Indian Commodity Market Research Articles

Exploring the Effectiveness of Hedging in the Indian Commodity Market: A Comparative Analysis of Constant and Dynamic Hedge Ratios Across Agricultural and Non-Agricultural Commodities

Exploring the Effectiveness of Hedging in the Indian Commodity Market: A Comparative Analysis of Constant and Dynamic Hedge Ratios Across Agricultural and Non-Agricultural Commodities

RELATIONSHIP BETWEEN COMMODITIES MARKET AND STOCK MARKETS EVIDENCE FROM MALAYSIA AND CHINA

The stock-commodity link affects market participants, commodity producers, policymakers, and, in emerging nations, the entire economy. The lead-lag relationship between market representative indices can be studied using many methodologies. This initiative examines the Indian stock-commodity market interaction. This project will leverage secondary data from the National Stock Exchange, Multi Commodity Exchange of India Limited, and Bombay Stock Exchange websites. Project results can be used to create an investment plan. Thus, market participants must draw crucial inferences or discover important relationships between the Indian Stock Market and Commodity Market. Statement of the Problem Market players and investors face difficult portfolio decisions. People struggle to choose stocks and commodities like gold, copper, etc. Due to a lack of credible market data on the Indian Stock Market-Commodity Market connection. The lack of research on the Indian stock-commodity market link is the issue. Such a study is needed, using some of the top marketplace contributors as examples.The project's results should help regulators and market participants understand the problem and adopt appropriate laws, rules, policies, and methods. Investors and individuals have confirmed the issue. .Objectives of the Study The following objectives are the focus of the project study. · To make important inferences about the way the Indian Stock Market and Commodity Market interact; · to ascertain the type, scope, and direction of the link between the Indian Commodity Market and the Indian Stock Market.

Volatility Spillover in the Indian Commodity Market : A Comparative Assessment of Various Commodity Segments

Volatility Spillover in the Indian Commodity Market : A Comparative Assessment of Various Commodity Segments

Natural Gas and Crude: Evidence of non linear co-integration from Indian Commodities Market

Natural Gas and Crude: Evidence of non linear co-integration from Indian Commodities Market

Natural gas and crude: evidence of nonlinear co-integration from Indian commodities market

Natural gas and crude: evidence of nonlinear co-integration from Indian commodities market

Presence of Calendar Anomalies in Indian Commodity Markets: Empirical Evidence on Nkrishi and MCX-Agri

Presence of Calendar Anomalies in Indian Commodity Markets: Empirical Evidence on Nkrishi and MCX-Agri

Does Seasonality and Volatility Affect the Price Discovery of Agricultural Commodities? A Systematic Literature Review Paper on the Indian Commodity Market

This study examines the volatility and seasonality influence on the price discovery of agricultural commodities. The present study intends to review price volatility that has been extensively researched over the last decade. The Web of Science is chosen to extract the data. The PRISMA 2020 flowchart is an eligibility criterion used in the data selection and rejection procedure. Following a thorough evaluation, the final fifty-four studies have been chosen for the review. The data classification is mainly discussing price discovery of agricultural commodities, volatility spillover, and its impact on the agricultural commodity market. Findings of the study signifies agri commodities like corn and soybean’s future marketplace is more competent in price discovery of agri commodities compared to the cash market.

Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH‐LSTM based Approach

SummaryThis study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)‐type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH‐type models into an ensemble learning‐based hybrid long short‐term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH‐type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET‐LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed‐rank tests.

Financialisation of Commodity Markets: Evidence from India

The known circumstances that favour financialisation of commodity markets which result in unidirectional co-movement of equity and commodity indices are either weak or non-existent in India. Yet, after 2015, there has been a greater correlation between equity and commodity markets even when decoupling is observed in global markets. Results from the rolling regression attest to the shift in response of commodity and equity indices to wholesale price inflation (WPI) and call rate after 2015, indicating that post 2015 co-movement could have been a result of inflation targeting regime. The linear regression as well as the Granger causality analysis based on vector autoregression (VAR) framework, which accounts for simultaneity, confirms that commodity markets are moving on its own supply-demand factors. The rolling regression also brings to light the disciplining effect of regulatory scrutiny and audit trail in the Indian commodity market around July 2013, when National Spot Exchange Ltd. (NSEL) payment crisis and commodity transaction taxes (CTT) occurred. JEL Classifications: G180, G28, C580, G1, G100. G130

Searching for hedging and safe haven assets for Indian equity market – a comparison between gold, cryptocurrency and commodities

