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Journal articles on the topic 'Futures markets'

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Published: 4 June 2021
Last updated: 4 May 2024

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1

Kjellberg, Hans, Kaj Storbacka, Melissa Akaka, Jennifer Chandler, John Finch, Sara Lindeman, Helge Löbler, Katy Mason, Janet McColl-Kennedy, and Suvi Nenonen. "Market futures/future markets: Research directions in the study of markets." Marketing Theory 12, no. 2 (May 17, 2012): 219–23. http://dx.doi.org/10.1177/1470593112444382.

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2

Hirshleifer, David, Daniel R. Siegel, and Diane F. Siegel. "Futures Markets." Journal of Finance 46, no. 4 (September 1991): 1564. http://dx.doi.org/10.2307/2328874.

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3

Kuenne, Robert E. "Futures markets." Energy Economics 8, no. 2 (April 1986): 127–28. http://dx.doi.org/10.1016/0140-9883(86)90037-x.

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4

Cornell, Bradford. "Futures markets." Journal of Monetary Economics 16, no. 1 (July 1985): 133–35. http://dx.doi.org/10.1016/0304-3932(85)90012-1.

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5

Dhivya, R., M. Prahadeeswaran, R. Parimalaragan, C. Thangamani, and S. Kavitha. "Commodity Future Trading and Cointegration of Turmeric Markets in India." Asian Journal of Agricultural Extension, Economics & Sociology 41, no. 9 (June 27, 2023): 190–99. http://dx.doi.org/10.9734/ajaees/2023/v41i92031.

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The government has reduced its direct market intervention in order to promote private sector engagement based on market forces, Farmers in an agriculture-dominated economy like India suffer not only yield risk but also pricing risk. As a result, agricultural products are now more vulnerable to market risks related to pricing and other factors. The futures market has to decide the prices of a commodity on the basis of demand and supply. It is important to know about the bi-directional and unidirectional relationship between different market’s the prices and future and Spot markets in India, price discovery process and price forecasting in Indian agricultural commodities. Knowing about different market’s price Integration will help us to know the prevailing prices in various markets and also the impact of one market’s price on another. It will help the farmers to know the different pricing statuses in different markets. The study analyses the efficiency of commodity futures of turmeric traded in NCDEX for 2016-2022 and the cointegration of theNizamabad, Erode, Sangli and Cuddapah Markets of India. In agriculture, commodity futures and derivatives are essential to the process of managing price risk.
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McKenzie, Andrew M., and Matthew T. Holt. "Market efficiency in agricultural futures markets." Applied Economics 34, no. 12 (August 2002): 1519–32. http://dx.doi.org/10.1080/00036840110102761.

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7

Dey, Kushankur, and Debasish Maitra. "Can futures markets accommodate Indian farmers?" Journal of Agribusiness in Developing and Emerging Economies 6, no. 2 (November 14, 2016): 150–72. http://dx.doi.org/10.1108/jadee-08-2013-0029.

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Purpose It has become an ongoing debate whether Indian commodity futures markets can accommodate farmers. The purpose of this paper is to examine whether Indian commodity futures markets help rationalize farmers’ price expectation. The study starts with questions on the efficiency and other roles of commodity futures markets. Design/methodology/approach From a sectoral standpoint and economic importance, the study considers pepper, coffee, and natural rubber (NR) futures and spot markets. The efficiency of futures markets, divergence/convergence and causality between futures and spot markets have been studied by employing co-integrations, error correction and causality models. The sample period of the data are taken from the inception of futures trading. These three commodities are also compared on the basis of trading at the futures markets vs spot markets. Findings Analysis shows that though pepper futures market is informationally efficient in price discovery, while coffee and NR spot markets do the process faster. Pepper and coffee futures and spot prices exhibit the convergence; NR shows a sign of divergence. Unidirectional causality from pepper futures to spot market is observed wherein the former was weakly exogenous to the latter and while, bidirectional causality is observed in coffee and rubber. Coffee spot appears weakly exogenous while this remains inconclusive in the case of NR. Research limitations/implications The authors analyzed the futures markets in rationalizing the spot market price in three plantation crops in India. In order to make the study more generalizable, further research is warranted in other commodities including those prices of which are government regulated. Originality/value The paper is unique in terms of understanding the interaction or interrelationship between futures markets and spot markets and drawing inferences about the role of futures markets in price formation in plantation commodities like pepper, coffee and NR.
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Whitchurch, Celia. "Futures and markets." Perspectives: Policy and Practice in Higher Education 5, no. 4 (January 2001): 91–92. http://dx.doi.org/10.1080/1360310120081635.

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9

Gehr, Adam K. "Undated futures markets." Journal of Futures Markets 8, no. 1 (February 1988): 89–97. http://dx.doi.org/10.1002/fut.3990080108.

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10

Chang, Matthew C., Chih-Ling Tsai, Rebecca Chung-Fern Wu, and Ning Zhu. "Market uncertainty and market orders in futures markets." Journal of Futures Markets 38, no. 8 (April 17, 2018): 865–80. http://dx.doi.org/10.1002/fut.21918.

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11

Ranganathan, Thiagu, and Usha Ananthakumar. "Market efficiency in Indian soybean futures markets." International Journal of Emerging Markets 9, no. 4 (September 9, 2014): 520–34. http://dx.doi.org/10.1108/ijoem-12-2011-0106.

