Bibliographies thématiques / COMMODITY FUTURES MARKET

Littérature scientifique sur le sujet « COMMODITY FUTURES MARKET »

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Publié le 11 septembre 2023

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Articles de revues sur le sujet "COMMODITY FUTURES MARKET"

1

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

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2

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

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Kumar Mahalik, Mantu, Debashis Acharya et M. Suresh Babu. « Price discovery and volatility spillovers in futures and spot commodity markets ». Journal of Advances in Management Research 11, no 2 (29 juillet 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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Christoffersen, Peter, Asger Lunde et Kasper V. Olesen. « Factor Structure in Commodity Futures Return and Volatility ». Journal of Financial and Quantitative Analysis 54, no 3 (28 août 2018) : 1083–115. http://dx.doi.org/10.1017/s0022109018000765.

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We uncover stylized facts of commodity futures’ price and volatility dynamics in the post-financialization period and find a factor structure in daily commodity volatility that is much stronger than the factor structure in returns. The common factor in commodity volatility relates to stock market volatility as well as to the business cycle. Model-free realized commodity betas with the stock market were high during 2008–2010 but have since returned to the pre-crisis level, close to 0. While commodity markets appear segmented from the equity market when considering only returns, commodity volatility indicates a nontrivial degree of market integration.
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Kristoufek, Ladislav, et Miloslav Vosvrda. « Commodity futures and market efficiency ». Energy Economics 42 (mars 2014) : 50–57. http://dx.doi.org/10.1016/j.eneco.2013.12.001.

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Bhagwat, Shree, et Angad Singh Maravi. « THE ROLE OF FORWARD MARKETS COMMISSION IN INDIAN COMMODITY MARKETS ». International Journal of Research -GRANTHAALAYAH 3, no 11 (30 novembre 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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Dubey, Priti, et Rishika Shankar. « Determinants of the Commodity Futures Market Performance : An Indian Perspective ». South Asia Economic Journal 21, no 2 (septembre 2020) : 239–57. http://dx.doi.org/10.1177/1391561420970837.

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This article aims to find out interlinkages between equity and commodity markets through the channel of investors’ outlook in the equity market. The proxies used for gauging perception of investors are investor sentiment index and Advance–Decline ratio. The study also incorporates the introduction of Commodity Transaction Tax (CTT) and occurrence of National Spot Exchange Limited (NSEL) scam in the year 2013. Additionally, returns in commodity market are examined to be a function of equity returns. The empirical findings suggest that the liquidity of commodity futures is inversely related to investor sentiments in equity market, and commodity returns are also negatively related to equity returns. Therefore, equity and commodity markets are inversely related, as liquidity in both the markets reacts to the investor sentiments; contrarily, commodity returns experience a significantly negative impact from equity returns. Additionally, the results also provide evidence that investor sentiment in equity possesses the ability to predict liquidity in the commodity futures market. The study also suggests that the CTT and NSEL scam have significantly and positively affected the liquidity of the Indian commodity market.
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Ranganathan, Thiagu, et Usha Ananthakumar. « Market efficiency in Indian soybean futures markets ». International Journal of Emerging Markets 9, no 4 (9 septembre 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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9

R L, Manogna, et 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 (23 mai 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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Agnihotri, Shalini, et 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 (7 juillet 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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Thèses sur le sujet "COMMODITY FUTURES MARKET"

1

Fan, Hua (John). « Momentum Investing in Commodity Futures ». Thesis, Griffith University, 2014. http://hdl.handle.net/10072/365723.

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Momentum, the tendency of recent winner stocks to continue to rise and recent loser stocks to continue to fall, is one of the most puzzling asset pricing anomalies in modern finance. The recent boom in commodity futures investments has sparked renewed interest from both academia and industry in momentum investment strategies. This thesis proposes and examines the performance of three novel momentum-based active investment strategies in commodity futures. Conventional momentum strategies rely on 12 months of past returns for the formation of investment portfolios. First, this thesis proposes a more granular strategy termed 'microscopic momentum‘, which decomposes conventional momentum into single-month momentum components. The novel decomposition reveals that a microscopic momentum strategy generates persistent economic profits even after controlling for sector-specific or month-of-year commodity seasonality effects. Furthermore, we find that all 12 months of past returns play an important role in determining the conventional momentum profits. Second, for the first time in the literature, we document a consistent reversal pattern in commodity momentum profits. Combining the observed reversal pattern with the momentum signal, the strategy in the second study significantly outperforms conventional strategies. The profitability of the proposed strategy cannot be explained by standard asset pricing risk factors, market volatility, investors‘ sentiment, data- mining or transaction costs, but appears to be related to global funding liquidity. Furthermore, the proposed investment strategy in commodity futures may be employed as a portfolio diversification tool.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith Business School
Griffith Business School
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2

Gurrib, Muhammad Ikhlaas. « Behaviour and performance of key market players in the US futures markets ». Thesis, Curtin University, 2008. http://hdl.handle.net/20.500.11937/1287.