PurposeThis paper aims to evaluate the hedging and safe haven properties of gold, cryptocurrency and commodities against the Indian equity market.Design/methodology/approachFirst, the authors estimate the hedging and safe haven abilities of gold, cryptocurrency and commodities for the Indian stock market and further verify whether such properties vary across the broad stock market indices and over the different degrees of market volatility. Second, the authors use the multivariate GARCH framework to calculate the dynamic hedge ratios and hedging efficiencies to compare the hedging properties of the alternative asset classes. Third, the authors verify the robustness of the general findings during the recent crisis emanating from the outbreak of the COVID-19 pandemic.FindingsGold, cryptocurrency and most commodities have significant hedging abilities. Only natural gas, crude oil and aluminum, on the other hand, have safe haven property. Neither gold nor cryptocurrency qualifies as a safe haven asset. On the other hand, the financialization of the Indian commodities market provides a significant dividend to investors in terms of hedging and safe haven capabilities. The authors find the least negative hedge ratio and the highest positive hedging effectiveness for the stock-crude oil and stock-natural gas portfolios. The central observations of the paper remain immune to the COVID crisis.Originality/valueFocusing on the Indian equity market, the paper compares the diversification abilities of traditional assets like gold with those of the modern class of assets, including cryptocurrency and other commodities.

A Study of Indian Copper Market in Multi-Commodity Exchange

This paper examines the relationship between spot and futures prices in the Indian commodity market from 2015-2019, considering copper as one of the base metals. In this study, the closing spot and future price data obtained from Multi commodity exchange of India are used to investigate price discovery. Various econometric tools are used to explore the long and the short-run relationship between spot and futures prices. ADF, Johansen’s and Juliesus cointegration test, Vector Error Correction Model Test, Granger causality is carried out during the empirical process. The statistical result of the study indicates that the price is first discovered in the spot market for copper during the study period. The granger causality test indicates that it is unidirectional in the short run.

Indian Commodity Market Price Comparative Study of Forecasting Methods - A Case Study on Onion, Potato and Tomato

In this study several characteristics are taken into account so that the crop price forecast is accurate. Forecasting the price of agriculture commodities based on Volume, diesel price helps the agriculturist and also the agriculture mandi’s in India. We look at onion, tomato, and potato trading in India and present the evaluation of a price forecasting model, and anomaly detection and compared differently Supervised, Unsupervised and Forecasting prediction models. We prefer to use wholesale prices, retail prices, arrival volumes of the agricultural commodities and Diesel prices in India. We also provide an in-depth forecasting analysis of the effect on these retail prices. Our results are encouraging and point towards the likelihood of building pricing models for agricultural commodities and to detect anomalies. These data can then be stored and analyzed. The empirical comparison of the chosen methods on the various data showed that some methods are more suitable than others for this type of problem. In this research, we did a comparative study of Auto ARIMA (Autoregressive Integrated Moving Average), RNN (Recurrent Neural Network), LSTM, VAR (vector autoregressive model), and Random Forest Regression, XGBoost in their ability to predict Retail prices of potatoes, onions and tomatoes.

Modeling the Commodity Prices of Base Metals in Indian Commodity Market Using a Higher Order Markovian Approach.

A higher order Markovian (HOM) model to capture the dynamics of commodity prices is proposed as an alternative to a Markovian model. In particular, the order of the former model, is taken to be the delay, in the response of the industry, to the market information. This is then empirically analyzed for the prices of copper mini and four other bases metals, namely aluminum, lead, nickel and zinc, in the Indian commodities market. In case of copper mini, the usage of the HOM approach consistently offered improvement, over the Markovian approach, in terms of the errors in forecasting. Similar trends were observed for the other base metals considered, with the exception of aluminum, which can be attributed to the volatility in the Asian market during the COVID-19 outbreak.

Seasonality in Indian Commodities Market: Insights for modeling from preceding commodity cycle

In this study we analyze seasonal behavior of Indian agriculture, energy and metal commodities through the ten years of preceding Super-cycle (2003-13) by considering monthly data for near month futures prices to remove basis variance. The thrust of the paper is to investigate seasonal behavior of selected commodities in India using monthly seasonal dummies. We found seasonal variation in four commodities namely gold, barley, guar and Jeera. In a declining interest-rate scenario and Post-Covid world, there is elevated likelihood that we are going to witness the next commodities super-cycle. It is imperative for hedge fund managers, global investors, commodities traders, high net-worth individuals, market participants and spread-traders to know what to expect based on the preceding super-cycle to strategize better and address seasonality.

Financialisation of commodities – Empirical evidence from the Indian financial market

Financialisation of commodities commenced from the year 2009 with massive inflows of institutional investment. There is mixed evidence as to whether this financialisation of commodities has led to increase in price volatility of the commodities. This study attempts to examine the financialisation of the Indian commodities market by examining the interconnectedness between the Indian commodities market and the Indian stock market in terms of return co-movements and also in terms of volatility spillovers. The analysis indicates a unidirectional causality as well as the existence of volatility spillovers from the stock market to the commodities market.