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Purpose – The National commodity exchanges were established in India in the year 2003-2004 to perform the functions of price discovery and price risk management in the economy. The derivatives market can perform these functions properly only if they are efficient and unbiased. So, there is a need to properly evaluate these aspects of the Indian commodity derivatives market. The purpose of this paper is to test the market efficiency and unbiasedness of the Indian soybean futures markets. Design/methodology/approach – The paper uses cointegration and a QARCH-M-ECM-based framework to test the market efficiency and unbiasedness in the soybean futures contract traded in the National Commodity Derivatives Exchange (NCDEX). The cointegration test is used to test the long-run unbiasedness and market efficiency of the contract, while the QARCH-M-ECM model is used to test the short-run market efficiency and unbiasedness of the contract by allowing for a time-varying risk premium. The price data is also tested for presence of structural breaks using a Zivot and Andrews unit root test. Findings – The soybean contract is unbiased in the long run, but there are short-run market inefficiencies and also a presence of a time-varying risk premium. Though the weak form of market efficiency is rejected in the short run, the semi-strong market efficiency is not rejected based on the forecasts. Originality/value – This is the first paper to consider time-varying risk premium while performing the tests of market efficiency and unbiasedness on Indian commodity markets.
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12

Kumar, Brajesh, and Ajay Pandey. "Market efficiency in Indian commodity futures markets." Journal of Indian Business Research 5, no. 2 (May 31, 2013): 101–21. http://dx.doi.org/10.1108/17554191311320773.

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13

Liu, Qingfu, Qian Luo, Yiuman Tse, and Yuchi Xie. "The market quality of commodity futures markets." Journal of Futures Markets 40, no. 11 (April 8, 2020): 1751–66. http://dx.doi.org/10.1002/fut.22115.

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14

Ronghua, Ju, and Yang Zhiling. "Assessing the functional efficiency of agricultural futures markets in China." China Agricultural Economic Review 11, no. 2 (May 7, 2019): 431–42. http://dx.doi.org/10.1108/caer-03-2018-0056.

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Purpose The purpose of this paper is to quantitatively analyse the changes in the functional efficiency of the six Chinese agricultural futures markets and compare the relative behaviour of different futures markets. In addition, this paper analyses the causes of differences in the functional efficiency of agricultural futures markets and advances policy suggestions. Design/methodology/approach The method used in this paper is the social loss index proposed by Stein (1981, 1986). This method can quantitatively measure the functional efficiency of agricultural futures markets from the perspective of social welfare. The indicator is calculated for the 2009–2017 period and for several sub-periods. The data are from the CSMAR research data services in China. Findings Preliminary results suggest that the longer it takes for an agricultural futures contract to reach maturity, the lower the functional efficiency of its market. Second, the functional efficiency of the agricultural futures markets in China is improved except for that of the wheat futures market. Finally, the corn futures market is most efficient probably due to the progress of marketization, while the strong wheat futures market is most inefficient probably due to the decrease in futures market liquidity. Originality/value This paper uses a more reasonable method to study the functional efficiency of Chinese agricultural futures markets and then analyses the causes of differences in the functional efficiency of agricultural futures markets.
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15

Tsiaras, Konstantinos. "Contagion in Futures FOREX Markets for the Post- Global Financial Crisis: A Multivariate FIGARCHcDCC Approach." Journal of Quantitative Methods 4, no. 1 (February 28, 2020): 1. http://dx.doi.org/10.29145/2020/jqm/040102.

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This paper seeks to investigate the time-varying conditional correlations to the futures FOREX market returns. We employ a dynamic conditional correlation (DCC) Generalized ARCH (GARCH) model to find potential contagion effects among the markets. The under investigation period is 2014-2019. We focus on four major futures FOREX markets namely JPY/USD, KRW/USD, EUR/USD and INR/USD. The empirical results show an increase in conditional correlation or contagion for all the pairsof future FOREX markets. Based on the dynamic conditional correlations, KRW/USD seems to be the safest futures FOREX market. The results are of interest to policymakers who provide regulations for the futures FOREX markets. JEL Classification Codes: C58, C61, G11, G15
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16

Tang, Sikai. "Investor Sentiment in Stocks and Futures Markets." Advances in Economics, Management and Political Sciences 41, no. 1 (November 10, 2023): 49–54. http://dx.doi.org/10.54254/2754-1169/41/20232033.

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As nations implement interest rate hikes and the economy heads towards a downward trajectory, investor sentiment undergoes a significant transformation. While extensive research has explored how investor sentiment influences stock markets, there is limited investigation into its effects on futures markets. This study aims to delve into various methodologies used to examine investor sentiment, including existed indexes, news analysis, and machine learning techniques. The study will evaluate how investor sentiment influences futures market and explore the connection between futures market and stock market. It acknowledges that there are both similarities and differences in studying investor sentiment in the stock and futures markets and suggests that futures market may play a role in affecting stock market prices. By conducting a thorough review of existing literature, the objective is to enhance our understanding of price dynamics in both the futures and stock markets within the context of the current global economic downturn.
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17

Karmakar, Madhusudan. "Price Discoveries and Volatility Spillovers in S&P CNX Nifty Future and its Underlying Index CNX Nifty." Vikalpa: The Journal for Decision Makers 34, no. 2 (April 2009): 41–56. http://dx.doi.org/10.1177/0256090920090204.

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In a perfectly functioning world, every piece of information should be reflected simultaneously in the underlying spot market and its futures markets. However, in reality, information can be disseminated in one market first and then transmitted to other markets due to market imperfections. And, if one market reacts faster to information than the other, a lead-lag relation is observed The lead-lag relationship in returns and volatilities between spot and futures markets is of interest to academics, practitioners, and regulators. In India, there are very few studies which have investigated the lead-lag relationship in the first moment of the spot and futures markets This study investigates the lead-lag relationship in the first moment as well as the second moment between the S&P CNX Nifty and the Nifty future. It also investigates how much of the volatility in one market can be explained by volatility innovations in the other market and how fast these movements transfer between these markets. It conducts Multivariate Cointegration tests on the long-run relation between these two markets. It investigates the daily price discovery process by exploring the common stochastic trend between the S&P CNX Nifty and the Nifty future based on vector error correction model (VECM). It examines the volatility spillover mechanism with a bivariate BEKK model. Finally, this study captures the effects of recent policy changes in the Indian stock market. The results reveal the following: The VECM results show that the Nifty futures dominate the cash market in price discovery. The bivariate BEKK model shows that although the persistent volatility spills over from one market to another market bi-directionally, past innovations originating in future market have the unidirectional significant effect on the present volatility of the spot market. The findings of the study thus suggest that the Nifty future is more informationally efficient than the underlying spot market. These findings may provide insights on the information transaction and index arbitrage between the CNX Nifty and futures markets.
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18

Yoo, Shiyong. "Volatility and Trading Volumes of Trader Types in KOSPI200 Index, Futures, and Options Markets." Journal of Derivatives and Quantitative Studies 22, no. 1 (February 28, 2014): 91–115. http://dx.doi.org/10.1108/jdqs-01-2014-b0005.