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This study gives an insight into the behaviour and performance of large speculators and large hedgers in 29 US futures markets. Using a trading determinant model and priced risk factors such as net positions and sentiment index, results suggest hedgers (speculators) exhibit significant positive feedback trading in 15 (7) markets. Information variables like the S&P500 index dividend yield, corporate yield spread and the three months treasury bill rate were mostly unimportant in large players’ trading decisions. Hedgers had better market timing abilities than speculators in judging the direction of the market in one month. The poor market timing abilities and poor significance of positive feedback results suggest higher trading frequency intervals for speculators. Hedging pressures, which measure the presence of risk premium in futures markets, were insignificant mostly in agricultural markets. As a robust test of hedging pressures, price pressure tests found risk premium to be still significant for silver, crude oil and live cattle. The positive feedback behaviour and negative market timing abilities suggest hedgers in heating oil and Japanese yen destabilize futures prices, and points to a need to check CFTC’s (Commodity Futures Trading Commission) position limits regulation in these markets. In fact, large hedgers in these two markets are more likely to be leading behaviour, in that they have more absolute net positions than speculators. Alternatively stated, positive feedback hedgers in these two markets are more likely to lead institutions and investors to buy (sell) overpriced (underpriced) contracts, eventually leading to divergence of prices away from fundamentals.Atlhought hedgers in crude oil had significant positive feedback behaviour and negative market timing skills, they would not have much of a destabilizing effect over remaining players because the mean net positions of hedgers and speculators were not far apart. While the results are statistically significant, it is suggested these could be economically significant, in that there have been no regulation on position limits at all for hedgers compared to speculators who are imposed with strict limits from the CFTC. Further, mean equations were regressed against decomposed variables, to see how much of the futures returns are attributed to expected components of variables such as net positions, sentiment and information variables. While the expected components of variables are derived by ensuring there are enough ARMA (autoregressive and moving average) terms to make them statistically and economically reliable, the unexpected components of variables measure the residual on differences of the series from its mean. When decomposing net positions against returns, it was found expected net positions to be negatively related to hedgers’ returns in mostly agricultural markets. Speculators’ expected (unexpected) positions were less (more) significant in explaining actual returns, suggesting hedgers are more prone in setting an expected net position at the start of the trading month to determine actual returns rather than readjusting their net positions frequently all throughout the remaining days of the month. While it important to see how futures returns are determined by expected and unexpected values, it is also essential to see how volatility is affected as well.In an attempt to cover three broad types of volatility measures, idiosyncratic volatility, GARCH based volatility (variance based), and PARCH based volatility (standard deviation) are used. Net positions of hedgers (expected and unexpected) tend to have less effect on idiosyncratic volatility than speculators that tended to add to volatility, reinforcing that hedgers trading activity hardly affect the volatility in their returns. This suggest they are better informed by having a better control over their risk (volatility) measures. The GARCH model showed more reliance of news of volatility from previous month in speculators’ volatility. Hedgers’ and speculators’ volatility had a tendency to decay over time except for hedgers’ volatility in Treasury bonds and coffee, and gold and S&P500 for speculators’ volatility. The PARCH model exhibited more negative components in explaining current volatility. Only in crude oil, heating oil and wheat (Chicago) were idiosyncratic volatility positively related to return, reinforcing the suggestion for stringent regulation in the heating oil market. Expected idiosyncratic volatility was lower (higher) for hedgers (speculators) as expected under portfolio theory. Markets where variance or standard deviation are smaller than those of speculators support the price insurance theory where hedging enables traders to insure against the risk of price fluctuations. Where variance or standard deviation of hedgers is greater than speculators, this suggest the motivation to use futures contracts not primarily to reduce risk, but by institutional characteristics of the futures exchanges like regulation ensuring liquidity.Results were also supportive that there was higher fluctuations in currency and financial markets due to the higher number of contracts traded and players present. Further, the four models (GARCH normal, GARCH t, PARCH normal and PARCH t) showed returns were leptokurtic. The PARCH model, under normal distribution, produced the best forecast of one-month return in ten markets. Standard deviation and variance for both hedgers’ and speculators’ results were mixed, explained by a desire to reduce risk or other institutional characteristics like regulation ensuring liquidity. Moreover, idiosyncratic volatility failed to accurately forecast the risk (standard deviation or variance based) that provided a good forecast of one-month return. This supports not only the superiority of ARCH based models over models that assume equally weighted average of past squared residuals, but also the presence of time varying volatility in futures prices time series. The last section of the study involved a stability and events analysis, using recursive estimation methods. The trading determinant model, mean equation model , return and risk model, trading activity model and volatility models were all found to be stable following the effect of major global economic events of the 1990s. Models with risk being proxied as standard deviation showed more structural breaks than where variance was used. Overall, major macroeconomic events didn’t have any significant effect upon the large hedgers’ and speculators’ behaviour and performance over the last decade.
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Tang, Weiqing. « Global commodity futures market modelling and statistical inference ». Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8661/.