Time-Varying Conditional Profitability of Momentum Strategies in Commodity Futures Market: Evidence from India

ABSTRACT Manuscript type: Research paper Research aim: This study aims to provide fresh evidence on the presence of momentum profitability in the Indian commodity futures market. Design/Methodology/Approach: This study is based on a sample of highly traded commodity future contracts of the Indian commodity market over the period from 2006 to 2017. It applies the conditional multi-factor model to test the time-varying performance of momentum strategies. Research findings: This study confirms the existence of exceptionally high abnormal momentum profitability in the commodity futures market despite the presence of transaction costs. However, the application of conditional multi-factor model suggests that momentum profits are basically time-varying. The low and insignificant correlation of momentum portfolios with stocks and bonds confirm that relative strength momentum portfolios of commodity futures can be effectively used to create a well-diversified portfolio. Theoretical contribution/Originality: This study analyses the time-varying conditional profitability of momentum strategies for the commodity market of emerging economies such as India. It enriches the small group of studies conducted on commodity futures in the Indian context. The major contribution of the study is the use of conditional multi-factor model to assess the possible role of time-varying conditional alpha and beta to define the momentum payoffs in commodity futures market for the Indian context. Policy implications: Policymakers should design more lucrative policies so as to attract the institutional investors for investment in the Indian commodity market. This is because domestic and foreign institutional investors are central to the enhancement and stability of the financial market. Research implications/Limitations: The study uses the 13 highly traded commodity futures contract to design the momentum strategies. The robustness of the high abnormal returns given by these strategies can be investigated further by the use of extended study period and expanded cross section of commodity futures contract.

The lead–lag relationship between spot and futures prices: Empirical evidence from the Indian commodity market

This paper examines the relationship between spot and futures prices in the Indian commodity market for the period 2009 to 2020. The ARDL bounds-testing technique is used to explore the long-run relationship between these two prices. A vector error correction model is then used to reveal the nature of Granger causality between the two. Six commodities, namely aluminum, copper, crude oil, gold, nickel, and silver, and three indices, namely agricultural, livestock, and precious metals, are used for this empirical process. The results indicate a long-run equilibrium relationship between the spot and futures prices of these commodities. The causality analysis reveals unidirectional causality in the long run from the spot to the futures price for aluminum and copper, and both bidirectional and unidirectional causality in the short run between the two prices for aluminum, copper, gold, and silver.

Active trading strategies based on momentum and term structure signals in commodity futures market: evidence from India

This research designs an active double-sort strategy which integrates both momentum and term structure signals present in the commodity futures market. By using a sample of highly traded commodity future contracts of the Indian commodity market from 2006 to 2016, this study confirms the exceptionally high abnormal profitability of the double-sort strategy. The abnormal returns of the double-sort portfolios are robust to transaction costs incurred for designing these active strategies. The application of a conditional multi-factor model and sub-sample analysis suggests that the return profile of these strategies is basically time-varying. Moreover, the low and insignificant correlation of double-sort portfolios with stocks and bonds confirms that relative strength portfolios of commodity futures can be effectively used to create a well-diversified portfolio.

Modelling and Forecasting Commodities Prices in Emerging Market: Lessons from the Preceding Super Cycle

Futures trading is one of the oldest methods of trading and investing in commodities. History of commodities futures trading in India is interrupted, flabbergasted and disrupted unlike commodities future trading in Chicago Mercantile Exchange (CME), Chicago Board of Trade (CBOT), or London Metal Exchange (LME) where futures trading has been taking place uninterrupted for over a century. Prohibiting of futures trading in India in a large part of the last few decades has ensured research on commodities trading in India is still at an embryonic stage. In this study, we model Commodity prices of select Agriculture (Barley, Jeera, Sugar, and Pepper), Metal (Aluminium, Copper, Lead, and Gold), and Energy commodities (Crude Oil) in Indian Commodity Markets. Data during the Super-cycle period of commodities in India from 2003 to 2013 is used for the study and modeled using the state-space specification. The results of the study suggest that state-space specification and Kalman filter provides preeminent estimates for modeling and forecasting Indian commodity prices during the Super-cycle period. The results of the study provide crucial insights for pension funds, alternate investment funds, hedge funds and sovereign wealth funds worldwide to strategize better in the next expected super-cycle period of commodities post Covid-19 with burgeoning demand from developing economies of Asia and Africa.

Determinants of the Commodity Futures Market Performance: An Indian Perspective

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.