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In this study, we explore the empirical relationship between trading volume and volatility among KOSPI200 index stock market, futures and options markets. In particular, in explaining the volatility of each market, the trading in other markets, as well as the trading volume of other markets, also served as explanatory variables. In other words, cross-market effects of trading volume by investor types are analyzed. The empirical results show that there exist the cross-market effects of the relationship between trading volume and volatility in deeply integrated financial markets such as KOSPI200 index stock, futures and options markets. That is, the volatility of one market is explained by the trading volume of trader types in other financial markets. And, overall options trading increases the volatility of each market, while the overall futures trading volume of foreign investors reduce the volatility of each market. Trading volume of Individual investors does not reduce the volatilities of KOSPI200 index and futures markets. That is, trading volume of Individual investors in stock, futures, and options markets increase the volatilities of stock and futures. This implies that foreign investors are informed traders, whereas individual investors are liquidity traders.
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19

Kang, Sang Hoon. "Analyzing the Network Structure of Spillover Connectedness Across Index Futures Markets During Market Distress Periods." Journal of Derivatives and Quantitative Studies 27, no. 2 (May 31, 2019): 141–64. http://dx.doi.org/10.1108/jdqs-02-2019-b0001.

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This paper aims to investigate the network structure of connectedness among global index futures markets in different distress periods. In this purpose, this employs the multivariate DECO-GARCH model of Engle and Kelly (2012) and the spillover index method of Diebold and Yilmaz (2014). From empirical analysis, this paper finds an evidence of a positive equicorrelation among global index futures, implying the contagion effect in global index futures markets. The spillover connectedness is intensified due to recent market distress, i.e., the 2008-2009 GFC, the 2010-2012 ESDC, the collapse of Chinese stock market in 2015, and the US FRB interest rate hike in 2018. Further, this paper measures the direction and strength of volatility connectedness assessed by the net pairwise directional spillover indexes. Thus this paper identifies the net spillover connectedness (transmitter/receiver) across global index futures markets. Finally, this paper shows the network structure of spillover connectedness in different market distress periods, and provides the channels of spillover connectedness across global index future markets.
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20

Mallikarjunappa, T., and E. M. Afsal. "Price Discovery Process and Volatility Spillover in Spot and Futures Markets: Evidences of Individual Stocks." Vikalpa: The Journal for Decision Makers 35, no. 2 (April 2010): 49–62. http://dx.doi.org/10.1177/0256090920100205.

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This paper analyses information-based superiority of markets mainly with an objective of exploring arbitrage opportunities. It attempts to determine the lead-lag relationship between spot and futures markets in the Indian context by using high frequency price data of twelve individual stocks, observed at one-minute interval. The study applies the concept of co-integration and establishes the spot-futures relationship using Vector Error Correction Mechanism (VECM) represented by EGARCH framework. To study the price discovery process in the two markets, five lags each of one-minute resolution for nine individual stocks and four lags for the remaining three stocks are chosen. The key results of the study are given below: There is a contemporaneous and bi-directional lead-lag relationship between the spot and futures markets. A feedback mechanism of short life is functional between the two markets. Price discovery occurs in both the markets simultaneously. There exists short-term disequilibrium that could be corrected in the next period. Volatility spillover from spot market to futures market is present in such a way that a decrease in spot volatility leads to a decrease in futures volatility. Volatility shocks are asymmetric and persistent in both the markets. Spillover from futures market to spot market is not significant. Neither spot nor futures assume a considerable leading role and neither of the markets is supreme in price discovery. In the case of 33.33 per cent of spot values and 33.33 per cent of futures values, there exists short-term disequilibrium that could be corrected in the next period by decreasing the prices. Spot market volatility spills over to futures market in most of the cases (66.66 %) and a decrease in spot volatility brings about a decrease in futures volatility in 50 per cent of the cases. Spillover effect from futures to spot market is present and significant in 91.66 per cent of stocks and is more than the spillover effect from spot to futures (50% valid cases). The markets are highly integrated. Asymmetric behaviour of volatility shocks is mixed in both the markets. Asymmetric volatility is detected in 50 per cent of the cases of spot market and 58.33 per cent cases of futures market. Stocks exhibiting asymmetric volatility show more sensitivity to negative shocks. There are no cases of market becoming more volatile in response to good news.
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21

Rastogi, Shailesh, and Chaitaly Athaley. "Volatility Integration in Spot, Futures and Options Markets: A Regulatory Perspective." Journal of Risk and Financial Management 12, no. 2 (June 9, 2019): 98. http://dx.doi.org/10.3390/jrfm12020098.

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The aim of this paper is to study the integration of volatility in the three markets, viz. spot, futures and options, in order to provide input for hedging purposes and the formulation of policies for derivatives. The generalized method of moments (GMM) is used to capture the simultaneous equation modelling of volatility in the three markets. The integration of the volatility in the three markets is also tested for structural breaks. The main finding of the paper is that the volatility in the options market is not associated with volatility in spot and futures market. However, volatility in spot and futures markets are associated with each other. As a consequence, investors can use options for hedging purposes and policy makers do not need to be concerned about the imminent impact of options markets on spot markets. To the best of the authors’ knowledge, there is no other study which discusses the integration of volatility in the three markets. Moreover, the finding of this paper that the options market behaves differently compared to the futures market has also not been discussed in earlier studies.
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22

Rentzler, Joel, Kishore Tandon, and Susana Yu. "Short-term market efficiency in the futures markets: TOPIX futures and 10-year JGB futures." Global Finance Journal 16, no. 3 (March 2006): 330–53. http://dx.doi.org/10.1016/j.gfj.2006.01.006.