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This thesis first investigates the asset pricing ability of a new risk factor, namely Risk-Neutral Skewness (estimated based on option data) in the global commodity futures market. Skewness trading behaviour in the option market is attributed to heterogeneous belief and selective hedging concern. The negative (positive) the Risk-Neutral Skewness is accompanied with excess trading on put (call) option contracts, which leads to underlings' over-pricing (under-pricing). Above results are robust to time-series and cross-sectional test and other alternatives. Secondly, a new functional mean change detection procedure is proposed via the Kolmogorov-Smirnov functional form. Simulations indicate decent testing power under the alternative. An empirical test procedure is deployed for crude oil and gold futures price term structure, showing real market data change. The multivariate forecasting regression analysis uncovers trading behaviours behind the real-world change occurrence. Lastly, the futures basis term structure is forecasted under the framework of the functional autoregressive predictive factor model with lag 1. By comparison, the new method outperforms other functional and non-functional methods, with maturities less than 10 months. The Model Confidence Set method statistically validate this result. A new variance minimization trading strategy is proposed and tested when the future futures basis is forecast and known.
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4

Wang, Ying. « Essays on Risk Management for Agricultural Commodity Futures Market ». The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461192690.

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5

Gurrib, Muhammad Ikhlaas. « Behaviour and performance of key market players in the US futures markets ». Curtin University of Technology, School of Economics and Finance, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=117995.

