovers.…”

Section: Resultssupporting

confidence: 91%

“…In recent years, there have been a large number of studies concentrating on the interrelatedness between crude oil and agricultural commodity markets. In general, most of the previous studies focused on two strands: price-level connectedness ( Vo et al, 2019 ; Taghizadeh-Hesary et al, 2019 ; Shiferaw, 2019 ; Su et al, 2019 ; Pal and Mitra, 2019 ; Melichar and Atems, 2019 ; Cheng and Cao, 2019 ; Živkov et al 2019a ; Živkov et al, 2019b ; Tiwari et al, 2021 ; Kumar et al, 2020 ; Mensi et al, 2017 ; Kumar et al, 2021 ) and volatility spillover effects across the markets under examination ( Lu et al, 2019 ; Fasanya and Akinbowale, 2019 ; Guhathakurta et al, 2020 ; Kang et al, 2019 ; Chen et al, 2019 ; Tiwari et al, 2018 ; Albulescu et al, 2020 ; Kang et al 2019 , 2019 ). The present study examines the price of the related markets in terms of connectedness and spillovers, and we will, therefore, review several articles with respect to this topic in this section.…”

Section: Literature Reviewmentioning

confidence: 99%

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Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak

Hung

2021

Resources Policy

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This paper represents an analysis of the spillover effects and time-frequency connectedness between crude oil prices and agricultural commodity markets using both the spillover index of Diebold and Yilmaz (2012) and the wavelet coherence model to evaluate whether the time-varying return spillover index exhibited the intensity and direction of transmission during the Covid-19 outbreak. Overall, the current results shed light on that in comparison with the pre-Covid-19 period, and the return spillover is more apparent during the Covid-19 crisis. However, levels of the intensity of this relationship vary through the period of research, with several intervals witnessing both negative and positive interactions. Further, our findings indicate significant heterogeneity among agriculture commodity markets in the degree of spillover to crude oil prices over time, amplifying our understanding of the economic channels through which the agriculture commodity markets are correlated. More importantly, there exist significant dependent patterns about the information spillovers across the crude oil and agriculture commodity markets might provide prominent implications for portfolio managers, investors, and government agencies.

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Section: Resultssupporting

confidence: 91%

Section: Literature Reviewmentioning

confidence: 99%

Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak

Hung

2021

Resources Policy

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“…The existence and otherwise of the TOM effect for different sample periods may change if the crisis period duration is changed. We have analyzed the TOM results by changing the classification of the crisis period as per Mollah et al (2016) and Kumar et al (2019), who consider GFC as the period from August 09, 2007 to December 31, 2009. Doing so, the number of observations for the pre-GFC, GFC and post-GFC period become 1981, 627, 2086, respectively.…”

Section: Robustness Resultsmentioning

confidence: 99%

Turn-of-the-month effect in three major emerging countries

Singh

Bhattacharjee

Kumar

2020

Self Cite

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PurposeThe purpose if this paper is to examine the turn-of-the-month effect in the equity market of three major emerging countries – Brazil, India and China – from January 2000 to December 2017.Design/methodology/approachOrdinary least square regression analysis is used to examine the presence of the turn-of-the-month effect and to test the efficiency of the emerging stock markets. The characteristics of the returns during the turn-of-the-month days are compared with that of the non-turn-of-the-month trading days.FindingsThe average returns during turn-of-the-month days for all the considered emerging market indices are significantly higher than the non-turn-of-the-month days for the full sample. For the subsample analysis, the average returns for Brazil and India for pre-GFC period are higher on the turn-of-the-month days than on the non-turn-of-the-month days. However, the effect disappears in China during the GFC period. During the crisis period, the results show that the turn-of-the-month effect disappears in Brazil and India, whereas for China, the effect is significant. For the post-GFC period, the-turn-of-the-month effect reappears for all the countries.Practical implicationsThe results have important implications for both traders and investors. The authors’ results indicate that the market participants can time the stock markets of these countries by taking long positions especially during the times when the turn-of-the-month effect is highly significant.Originality/valueTo the best of the authors’ knowledge, this paper is the first to study the turn-of-the-month effect, in the key emerging countries such as Brazil, China and India. Second, the authors divide the sample into three subperiods based on the 2008 GFC such as pre-GFC, GFC and post-GFC to understand the dynamic behavior of turn-of-the-month effect over time. Most importantly, the authors control for the day-of-the-week effect while examining the turn-of-the-month effect.

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“…For instance, Mejdoub and Ghorbel [ 13 ] investigated conditional dependence between oil price and renewable energy stock prices and considered threshold Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, see also Trabelsi [ 7 ] for tail risk dependence between oil and stocks of oil-exporting countries. Kumar et al [ 14 ] examined conditional dependence among not only energy commodities but also agricultural and precious metals commodities. Furthermore, Çekin et al [ 15 ] studied dependence structure among economic policy uncertainty (EPU) of Latin American countries.…”

Section: Introductionmentioning

confidence: 99%

Modeling risk dependence and portfolio VaR forecast through vine copula for cryptocurrencies

Syuhada

Hakim

2020

PLoS ONE

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Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance. It is often that we encounter several risks, in practice, instead of single risk. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. The dependence structure is presented through vine copula. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. It is found that the VaR forecast of returns, by considering vine copula-based dependence among different returns, has higher forecast accuracy than that of returns under prefect dependence assumption as benchmark. In addition, through vine copula, the aggregate VaR forecast has not only lower value but also higher accuracy than the simple sum of individual VaR forecasts. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.

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Dependence risk analysis in energy, agricultural and precious metals commodities: a pair vine copula approach

Cited by 22 publications

References 53 publications

Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak

Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak

Turn-of-the-month effect in three major emerging countries

Modeling risk dependence and portfolio VaR forecast through vine copula for cryptocurrencies

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