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23

Peake, Charles F. "Causality in Futures Markets." CFA Digest 37, no. 2 (May 2007): 13–15. http://dx.doi.org/10.2469/dig.v37.n2.4585.

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24

Bryant, Henry L., David A. Bessler, and Michael S. Haigh. "Causality in futures markets." Journal of Futures Markets 26, no. 11 (2006): 1039–57. http://dx.doi.org/10.1002/fut.20231.

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25

Li, Yin-Hua, Guo-Dong Yang, and m. m. "A Study on Co-movements and Information Spillover Effects Between the International Commodity Futures Markets and the South Korean Stock Markets: Comparison of the COVID-19 and 2008 Financial Crises." Journal of Korea Trade 27, no. 5 (October 31, 2023): 167–98. http://dx.doi.org/10.35611/jkt.2023.27.5.167.

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Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises. Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat. Findings - The results of the co-movement analysis were as follows: First, it was shown that the co- movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed. Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.
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26

Ohk, Ki Yool. "The Effect of Futures Trading on Spot Market Liquidity." Journal of Derivatives and Quantitative Studies 13, no. 1 (May 31, 2005): 29–52. http://dx.doi.org/10.1108/jdqs-01-2005-b0002.

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This study analyzes the effect of stock index futures trading on the price volatility and liquidity of spot markets, It is found that spot price volatility increases significantly after stock index futures are listed, This study partitions the trading activity series of sPOt markets into expected and unexpected components, and documents that unexpected spot-trading activities are associated with smaller sPOt price movements subsequent to the introduction of futures trading, This imolies that spot market liquidity has been increased by the intraduction of futures trading, Furthermore, this study examines the effect of futures-trading activity on the liquidity of spot markets, Results show that active futures markets enhance the liquidity of soot markets.
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27

Beck, Stacie E. "Cointegration and market efficiency in commodities futures markets." Applied Economics 26, no. 3 (March 1, 1994): 249–57. http://dx.doi.org/10.1080/00036849400000006.

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28

Kuhn, Betsey A., Frieda W. Shaviro, and Margaret M. Burke. "Market Regulation and International Use of Futures Markets." American Journal of Agricultural Economics 67, no. 5 (December 1985): 992–98. http://dx.doi.org/10.2307/1241360.

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29

Khan, Mohammed Arshad, Md Mobashshir Hussain, Asif Pervez, Mohd Atif, Rohit Bansal, and Hamad A. Alhumoudi. "Intraday Price Discovery between Spot and Futures Markets of NIFTY 50: An Empirical Study during the Times of COVID-19." Journal of Mathematics 2022 (August 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/2164974.

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The current study investigates the intraday dynamics of futures and spot markets in India. By analyzing one-minute data of Nifty 50 and the associated futures index, the study finds that both the markets are cointegrated. The results of the VECM reveal that any disequilibrium between the spot and futures market is restored by the spot market. Granger causality tests reveal that the spot and futures markets have a bidirectional causal relationship. Common factor weights and Hasbrouck’s information share (IS) reveal the greater role of the futures market in price discovery. Gonzalo and Granger's common factor model indicates that the permanent factor is made up of futures series only. Using the BEK-GARCH model, we found two-way volatility spillovers between the spot and futures markets. The futures market is found to have a greater impact in terms of volatility spillovers also. The findings of our research are relevant to investors, money managers, traders, and policymakers.
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30

Gurrib, Ikhlaas. "Are key market players in currency derivatives markets affected by financial conditions?" Investment Management and Financial Innovations 15, no. 2 (June 4, 2018): 183–93. http://dx.doi.org/10.21511/imfi.15(2).2018.16.

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This study investigates if the biggest players in major foreign currencies futures markets are affected by current and previous financial conditions. Using root mean squared errors (RMSE), normalized RMSE, and Nash-Sutcliffe efficiency, this study compares the impact of current, 1 and 2 week lags of financial conditions onto foreign currency futures players’ net positions. The financial conditions indices used are UFCI, STLFSI, NFCI and ANFCI with weekly data set from January 2007 till December 2018. The US dollar index futures is included as a benchmark, since the financial conditions are based on US data and the most actively traded foreign currencies are paired against the USD. While RMSE and NRMSE gave mixed results into how current, 1 week and 2 weeks lagged Financial Conditions Indices (FCIs) values are related to speculators and hedgers’ net positions, lagged NFCI captured the highest correlation with both players’ net positions in Japanese Yen. 95% prediction levels encompassed the actual net positions held, including the financial crisis of 2008-2009. Forecasts were lower (higher) for hedgers (speculators) than actual net positions held during the same period. Comparatively, in the period 2016-2017, hedgers (speculators) net positions forecasts were higher (lower) than actual positions. The latter could be explained by FCIs not being affected during this period’s event, compared to net positions. While net positions data were stationary, excess kurtosis was present pointing to non-normal and autocorrelated series. This suggests the need to look into other components like non-reportable long or short positions in future analysis.
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31

R L, Manogna, and Aswini Kumar Mishra. "Price discovery and volatility spillover: an empirical evidence from spot and futures agricultural commodity markets in India." Journal of Agribusiness in Developing and Emerging Economies 10, no. 4 (May 23, 2020): 447–73. http://dx.doi.org/10.1108/jadee-10-2019-0175.

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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.
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32

Hong, Bae Gi, and Su Jae Jang. "A Comparative Analysis of Informational Efficiency of KOSDAQ50 and KOSPI200 Index Futures." Journal of Derivatives and Quantitative Studies 11, no. 2 (November 30, 2003): 27–49. http://dx.doi.org/10.1108/jdqs-02-2003-b0002.