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This study gives an insight into the behaviour and performance of large speculators and large hedgers in 29 US futures markets. Using a trading determinant model and priced risk factors such as net positions and sentiment index, results suggest hedgers (speculators) exhibit significant positive feedback trading in 15 (7) markets. Information variables like the S&P500 index dividend yield, corporate yield spread and the three months treasury bill rate were mostly unimportant in large players’ trading decisions. Hedgers had better market timing abilities than speculators in judging the direction of the market in one month. The poor market timing abilities and poor significance of positive feedback results suggest higher trading frequency intervals for speculators. Hedging pressures, which measure the presence of risk premium in futures markets, were insignificant mostly in agricultural markets. As a robust test of hedging pressures, price pressure tests found risk premium to be still significant for silver, crude oil and live cattle. The positive feedback behaviour and negative market timing abilities suggest hedgers in heating oil and Japanese yen destabilize futures prices, and points to a need to check CFTC’s (Commodity Futures Trading Commission) position limits regulation in these markets. In fact, large hedgers in these two markets are more likely to be leading behaviour, in that they have more absolute net positions than speculators. Alternatively stated, positive feedback hedgers in these two markets are more likely to lead institutions and investors to buy (sell) overpriced (underpriced) contracts, eventually leading to divergence of prices away from fundamentals.
Atlhought hedgers in crude oil had significant positive feedback behaviour and negative market timing skills, they would not have much of a destabilizing effect over remaining players because the mean net positions of hedgers and speculators were not far apart. While the results are statistically significant, it is suggested these could be economically significant, in that there have been no regulation on position limits at all for hedgers compared to speculators who are imposed with strict limits from the CFTC. Further, mean equations were regressed against decomposed variables, to see how much of the futures returns are attributed to expected components of variables such as net positions, sentiment and information variables. While the expected components of variables are derived by ensuring there are enough ARMA (autoregressive and moving average) terms to make them statistically and economically reliable, the unexpected components of variables measure the residual on differences of the series from its mean. When decomposing net positions against returns, it was found expected net positions to be negatively related to hedgers’ returns in mostly agricultural markets. Speculators’ expected (unexpected) positions were less (more) significant in explaining actual returns, suggesting hedgers are more prone in setting an expected net position at the start of the trading month to determine actual returns rather than readjusting their net positions frequently all throughout the remaining days of the month. While it important to see how futures returns are determined by expected and unexpected values, it is also essential to see how volatility is affected as well.
In an attempt to cover three broad types of volatility measures, idiosyncratic volatility, GARCH based volatility (variance based), and PARCH based volatility (standard deviation) are used. Net positions of hedgers (expected and unexpected) tend to have less effect on idiosyncratic volatility than speculators that tended to add to volatility, reinforcing that hedgers trading activity hardly affect the volatility in their returns. This suggest they are better informed by having a better control over their risk (volatility) measures. The GARCH model showed more reliance of news of volatility from previous month in speculators’ volatility. Hedgers’ and speculators’ volatility had a tendency to decay over time except for hedgers’ volatility in Treasury bonds and coffee, and gold and S&P500 for speculators’ volatility. The PARCH model exhibited more negative components in explaining current volatility. Only in crude oil, heating oil and wheat (Chicago) were idiosyncratic volatility positively related to return, reinforcing the suggestion for stringent regulation in the heating oil market. Expected idiosyncratic volatility was lower (higher) for hedgers (speculators) as expected under portfolio theory. Markets where variance or standard deviation are smaller than those of speculators support the price insurance theory where hedging enables traders to insure against the risk of price fluctuations. Where variance or standard deviation of hedgers is greater than speculators, this suggest the motivation to use futures contracts not primarily to reduce risk, but by institutional characteristics of the futures exchanges like regulation ensuring liquidity.
Results were also supportive that there was higher fluctuations in currency and financial markets due to the higher number of contracts traded and players present. Further, the four models (GARCH normal, GARCH t, PARCH normal and PARCH t) showed returns were leptokurtic. The PARCH model, under normal distribution, produced the best forecast of one-month return in ten markets. Standard deviation and variance for both hedgers’ and speculators’ results were mixed, explained by a desire to reduce risk or other institutional characteristics like regulation ensuring liquidity. Moreover, idiosyncratic volatility failed to accurately forecast the risk (standard deviation or variance based) that provided a good forecast of one-month return. This supports not only the superiority of ARCH based models over models that assume equally weighted average of past squared residuals, but also the presence of time varying volatility in futures prices time series. The last section of the study involved a stability and events analysis, using recursive estimation methods. The trading determinant model, mean equation model , return and risk model, trading activity model and volatility models were all found to be stable following the effect of major global economic events of the 1990s. Models with risk being proxied as standard deviation showed more structural breaks than where variance was used. Overall, major macroeconomic events didn’t have any significant effect upon the large hedgers’ and speculators’ behaviour and performance over the last decade.
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6

Howell, James Andreas. « An analysis of speculator behavior and the dynamics of price in a futures market ». Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/24847.

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7

Dai, Jingyu. « Testing Overreaction and Under-reaction in the Commodity Futures Market ». Thesis, Singapore Management University (Singapore), 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1548068.

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Results from previous studies testing for under-reaction and overreaction in the commodity futures market are mixed and inconclusive. Using a data of more than 20 categories of future contacts ranging from agricultural, metal and energy, we have found significant evidence of under-reaction in food and agricultural commodities but not in the energy and metal sector. It is also found that those relatively inactive commodity future contracts tend to have a stronger tendency to under-react than commodity future contracts are very actively traded. The result also agrees with the behavioral hypothesis that under-reaction is caused by gradual incorporation of information among investors.

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8

Kim, Sang Hyo. « Analysis of Agricultural Commodity Storage Using Futures and Options Market ». The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436958589.

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9

Brunetti, Celso. « Comovement and volatility in international asset markets ». Thesis, Queen Mary, University of London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322235.

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10

Goetz, Cole Louis. « The Effects of Futures Markets on the Spot Price Volatility of Storable Commodities ». Thesis, North Dakota State University, 2019. https://hdl.handle.net/10365/29795.

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This thesis examines the relationship between spot prices, futures prices, and ending stocks for storable commodities. We used Granger causality and DAGs to determine causal relationships and cointegration tests to determine long-run relationships. We use VAR/VECM and consider innovation accounting techniques to see how volatility in one market affects the price behavior and volatility in the other market. Results suggest that for agricultural commodities, innovations in futures price permanently increase the level of spot prices while accounting for much of spot price variance over time. For national oil, shocks to futures price decrease the level of spot price in the long run. In regional oil markets, there are transitory impulse responses. Futures price plays a small role in the volatility of spot prices for oil over time. Overall results are mixed, with oil suggesting futures markets may have a price stabilizing effect and agriculture commodities indicating spot price destabilization.
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Livres sur le sujet "COMMODITY FUTURES MARKET"

1

Kevin, Koy, dir. Markets and market logic. Chicago : Porcupine Press, 1986.

