Analysis of lead-lag relationship and volatility spillover: evidence from Indian agriculture commodity markets

2021 ◽  
Vol 4 (3) ◽  
pp. 258
Author(s):  
Muneer Shaik ◽  
Abhiram Kartik Lanka ◽  
Gurmeet Singh
Keyword(s):  
Commodity Markets ◽  
Volatility Spillover ◽  
Indian Agriculture

Analysis of lead-lag relationship and volatility spillover: evidence from Indian agriculture commodity markets

2021 ◽  
Vol 4 (3) ◽  
pp. 258
Author(s):  
Gurmeet Singh ◽  
Abhiram Kartik Lanka ◽  
Muneer Shaik
Keyword(s):  
Commodity Markets ◽  
Volatility Spillover ◽  
Indian Agriculture

Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture

2020 ◽  
Vol 20 ◽  
pp. 100111 ◽  
Author(s):  
Roy Endré Dahl ◽  
Atle Oglend ◽  
Muhammad Yahya
Keyword(s):  
Crude Oil ◽  
Commodity Markets ◽  
Volatility Spillover

scholarly journals Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis

2011 ◽  
Vol 33 (3) ◽  
pp. 497-503 ◽  
Author(s):  
Xiaodong Du ◽  
Cindy L. Yu ◽  
Dermot J. Hayes
Keyword(s):  
Bayesian Analysis ◽  
Crude Oil ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Agricultural Commodity

Volatility Spillover Effect in Commodity Derivatives Market: Empirical Evidence Through Generalized Impulse Response Function

2019 ◽  
Vol 23 (4) ◽  
pp. 374-396
Author(s):  
Bhabani Sankar Rout ◽  
Nupur Moni Das ◽  
K. Chandrasekhara Rao
Keyword(s):  
Empirical Evidence ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Spillover Effect ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Agricultural Commodities ◽  
Commodity Derivatives ◽  
Volatility Spillover Effect

The present work is specifically directed to examine the volatility spillover mechanism in Indian commodity derivatives market. It has especially focused on comparing the agricultural and metal commodity segment by considering five agri-commodities and five metal commodities. The study period taken is 2010–2015 for understanding the mechanism between the spot and the futures commodity markets. Generalized impulse response function is mainly used to check the magnitude of volatility spill, pattern of volatility and lead–lag relationship. The result shows metal commodities are more prominent and investment worthy than agricultural commodities.

scholarly journals Volatility Spillover Among Equity and Commodity Markets

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402092441
Author(s):  
Tariq Aziz ◽  
Ranjeeta Sadhwani ◽  
Ume Habibah ◽  
Mazin A. M. Al Janabi
Keyword(s):  
United States ◽  
The United States ◽  
Equity Market ◽  
Commodity Markets ◽  
Commodity Market ◽  
Volatility Spillover ◽  
Monthly Data ◽  
West Texas ◽  
Oil Gas ◽  
Autoregressive Conditional Heteroscedasticity

This study aims to examine volatility spillover among equity and commodity markets of the United States. The analysis focuses on crude oil (Brent and WTI [West Texas Intermediate]), rice, and gasoline. For the analysis, generalized autoregressive conditional heteroscedasticity (GARCH) (1, 1) model is applied on monthly data for the period of February 2005 to December 2016. Results show that there is no volatility spillover from commodity market (gold, oil, gas, and rice) to equity market, whereas it only exists in few commodity markets, from oil to rice and gas. The study also finds that there is neither mean spillover nor volatility spillover among gold and equity market; therefore, investor can invest in equity and gold to diversify risk of portfolio.

scholarly journals Erratum to “Volatility Spillover Among Equity and Commodity Markets”

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402096330
Keyword(s):  
Commodity Markets ◽  
Volatility Spillover

Price discovery and volatility spillover: an empirical evidence from spot and futures agricultural commodity markets in India

2020 ◽  
Vol 10 (4) ◽  
pp. 447-473 ◽  
Author(s):  
Manogna R L ◽  
Aswini Kumar Mishra
Keyword(s):  
Granger Causality ◽  
Futures Markets ◽  
Price Discovery ◽  
Spillover Effects ◽  
Futures Market ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Agricultural Commodity ◽  
Content Type ◽  
Spot Prices

PurposePrice discovery and spillover effect are prominent indicators in the commodity futures market to protect the interest of consumers, farmers and to hedge sharp price fluctuations. The purpose of this paper is to investigate empirically the price discovery and volatility spillover in Indian agriculture spot and futures commodity markets.Design/methodology/approachThis study uses Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) to examines the price discovery and spillover effects for nine most liquid agricultural commodities in spot and futures markets traded on National Commodity and Derivatives Exchange (NCDEX).FindingsThe VECM results show that price discovery exists in all the nine commodities with futures market leading the spot in case of six commodities, namely soybean seed, coriander, turmeric, castor seed, guar seed and chana. Whereas in case of three commodities (cotton seed, rape mustard seed and jeera), price discovery takes place in the spot market. The Granger causality tests indicate that futures markets have stronger ability to predict spot prices. Supporting these, the results from EGARCH volatility test reveal that there exist mutual spillover effects on futures and spot markets. Thus, it could be inferred that futures market is more efficient in price discovery of agricultural commodities in India.Research limitations/implicationsThese results can help the market participants to benefit by hedging out the uncertainty and the policymakers to design futures contracts to improve the efficiency of the agricultural commodity derivatives market.Practical implicationsThe findings provide fresh view on lead–lag relationship between future and spot prices using the latest data confirming that futures market indeed is dominant in price discovery.Originality/valueThere are very few studies that have explored the efficiency of the agricultural commodity spot and futures markets in India using both price discovery and volatility spillover in a detailed manner, especially at the individual agriculture commodity level.

Spillover effects between energies, gold, and stock: the United States versus China

2020 ◽  
Vol 31 (8) ◽  
pp. 1416-1447 ◽  
Author(s):  
Xie He ◽  
Tetsuya Takiguchi ◽  
Tadahiro Nakajima ◽  
Shigeyuki Hamori
Keyword(s):  
United States ◽  
Stock Market ◽  
Crude Oil ◽  
Spillover Effect ◽  
The United States ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
The Us ◽  
Volatility Spillover Effect

This study investigates the time–frequency dynamics of return and volatility spillovers between the stock market and three commodity markets: natural gas, crude oil, and gold via a comparative analysis between the United States and China is conducted with the help of new empirical methods. Our findings are as follows. First, in terms of time, return spillovers between crude oil and the stock market are strongest in two of the three commodity markets. Crude oil emits a net negative return spillover to the US stock market, and a net positive return spillover to the Chinese stock market. By contrast, the strongest volatility spillover effect is transmitted to the stock markets of both countries through gold. However, gold has a net positive volatility spillover effect on the US stock market and a net negative effect on the Chinese stock market. In the frequency domain, most of the return spillover is produced in the short term, and most of the volatility spillover occurs in the long term. In addition, the moving-window method reveals the dynamic nature of the spillover effect. Some extreme events can have a dramatic effect on the spillover index. Conversely, the spillover effect differs significantly between the two countries and is characterized by time variation and frequency dependence.

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