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This paper examines the information efficiency of KOSDAQ50 and KOSPI200 index futures markets. The study analyzes and compares both markets in three respects : 1) price discovery (lead-lag relationship between spot and futures markets.), 2) volatility-volume relationship, and 3) mispricings between spot and futures prices. The first, analysis shows the in the KOSPI200 market, futures price leads spot price. While spot price leads futures price in the KOSDAQ50 market. The second analysis shows that the volatility-volume relation is positive in the KOSPI200 futures market, supporting the hypothesis of mixture of distribution. In contrast, there is little relation between volume and volatility in the KOSDAQ50 futures market. This result casts doubt that the futures market price reflects information. The last analysis shows that the magnitude of mispricing becomes smaller with more volume in the KOSPI200 futures market, while it becomes larger with more volume in the KOSDAQ50 futures market. The overall results imply that the KOSDAQ50 futures market is less informationally efficient that the KOSPI200 market. The inefficiency appears due to the lack of institutional investor participation, especially securities firms, in making up the market.
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Bhagwat, Shree, and Angad Singh Maravi. "THE ROLE OF FORWARD MARKETS COMMISSION IN INDIAN COMMODITY MARKETS." International Journal of Research -GRANTHAALAYAH 3, no. 11 (November 30, 2015): 87–105. http://dx.doi.org/10.29121/granthaalayah.v3.i11.2015.2919.

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This paper examines the role of Forward Markets Commission (FMC) in Indian Commodity Markets. The Results show important developments of Forward Markets Commission. Commodity futures and derivatives have a crucial role to play in the price risk management process, especially in agriculture sector. The significance of commodity derivatives has increased in the current scenario. India has long history of trade in commodity derivatives. Organized commodity derivatives in India started as early as 1875, barely about a decade after they started in Chicago. Since 2003, when commodity futures’ trading was permitted, commodity futures market in India has experienced an unprecedented boom in terms of the number of modern exchanges, number of commodities allowed for derivatives trading as well as the value of futures trading in commodities. There are 6 national and 16 regional commodity exchanges recognized and regulated by the FMC. Different types of commodities such as agricultural; bullion, plantation, energy etc. is traded on commodity exchanges in the country. So considering these points an attempt has been made to know the regulatory framework of commodity futures and derivatives market in India and various developments in Indian commodity market and commodity exchanges. This study is an attempt to investigate the performance of Forward Markets Commission in India and its role in Indian commodity market.
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Herbert, John H. "Do Changes in Natural Gas Futures Prices Influence Changes in Natural Gas Spot Prices?" Energy Exploration & Exploitation 11, no. 5 (October 1993): 467–72. http://dx.doi.org/10.1177/014459879301100506.

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Data on natural gas futures and spot markets are examined to determine if variability in price on futures markets influences variability in price on spot markets. Using econometric techniques, it is found that changes in futures contract prices do not precede changes in spot market prices.
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35

Lievens, Eewoud, Kobe Tielens, and Erik Mathijs. "Creating a market for price swaps: Case study of an innovative risk management instrument in the Belgian-Dutch pear market." Agricultural Economics (Zemědělská ekonomika) 67, No. 1 (January 29, 2021): 33–40. http://dx.doi.org/10.17221/373/2020-agricecon.

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While the benefits of using futures to manage price risk are widely recognised, only certain groups of farmers have suitable futures at their disposal. This paper discusses an innovative instrument, developed in the Belgian-Dutch pear market, that provides an alternative to futures markets by creating a market for price swaps. Thus, the instrument provides some benefits of market-traded derivatives (like futures) while remaining a relatively simple instrument, which requires fewer market transactions. The paper describes key properties of the swap contracts and the platform used to trade them. In addition, it compares the conditions required for establishing price swap markets and futures markets. Thus, our study informs the design of similar risk management instruments for commodities and contexts where futures are absent.
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36

Floros, Christos, and Enrique Salvador. "Volatility, trading volume and open interest in futures markets." International Journal of Managerial Finance 12, no. 5 (October 10, 2016): 629–53. http://dx.doi.org/10.1108/ijmf-04-2015-0071.

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Purpose The purpose of this paper is to examine the effect of trading volume and open interest on volatility of futures markets. The authors capture the size and change in speculative behaviour in futures markets by examining the role of liquidity variables (trading volume and open interest) in the behaviour of futures prices. Design/methodology/approach The sample includes daily data covering the period 1996-2014 from 36 international futures markets (including currencies, commodities, stock indices, interest rates and bonds). The authors employ a two-stage estimation methodology: first, the authors employ a E-GARCH model and consider the asymmetric response of volatility to shocks of different sign. Further, the authors consider a regression framework to examine the contemporaneous relationships between volatility, trading volume and open interest. To quantify the percentage of volatility that is caused by liquidity variables, the authors also regress the estimated volatilities on the measures of open interest and trading volume. Findings The authors find that: market depth has an effect on the volatility of futures markets but the direction of this effect depends on the type of contract, and there is evidence of a positive contemporaneous relationship between trading volume and futures volatility for all futures contracts. Impulse-response functions also show that trading volume has a more relevant role in explaining market volatility than open interest. Practical implications These results are recommended to financial managers and analysts dealing with futures markets. Originality/value To the best of the authors’ knowledge, no study has yet considered a complete database of futures markets to investigate the empirical relation between price changes (volatility), trading volume and open interest in futures markets.
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Cheung, Yin-Wong, and Hung-Gay Fung. "Information Flows Between Eurodollar Spot and Futures Markets." Multinational Finance Journal 1, no. 4 (December 1, 1997): 255–71. http://dx.doi.org/10.17578/1-4-1.

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38

Tetik, Metin, and Ercan Özen. "Time-Varying Structure of the Optimal Hedge Ratio for Emerging Markets." Scientific Annals of Economics and Business 69, no. 4 (December 19, 2022): 521–37. http://dx.doi.org/10.47743/saeb-2022-0030.