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Commodity market fundamentals. Upper Saddle River, N.J : FTPress Delivers, 2011.

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3

Dalton, James F. Mind over markets : Power trading with market generated information. London : McGraw-Hill, 1990.

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4

Dalton, James F. Mind over markets : Power trading with market generated information. Chicago, Ill : Probus, 1990.

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5

Dalton, James F. Mind over markets : Power trading with market generated information. London : McGraw-Hill Book Co., 1990.

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6

Sofyan, Hanafi. Perdagangan berjangka dan ekonomi Indonesia. Jakarta : Elex Media Komputindo, 2000.

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1971-, Kurnia Ahmad Doli, et Syafi'i Abdullah 1971-, dir. Perdagangan berjangka komoditi Indonesia : Relevansinya dengan kon[s]truksi nilai etika dalam pasar bebas dan pertumbuhan nilai ekonomi bangsa. [Jakarta] : HMI Publisher, 1999.

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8

Duffie, Darrell. Futures markets. EnglewoodCliffs : Prentice-Hall, 1989.

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9

Futures markets. Englewood Cliffs, N.J : Prentice Hall, 1989.

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George, Angell, dir. Winning in the futures market. 2e éd. Garden City, N.Y : Doubleday, 1987.

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Chapitres de livres sur le sujet "COMMODITY FUTURES MARKET"

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Daloz, Jean Pierre. « The producer and futures markets ». Dans International Commodity Market Models, 253–62. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3084-4_13.

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Rausser, Gordon C., et Nicholas Walraven. « Dynamic welfare analysis and commodity futures markets overshooting ». Dans International Commodity Market Models, 211–32. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3084-4_11.

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Lowry, Mark Newton. « Futures prices and hidden stocks of refined oil products ». Dans International Commodity Market Models, 263–73. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3084-4_14.

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Artus, Patrick. « When does the creation of a futures market destabilize spot prices ? » Dans International Commodity Market Models, 233–52. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3084-4_12.

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Hallett, Andrew Hughes, et Prathap Ramanujam. « Market Solutions to the Problem of Stabilizing Commodity Earnings ». Dans Commodity, Futures and Financial Markets, 1–34. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3354-8_1.

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Luo, Guo Ying. « Evolution and Informationally Efficient Equilibrium in a Commodity Futures Market ». Dans Studies in Economic Theory, 61–88. New York, NY : Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0712-6_4.

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Kumar, Raushan, Nand Kumar, Aynalem Shita et Sanjay Kumar Pandey. « Lead–Lag Relationship Between Spot and Futures Prices of Indian Agri Commodity Market ». Dans Lecture Notes in Mechanical Engineering, 339–48. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8542-5_29.

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Vaitonis, Mantas, et Saulius Masteika. « Statistical Arbitrage Trading Strategy in Commodity Futures Market with the Use of Nanoseconds Historical Data ». Dans Communications in Computer and Information Science, 303–13. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67642-5_25.

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Lerkeitthamrong, Khunanont, Chatchai Khiewngamdee et Rossarin Osathanunkul. « Impacts of Global Market Volatility and US Dollar on Agricultural Commodity Futures Prices : A Panel Cointegration Approach ». Dans Structural Changes and their Econometric Modeling, 412–22. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04263-9_32.

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Geisst, Charles R. « Commodity Futures Markets ». Dans A Guide to the Financial Markets, 90–106. London : Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-20348-2_5.

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Actes de conférences sur le sujet "COMMODITY FUTURES MARKET"

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Tan, Li, Qi Zhong-ying, Sui Xue-shen et Lei Ying. « Heterogeneous Agent Beliefs and Clustered Volatility in Commodity Futures Market ». Dans The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/ipc.2007.18.

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Jain, Neeti, et Niti Nandini Chatnani. « Financialization – Evidence from Dynamic Connectedness among Agricultural Index Futures ». Dans 8th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2022. http://dx.doi.org/10.31410/eraz.s.p.2022.35.

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The introduction of index futures was a landmark event for glob­al commodity markets. It has been blamed by regulators and academicians for its role in food price surges from time to time. This paper examines the price discovery and volatility spillover relationship among agricultural in­dex futures globally. Results from the study reveal that index futures play a dominant role in contributing to price discovery. The price leadership of the futures market, although found to be strong, is diminished in the presence of stringent regulatory trading curbs that were put in place as a response to the crisis. Furthermore, an improved Diebold & Yilmaz method based on TVP-VAR-SV model was used to analyze dynamic connectedness between the index and standalone contracts of agriculture commodity markets. The results show that the impacts on the net spillover of various indices are different. Howev­er, the evidence fails to support the argument that volatility is induced due to spillovers among the indices.
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Huang, Ke, Jifeng Sun, Zuominyang Zhang, Ying Ye et Wenjian Hou. « Dynamic network of commodity futures market and systemic risk contribution of key commodities ». Dans 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020485.

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Fu, Zeng-yu, et Hu-wei Wen. « Measure and Manage the Dynamic risk of Commodity Futures Market Based on CAViaR ». Dans 2016 International Conference on Engineering Management (Iconf-EM 2016). Paris, France : Atlantis Press, 2016. http://dx.doi.org/10.2991/iconfem-16.2016.7.

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SWITZER, LORNE N., et HUI JIANG. « MARKET EFFICIENCY AND THE RISKS AND RETURNS OF DYNAMIC TRADING STRATEGIES WITH COMMODITY FUTURES ». Dans First Interdisciplinary Chess Interactions Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814295895_0008.

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Yang, Zijian. « Stability of Spot price and Futures market for Agricultural commodity : based on the sugar products ». Dans 2016 International Forum on Management, Education and Information Technology Application. Paris, France : Atlantis Press, 2016. http://dx.doi.org/10.2991/ifmeita-16.2016.96.

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Staugaitis, Algirdas Justinas. « Financial speculation impact on agricultural commodity price volatility : TGARCH approach ». Dans 21st International Scientific Conference "Economic Science for Rural Development 2020". Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2020. http://dx.doi.org/10.22616/esrd.2020.53.014.

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Motivated by agricultural commodity price fluctuations and spikes in the last decade, we investigate whether financial speculation destabilizes the price of agricultural commodities. The aim of this research is to assess the impact of financial speculation on agricultural commodity price volatility. In our study we use weekly returns on wheat, soybean and corn futures from Chicago Mercantile of Exchange. To measure this impact, we apply autoregressive conditional heteroskedasticity (ARCH) technique. We also propose a model with seasonal dummy variables to measure if financial speculation impact on price volatility differs among seasons. The results of our research indicate that financial speculation as an exogenous factor has either no effect or reduces the volatility of the underlying futures prices. Therefore, we conclude that the increase of non-commercial market participants does not make the agricultural commodity prices more volatile or this link is at least questionable.
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Darden, Thaddeus A., Margaret E. Ferrenz, Christopher C. Klann, Michael J. Ledwith, Mark E. Paddrik et Ginger M. Davis. « Modified momentum strategies in commodity futures markets ». Dans 2009 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2009. http://dx.doi.org/10.1109/sieds.2009.5166181.

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Grossmann, Vasco, et Manfred Schimmler. « Portfolio-based contract selection in commodity futures markets ». Dans 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850018.

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Shen, Li, Kun Shen, Chao Yi et Yixin Chen. « An Evaluation of Pairs Trading in Commodity Futures Markets ». Dans 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377766.

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Rapports d'organisations sur le sujet "COMMODITY FUTURES MARKET"

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Considine, Jennifer, Philip Galkin et Abdullah Aldayel. Global Crude Oil Storage Index : A New Benchmark for Energy Policy. King Abdullah Petroleum Studies and Research Center, septembre 2022. http://dx.doi.org/10.30573/ks--2022-mp01.

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The global oil market dwarfs other commodity markets. Its size and role in the energy and industrial value chains underscore its significant economic and geopolitical impacts. Thus, the consequences of oil price fluctuations extend far beyond the oil industry and can be viewed as a barometer of trends in the global economy. Several oil price benchmarks currently compete in the global market. The most popular ones, such as Brent or West Texas Intermediate (WTI), are backed by a sufficient supply of the underlying crude. They also meet the criteria for efficient trading, hedging and speculating — including having sufficient liquidity, developed futures markets, low transaction costs and strong institutional support.
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Breman, Carlotta, et Servaas Storm. Betting on black gold : Oil speculation and U.S. inflation (2020-2022). Institute for New Economic Thinking Working Paper Series, juin 2023. http://dx.doi.org/10.36687/inetwp208.