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Emerging markets are more exposed to risk than developed markets. Therefore, they require risk management using futures market instruments. This study aims to determine the hedging effectiveness of the spot index market risks in the stock index futures market in Brazil, Russia, India, South Africa, and Turkey. Measuring the hedging effectiveness level of futures markets is vital for these countries because investors must remain in the stock markets for the sustainability of the financial markets and economies. Weekly closing data for the period from January 2009 to October 2021 were analyzed via a dynamic method referred to as flexible least squares (FLS). Although the FLS results show that futures transactions provide high hedging effectiveness for all countries within the scope of this study, country-specific conditions may reduce the hedging effectiveness.
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39

Kumar Mahalik, Mantu, Debashis Acharya, and M. Suresh Babu. "Price discovery and volatility spillovers in futures and spot commodity markets." Journal of Advances in Management Research 11, no. 2 (July 29, 2014): 211–26. http://dx.doi.org/10.1108/jamr-09-2012-0039.

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Purpose – The purpose of this paper is to investigate empirically the price discovery and volatility spillovers in Indian spot-futures commodity markets. Design/methodology/approach – The study has used four futures and spot indices of Multi-Commodity Exchange, Mumbai. The study also employs vector error correction model (VECM) and bivariate exponential Garch model (EGARCH) to analyze the price discovery and volatility spillovers in Indian spot-futures commodity market. Findings – The VECM shows that agriculture future price index (LAGRIFP), energy future price index (LENERGYFP) and aggregate commodity index (LCOMDEXFP) effectively serve the price discovery function in the spot market implying that there is a flow of information from future to spot commodity markets but the reverse causality does not exist. There is no cointegrating relationship between metal future price index (LMETALFP) and metal spot price index (LMETALSP). Besides the bivariate EGARCH model indicates that although the innovations in one market can predict the volatility in another market, the volatility spillovers from future to the spot market are dominant in the case of LENERGY and LCOMDEX index while LAGRISP acts as a source of volatility toward the agri-futures market. Research limitations/implications – The results are aggregate in nature. Further study at disaggregated level will provide further insights on behavior of specific commodity prices and the price discovery process. Originality/value – The paper provides useful information about the evolution and structures of futures commodity trading in India, related literature and relevant methodology concerning the hypotheses.
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Kaura, Ruchika, Nawal Kishor, and Namita Rajput. "VOLATILITY SPILLOVER BETWEEN SPOT AND FUTURES MARKET OF HIGHLY TRADED COMMODITIES IN INDIA: The DCC-GARCH Approach." Australian Journal of Business and Management Research 05, no. 09 (July 10, 2018): 34–49. http://dx.doi.org/10.52283/nswrca.ajbmr.20180509a04.

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This study intends to examine the volatility spillover effects and measure the time-varying correlations between futures and spot prices of thirteen highly traded commodities traded on Multi Commodity Exchange (MCX) of India. The research uses Exponential GARCH proposed by Nelson (1991) to explore the direction and magnitude of spillover effects between futures and spot commodity market and employs Dynamic Conditional Correlation (DCC) GARCH proposed by Engle (2002) to demonstrate the time varying conditional correlation between heteroscedastic coefficients of the futures and spot markets. Empirical results show that significant and asymmetric bi-directional volatility spillover effects exist in case of most of the selected commodities, even though, the magnitude of volatility spillover is found larger in the direction from futures market to spot market. The dynamic correlation between the conditional variance of the spot and future markets is found to be significant in case of all the commodities except Silver and Copper. It proves that significant volatility spillover effect is present between spot and futures markets of selected commodities. Understanding of volatility transmission and interrelationship between spot and futures commodity market will help investors make right investment decisions, portfolio optimization and financial risk management. Policy makers and regulators can use this knowledge in planning and implementing appropriate regulatory framework. Much of the earlier research focuses on inter market volatility spillover taking into consideration two or more different financial markets. This study focuses on intra market volatility spillover by studying the interactions of spot-futures prices of commodities. Also, considering the time-varying nature of conditional correlations, this study employs EGARCH and multivariate GARCH (DCC) to capture the volatility spillover effects instead of univariate GARCH or standard linear VAR models.
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Xu, Yuanyuan, Fanghui Pan, Chuanmei Wang, and Jian Li. "Dynamic Price Discovery Process of Chinese Agricultural Futures Markets: An Empirical Study Based on the Rolling Window Approach." Journal of Agricultural and Applied Economics 51, no. 04 (August 1, 2019): 664–81. http://dx.doi.org/10.1017/aae.2019.23.

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AbstractWe investigate the dynamic evolution of the price discovery function in Chinese agricultural futures markets using a newly developed rolling window cointegration approach. The results show that, compared with wheat and rice, the futures-spot cointegration relationship in the soybean and corn markets tends to be more durable and frequent. Dynamic cointegration analysis indicates that the recent market-oriented reforms in China have boosted the price discovery function of soybean and corn futures markets, whereas price stabilization policies tend to weaken the price discovery function of futures markets. The difference in price discovery function is attributed to differences in market mechanisms and Chinese agricultural policies.
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Hui, GAO, and GAO Tian Chen. "Volatility Asymmetry and Spillover Effects in Crude Oil Futures Market: Evidence from China." Applied Economics and Finance 9, no. 3 (August 24, 2022): 82. http://dx.doi.org/10.11114/aef.v9i3.5693.