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Sharp increases in systemically important crude oil prices have been a major cause of the recent surge in the inflation rate in the U.S. This paper investigates the extent to which the increase in oil prices can be attributed to excessive speculation in the oil futures market. Our analysis suggests that excessive speculation in the crude oil market has been responsible for 24%-48% of the increase in the WTI crude oil price during October 2020-June 2022. These estimates translate into an oil price increase of around $18-$36 per barrel and an increase in the U.S. PCE inflation rate by circa 0.75 to 1.5 percentage points during the same period. We complement the analysis with an empirical investigation of the crude oil market which shows that (speculative) long non-commercial open-interest positions in oil futures have increased considerably relative to short non-commercial positions. We further find that higher futures prices for crude oil ‘Granger-cause’ oil spot prices, the futures prices of corn and soybeans and the fertilizer price. These econometric results show that oil speculators have to be held accountable for not just raising oil prices, but also driving up food commodity prices. We finally discuss measures to clamp down on excessive speculation in oil in order to eliminate its systemically adverse consequences for the U.S. economy.
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Cheng, Ing-Haw, Andrei Kirilenko et Wei Xiong. Convective Risk Flows in Commodity Futures Markets. Cambridge, MA : National Bureau of Economic Research, mars 2012. http://dx.doi.org/10.3386/w17921.

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Rouwenhorst, K. Geert. A Tale of Two Premiums : The Role of Hedgers and Speculators in Commodity Futures Markets. American Finance Association, septembre 2021. http://dx.doi.org/10.37214/jofdata.3.

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Ludena, Carlos, Thomas Hertel, Paul Preckel, Kenneth Foster et Alejandro Nin-Pratt. Productivity Growth and Convergence in Crop, Ruminant and Non-Ruminant Production : Measurement and Forecasts. GTAP Working Paper, novembre 2006. http://dx.doi.org/10.21642/gtap.wp35.

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There is considerable interest in projections of future productivity growth in agriculture. Whether one is interested in the outlook for global commodity markets, future patterns of international trade, or the interactions between land use, deforestation and ecological diversity, the rate of productivity growth in agriculture is an essential input. Yet solid projections for this variable have proven elusive – particularly on a global basis. This is due, in no small part, to the difficulty in measuring historical productivity growth. The purpose of this paper is to report the latest time series evidence on total factor productivity growth for crops, ruminants and non-ruminant livestock, on a global basis. We then follow with tests for convergence amongst regions, providing forecasts for farm productivity growth to the year 2040. The results suggest that most regions in the sample are likely to experience larger productivity gains in livestock than in crops. Within livestock, the non-ruminant sector is expected to continue to be more dynamic than the ruminant sector. Given the rapid rates of productivity growth observed recently, non-ruminant and crop productivity in developing countries may be converging to the productivity levels of developed countries. For ruminants, the results show that productivity levels may be diverging between developed and developing countries.
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Trapani, Paola. Collaborative Housing as a Response to the Housing Crisis in Auckland. Unitec ePress, juillet 2018. http://dx.doi.org/10.34074/ocds.0821.

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According to future projections based on current demographic growth trends, Auckland’s population will reach two million in 2033. Since the city is already afflicted by a serious housing crisis, at the beginning of 2017 the newly elected Mayor Phil Goff set up a task force. Formed by representatives of various stakeholders, it was given the task of producing a report with strategic and tactical guidelines to mitigate the situation. Unitec researchers were invited to respond to the report, which came out at the end of 2017, in the form of three think pieces towards the Building Better Homes, Towns and Cities National Science Challenge. This paper is a new iteration of one of these think pieces, focused on collaborative living, and expands on the new role that designers should play in this field. Its ideological position is that the house cannot and should not be considered as a commodity on the free market; nor should focus solely be on bringing down prices by increasing the number of houses on offer. Over time, housing might evolve to being more about social (use) value than exchange value. Other models of the production and consumption of household goods are documented throughout the world as alternatives to mainstream market logic, using collective procurement mechanisms to cut construction and marketing costs with savings of up to 30%. These experiments, not limited to achieving financially sustainable outcomes, are linked to new social practices of collaboration between neighbours. The sharing of spaces and equipment to complement private housing units also leads to social and environmental sustainability.
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Hertel, Thomas, Wally Tyner et Dileep Birur. Biofuels for all ? Understanding the Global Impacts of Multinational Mandates. GTAP Working Paper, avril 2008. http://dx.doi.org/10.21642/gtap.wp51.

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The recent rise in world oil prices, coupled with heightened interest in the abatement of greenhouse gas emissions, has led to a sharp increase in domestic biofuels production around the world. Previous authors have devoted considerable attention to the impacts of these policies on a country-by-country basis. However, there are also strong interactions among these programs, as they compete in world markets for feedstocks and ultimately for a limited supply of global land. In this paper, we evaluate the interplay between two of the largest biofuels programs, namely the renewable fuel mandates in the US and the EU. We examine how the presence of each of these programs influences the other, and also how their combined impact influences global markets and land use around the world. We begin with an analysis of the origins of the recent bio-fuel boom, using the historical period from 2001-2006 for purposes of model validation. This was a period of rapidly rising oil prices, increased subsidies in the EU, and, in the US, there was a ban on the major competitor to ethanol for gasoline additives. Our analysis of this historical period permits us to evaluate the relative contribution of each of these factors to the global biofuel boom. We also use this historical simulation to establish a 2006 benchmark biofuel economy from which we conduct our analysis of future mandates. Our prospective analysis of the impacts of the biofuels boom on commodity markets focuses on the 2006-2015 time period, during which existing investments and new mandates in the US and EU are expected to substantially increase the share of agricultural products (e.g., corn in the US, oilseeds in the EU, and sugar in Brazil) utilized by the biofuels sector. In the US, this share could more than double from 2006 levels, while the share of oilseeds going to biodiesel in the EU could triple.
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Zholdayakova, Saule, Yerdaulet Abuov, Daulet Zhakupov, Botakoz Suleimenova et Alisa Kim. Toward a Hydrogen Economy in Kazakhstan. Asian Development Bank Institute, octobre 2022. http://dx.doi.org/10.56506/iwlu3832.

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The energy transition is driving governments and industries to adopt various measures to reduce their climate impacts while maintaining the stability of their economy. Hydrogen technologies are one of the central topics in the energy transition. Different nations have different stances on it. Some governments see hydrogen as a decarbonization tool or part of their energy security strategy, while some others see it as a potential export commodity. While identifying priorities for the future, Kazakhstan should clearly define the role of hydrogen in the country’s long-term energy and decarbonization strategy. This work presents the first country-scale assessment of hydrogen technologies in Kazakhstan by focusing on policy, technology and economy aspects. A preliminary analysis has shown that Kazakhstan should approach hydrogen mainly as a part of its long-term decarbonization strategy. While coping with the financial risks of launching a hydrogen economy, the country can benefit from the export potential of low-carbon hydrogen in the near term. The export potential of low-carbon hydrogen in Kazakhstan is justified by its proximity to the largest hydrogen markets, huge resource base, and potentially low cost of production (in the case of blue hydrogen). Technology options for hydrogen transportation and storage for Kazakhstan are discussed in our work. The paper also identifies target hydrogen utilization areas in emission sectors regulated by Kazakhstan’s Emissions Trading System.
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The Oil Industry Challenges and Strategic Responses. Universidad de Deusto, 2018. http://dx.doi.org/10.18543/fwgz8427.

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Oil and gas prices and uncertainty in the main global markets, are likely to have a profound effect on the decisions made by O&G companies regarding exploration, appraisal development and operations. In addition to commodity prices, there has been increasing volatility in the relationships between industry, government policy makers and communities. Hence, the general object of this study will consist of analyzing the evolution of the industry within the new landscape and assess the challenges and strategies in response to them that the O&G industry must have to face in the coming years. In addition, this is complemented by a description of the value chain operations and market aspects as support and comprehension facilitator In summary, this document presents the in-depth strategic-focused conclusions that can be made from critically reviewing the current value chain. In this document, Chapter 2 first analyzes the new landscape and challenges that O&G companies are facing in respect to the four subject areas that has been considered to conform the new landscape: climate change policies and challenges, social concerns and new market trends, technological developments and applications, and regulations. Within each of these categories, a number of key developments and trends have been defined and described, along with the multiple challenges and decisions that industry players shall face. The dynamics of demand and supply are discussed in Chapter 3, along with the future uncertainties and factors that will have a profound effect on this balance. Within this chapter, the evolution of investments in E&P is also discussed, leading on to aspects of investments with regards to refining, and subsequently portfolio management. As a kind of conclusion, Chapter 4 pairs the new landscape issues identified in Chapter 2, with seven general challenges and related strategies for the industry. Furthermore, a second level of challenge and response granularity has been identified, which companies shall address in order to remain competitive in the new era of O&G industry. These two chapters, which deal with the strategic responses and business models, should be read jointly, as they try to look at the current situation - and future perspectives of the O&G industry, and how industry players may respond with different strategies, be they of a general or a more specific nature.
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