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In order to study the international influence and function of domestic crude oil futures prices in depth, based on the daily data of 2018-2022, this paper uses Granger causality test, cointegration test, ECM model and several forms of GARCH model to make empirical research on the price correlation, volatility, asymmetry and risk spillover effects of domestic crude oil, Brent crude oil, WTI crude oil and Oman crude oil futures in the three major foreign crude oil markets. The study found that there is a Granger causal relationship and a cointegration relationship between domestic and foreign crude oil futures prices. There are interactions in the crude oil futures markets at home and abroad, and there are different fluctuation patterns in the short-term fluctuation process. There are asymmetries, leverage effects and spillover effects in the volatility of the four crude oil futures markets at home and abroad with different intensities. The bearish news of the three foreign markets is greater than the impact of the bullish news than that of the domestic one. There are risk spillover effects and volatility effects between domestic and the three foreign crude oil futures markets, and the domestic spillover effect on foreign crude oil is greater than the spillover effect on domestic crude oil from abroad. The conclusion is that the international influence of domestic crude oil futures prices is stronger than that of Omani crude oil, and weaker than Brent and WTI crude oil. The leverage effect of the three foreign markets is greater than that of China, and the leverage effect of the Oman crude oil futures market is the strongest. The asymmetry and spillover effect of fluctuations in the domestic and foreign crude oil futures markets lead to the risk and speculation of the three foreign markets being greater than that of the domestic one, and the speculative arbitrage through the domestic and foreign crude oil markets may cause huge market risks, and it is suggested that the internationalization of the domestic futures market needs more systems and mechanisms to support the construction.
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Fan, Xuejun, and De Du. "The spillover effect between CSI 500 index futures market and the spot market." China Finance Review International 7, no. 2 (May 15, 2017): 249–72. http://dx.doi.org/10.1108/cfri-08-2016-0103.

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Purpose Focusing on the spillover effects between the CSI 500 stock index futures market and its underlying spot market during April to September 2015, the purpose of this paper is to explore whether Chinese stock index futures should be responsible for the 2015 stock market crash. Design/methodology/approach Using both linear and non-linear econometric models, this paper empirically examines the mean spillover and the volatility spillover between the CSI 500 stock index futures market and the underlying spot market. Findings The results showed the following: the CSI 500 stock index futures market has significant one-way mean spillover effect on its spot market. The volatility in CSI 500 stock index futures market also has a significant positive spillover effect on its spot stock market, and the mean value of dynamic correlation coefficient between the two market volatility is 0.4848. The spillover effect of the CSI 500 stock index futures market on the underlying spot market is significantly asymmetric, characterized by relatively moderate and slow during the period of the markets rising, yet violent and rapid during the period of the markets falling. The findings suggest that although the stock index futures itself was not the “culprit” of Chinese stock market crash in 2015, its existence indeed accelerated and exacerbated the stock market’s decline under the imperfect trading system. Originality/value Different from the existing literature mainly focusing on CSI 300 stock index futures, this paper empirically examines the impact of the introduction of CSI 500 stock index futures on 2015 Chinese stock market crash for the first time.
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44

Long, Zhengfang. "A Study on the Differences and Correlations Between Soybean Futures Markets in USA and China." Advances in Economics, Management and Political Sciences 59, no. 1 (January 5, 2024): 238–48. http://dx.doi.org/10.54254/2754-1169/59/20231095.

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Futures market in the United States has gone through more than 150 years and has the most developed grain futures market in the world. By selecting soybean, the most representative product in the futures market, in this work, the ADF test and Granger test are utilized to objectively distinguish between the soy bean futures markets of China and America. This article examines the effects of exceptional events on the soybean futures in two nations in light of the two typical special events of the Sino-US trade war and the COVID-19 epidemic. The findings are made by the author: there is a strong correlation between soybean futures and spot in China and the US. However, the association is not as prevalent in China as it is in the US. There is a cointegration link between the soy bean futures markets in China and the US, and there is a Granger guiding relationship for China on the USA's soy bean futures market. The impact of the Chinese and American markets on each other is long-term. The COVID-19 epidemic and the Sino-US trade dispute are investigated as well by the author, who discovers a negative link between the prices on the soybean futures markets of the two nations. The Chinese bean futures market is characterized by a lack of risk appetite and an overwhelming reliance on the US.
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45

Thazhugal Govindan Nair, Saji. "Measuring volatility spillovers and asymmetric responses of Agri commodity prices: evidence from spices and rubber futures in India." Indian Growth and Development Review 14, no. 2 (June 2, 2021): 242–67. http://dx.doi.org/10.1108/igdr-10-2020-0147.

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Purpose This paper aims to investigate price responses and volatility spillovers between commodity spot and futures markets. The study ultimately seeks the evidence-based claims on the efficiency of the long run and short run horizontal price transmissions from futures markets to spot markets. Design/methodology/approach This study used the most recent daily price series of pepper, cardamom and rubber, during the period 2004–2019, use “cointegration-ECM-GARCH framework” and verify the persisting validity of the “expectancy theory” of commodity futures pricing. Findings The results offer overwhelming evidence of futures market dominance in the price discoveries and volatility spillovers in spot markets. However, this paper finds asymmetric responses between cash and futures prices across markets. The hedging efficiency of futures contracts is commodities specific’ where spices futures are more efficient than the rubber futures. Practical implications The study passes on vital information to the producers and traders of spices and rubber who have a potential interest in the use of futures contracts to make profits from arbitrage between futures and cash markets. Originality/value The paper is unique in terms of understanding asymmetric price linkages in markets for plantation crops.
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46

Agnihotri, Shalini, and Kanishk Chauhan. "Modeling tail risk in Indian commodity markets using conditional EVT-VaR and their relation to the stock market." Investment Management and Financial Innovations 19, no. 3 (July 7, 2022): 1–12. http://dx.doi.org/10.21511/imfi.19(3).2022.01.

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Investment in commodity markets in India accelerated after 2007; this was accompanied by large price variability, hence, it becomes imperative to measure commodity price risk precisely. It becomes equally important to study the relationship between commodity price variability and the stock market. Hence, this study aims to calculate the tail risk of highly traded Indian commodity futures returns using the conditional EVT-VaR method for risk measurement. Secondly, the linkage between commodity markets and the stock market is also studied using the Delta CoVaR method. Results highlight the following points. There is risk transfer from the extreme increase/decrease in crude oil futures returns to the Nifty Index returns. Both extreme price increase or decrease of crude oil futures driven either by financial or a combination of financial and economic shocks affect the stock market. Zinc and Natural gas futures are not linked to the stock market, which means they can be useful in portfolio diversification. The findings suggest that, in Indian commodity markets, EVT-VaR is a useful tool for measuring risk. Only Crude oil futures shocks affect the stock market, and extreme integration between them becomes more prominent when oil shocks are driven by financial factors. Commodities other than Crude oil are not integrated with stock markets in India.
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47

Sheng, Chunguang, Guangyu Wang, Yude Geng, and Lirong Chen. "The Correlation Analysis of Futures Pricing Mechanism in China’s Carbon Financial Market." Sustainability 12, no. 18 (September 7, 2020): 7317. http://dx.doi.org/10.3390/su12187317.

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China, taking the concept of sustainable development as the premise, puts forward Intended Nationally Determined Contributions (INDC) to reduce the greenhouse gas emissions in response to climate change. In this context, with the purpose of seeking solutions to a carbon financial market pricing mechanism to build China’s carbon finance market actively and thus achieving the goal of sustainable development, this paper, based on the autoregressive integrated moving average (ARIMA) model, established a carbon price prediction model for the carbon financial market, and studied the relationship between Certified Emission Reduction (CER) futures prices and spot prices, as well as the relationship between European Union allowances (EUA) futures prices and CER futures prices in an empirical manner. In this paper, EUA and CER futures prices of the European Climate Exchange (ECX) and EUA and CER spot prices of the BlueNext Environmental Exchange were selected as research objects. Granger causality test, co-integration test, and ECM were used to form a progressive econometric analysis framework. The results show that firstly, the ARIMA model can effectively predict carbon futures prices; secondly, CER futures prices cannot guide spot price, and the futures pricing function does not play a role in this market; thirdly, EUA futures price can, in the short term, effectively guide the trend of CER futures prices, with the futures pricing function between the two markets. In the long run, however, the future pricing function of the two markets is not obvious. Therefore, great differences among maturity of the two markets, degree of policy influence, and market share lead to the failure of long-run futures pricing functions.
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Garg, Mohit, Shelly Singhal, Kiran Sood, Ramona Rupeika-Apoga, and Simon Grima. "Price Discovery Mechanism and Volatility Spillover between National Agriculture Market and National Commodity and Derivatives Exchange: The Study of the Indian Agricultural Commodity Market." Journal of Risk and Financial Management 16, no. 2 (January 19, 2023): 62. http://dx.doi.org/10.3390/jrfm16020062.

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Agricultural commodity markets are critical to the global economy. This study investigates the price discovery mechanism, lead-lag relationship, and volatility spillover between spot prices on the National Agriculture Market (E-NAM) and futures and spot prices on the National Commodity and Derivative Exchange (NCDEX) in the Indian agricultural commodity market. The Johansen Cointegration, Vector Error Correction (VEC), Granger causality tests, and bivariate GARCH models were applied to daily data from April 2016 to December 2020 for twelve agricultural commodities traded on the E-NAM and NCDEX. We discovered the long-run relationship using the Johansen Cointegration test and concluded that the NCDEX spot and futures market is dominant in the price discovery mechanism, and the NCDEX futures and spot markets lead the E-NAM spot prices having a unidirectional or bidirectional relationship. Furthermore, the bivariate GARCH model suggested a volatility spillover from E-NAM spot prices to NCDEX futures and spot markets for most commodities, except for bajra, barley, and jeera, which have no volatility spillover. The study’s findings have important implications for various stakeholders, including policymakers, farmers, investors, traders, and others who want to reduce price risks by using information from the E-NAM market’s spot prices.
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Hong, Chung-Hyo. "An Empirical Study on the Asymmetric Effects of Trading Volume Information in int‘l Currency Futures Markets: Advanced vs Emerging Markets." Journal of Derivatives and Quantitative Studies 20, no. 2 (May 31, 2012): 237–64. http://dx.doi.org/10.1108/jdqs-02-2012-b0004.

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This paper tested the conditional mean spillover effects between trading volume and price changes in international currency futures markets. We use 8 kinds of currency futures markets such as Japanese yen, British pound, Australian and Canadian dollar as the advanced market and Korean Won/Dollar, Brazilian Real, Russian rubul and South African's land futures as the emerging markets. The sample period is covered from May 19, 2006 to March 15, 2009. For this purpose we employed dynamic time series model such as Nelson (1991)'s Exponential GARCH (1, 1)-M. The major empirical results are as follows; First, according to the empirical results of 4 advanced currency futures markets, we find that the open interests have a strong impact on the price changes at a statistically significant level. In case of the British pound and Canadian dollar futures, the price changes also have influence on the open interests. Second, according to the empirical results of 4 emerging currency futures markets, only Russian currency futures' open interest has an impact on the price change but the price changes of the remain 3 countries have an impact on open interests respectively at a significant level. Third, we also find that there is a asymmetric volatility spillover effects between open interests and price changes in all the advanced and emerging currency futures markets. Fourth, according to Granger causality test the influence of Japanese yen, Australian and Canadian dollar and Brazilian Real futures on the other currency futures markets are dominant. From these empirical results we infer that most of currency futures markets have a much better price discovery function than currency cash market and are inefficient to the information.
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Monteiro, Claudio, Ignacio J. Ramirez-Rosado, and L. Alfredo Fernandez-Jimenez. "A strategy for electricity buyers in futures markets." E3S Web of Conferences 152 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202015203007.

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This paper presents an original trading strategy for electricity buyers in futures markets. The strategy applies a medium-term electricity price forecasting model to predict the monthly average spot price which is used to evaluate the Risk Premium for a physical delivery under a monthly electricity futures contract. The proposed trading strategy aims to provide an advantage relatively to the traditional strategy of electricity buyers (used as benchmark), anticipating the good/wrong decision of buying electricity in the futures market instead in the day-ahead market. The mid-term monthly average spot price forecasting model, which supports the trading strategy, uses only information available from futures and spot markets at the decision moment. Both the new trading strategy and the monthly average spot price forecasting model, proposed in this paper, have been successfully tested with historical data of the Iberian Electricity Market (MIBEL), although they could be applied to other electricity markets.
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