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The purpose of this paper is to examine the price discovery and volatility spillovers in spot and futures prices of four currencies (namely, USD/INR, EURO/INR, GBP/INR and JPY/INR) and between futures prices of both stock exchanges namely, Multi-Commodity Stock Exchange (MCX-SX) and National Stock Exchange (NSE) in India.

The study applies cointegration test of Johansen’s along with VECM to investigate the price discovery. GARCH-BEKK model is used to examine the volatility spillover between spot and futures and between futures prices. The other two models namely, constant conditional correlation and dynamic conditional correlation are used to demonstrate the constant and time-varying correlations. In order to confirm the volatility spillover results, the study also applies test of directional spillovers suggested by Diebold and Yilmaz (2009, 2012).

The results of the study show that there is long-term equilibrium relationship between spot and futures and between futures markets. Between futures and spot prices, futures price appears to lead the spot price in the short-run. Volatility spillover results indicate that the movement of volatility spillover takes place from futures to spot in the short-run while spot to futures found in the long-run. However, the results of between futures markets exhibit the dominance of MCX-SX over NSE in terms of volatility spillovers. By and large, the findings of the study indicate the important role of futures market in price discovery as well as volatility spillovers in India’s currency market.

The results highlight the role of futures market in the information transmission process as it appears to assimilate new information quicker than spot market. Hence, policymakers in emerging markets such as India should focus on the development of necessary institutional and fiscal architecture, as well as regulatory reforms, so that the currency market trading platforms can achieve greater liquidity and efficiency.

Due to recent development of currency futures market, there is dearth of literature on this subject. With the apparent importance of currency market in recent time, this study attempts to study the efficient behavior of currency market by way of examining the price discovery and volatility spillovers between spot and futures and between futures prices of four currencies traded on two platforms. The study has strong implications for India’s stock market especially at the time when its currency is under great strain owing to the adverse impact of global financial crisis.

The nature of price volume relationship in asset market has been an interesting subject in financial research as it reveals a very important aspect which has implications for market efficiency. The purpose of this paper is to examine price volume relationships in Indian commodity futures market.

There are two competing models in price volume relationship. Mixture of distribution hypothesis, suggesting a positive contemporaneous relationship and sequential information arrival hypothesis (SIH), suggesting a positive intertemporal causal relationship. Both are tested using correlation coefficient and Granger causality test with vector auto regressive methodology.

Though there exists contemporaneous correlation between volume and price change in some of the cases, but in general on the basis of the presence of Granger causality it follows that SIH is supported.

As only three commodities futures have been studied in this paper, this study can be extended to include more number of commodities currently being traded so as to make it more exhaustive.

The research has been done with the data of MCX Gold, MCX Silver and MCX Crude. The results of causality suggest that inefficiency level is maximum in Silver which may be attributed to informational asymmetry.

The Indian commodity futures market is of very recent origin. Hence, very little research work has been undertaken in this space. The paper presents an assessment of the existence of informational asymmetry among the three commodity futures under the study.

The study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.

The study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.

The results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.

The results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.

Majority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.

The commodity and equity derivatives have a close resemblance between them in trade practices and mechanisms, which makes it easy for the investors to combine these two assets classes for building up their portfolio. The diversification of investment among asset classes builds some relation between them. The integration of market within a country is necessary to bring in a smooth and balanced economic growth. Thus, the purpose of this paper is to examine the spillover between the equity and commodity futures markets which will be helpful not only for the investors but also for the policy makers, producers and the regulators.

To examine the spillover between the equity and commodity market, the major benchmarking indices of these markets, namely COMDEX of MCX, Dhaanya of NCDEX and NIFTY 50 of NSE, were chosen. NIFTY 50 index was chosen as representative of equity market due to its composition of most active constituent stocks than any other broad market index of Indian stock market. As the commodity market indices are not been traded, their constituent commodities were taken for the study. Thus, 11 MCX-COMDEX constituents such as Gold, Silver, Copper, Zinc, Aluminum, Nickel, Lead, Crude oil, Natural gas, Kapaskhali and Mentha oil and eight NCDEX-Dhaanya constituents such as Castor seed, Chana, Cotton seed oilcake, Jeera, Mustard seed, Refined soy oil, Turmeric and Wheat futures prices were taken against the NIFTY 50 futures prices with daily trading data for ten years starting from January 1, 2006 till December 31, 2015 to analyze their spillover effect. The return series data were used to test the spillover between equity and commodity futures market as it gives the crux of investors’ diversification through the Vector Autoregression (VAR) model and verified with Impulse Response Function by testing the null hypothesis, H₀, that there is no return spillover between the equity and commodity futures market.

The investors move from equity to commodity when there is a threat in equity market and vice versa, thereby diversify their risk for those commodities which are vulnerable to global and domestic pressures in the economy. Investigating the spillover between equity and commodity market gives an insight of market integration effect. A nation can achieve its economic growth easily when its markets are integrated.

The commodity indices are still notional indices in the market; therefore, individual constituent commodities of commodities indices were considered with the benchmarking equity futures index, which is one of the limitations of the study.

The integration of market within a country is necessary to bring in a smooth and balanced economic growth.

Most of the past studies dealt only with few commodities and equities and not with the broad-based benchmarking indices. This paves way for enquiry into the commodity and equity markets integration with the major constituent commodities traded in the economy. Hence, this paper looks into the presence of spillover between the equity and commodity markets. The VAR model is verified with the impulse response function which explains the reaction of any dynamic system in response to a pulse change in another. The impulse response function is presented graphically for easy and better understanding.

We propose a Machine Learning (ML) approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms. The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities. The network will be trained and evaluated for accuracy with various sizes of data sets, i.e. weekly historical data of MCX, GOLD, COPPER and the results will be calculated.

Desirable LSTM model for script price forecasting from the perspective of minimizing MSE. The approach which we have followed is shown below. (1) Acquire the Dataset. (2) Define your training and testing columns in the dataset. (3) Transform the input value using scalar. (4) Define the custom loss function. (5) Build and Compile the model. (6) Visualise the improvements in results.

Financial series is one of the very aged techniques where a commerce person would commerce financial scripts, make business and earn some wealth from these companies that vend a part of their business on trading manifesto. Forecasting financial script prices is complex tasks that consider extensive human–computer interaction. Due to the correlated nature of financial series prices, conventional batch processing methods like an artificial neural network, convolutional neural network, cannot be utilised efficiently for financial market analysis. We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic (LSTM). The LSTM Classic is quite different from normal LSTM as it has customised loss function in it. This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure, and it helps forecast financial time series. Financial Series Index is the combination of various commodities (time series). This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.

We had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset. For every epoch we can visualise the improvements in loss. One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts. Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.

The purpose of this paper is to investigate empirically the price discovery and volatility spillovers in Indian spot-futures commodity markets.

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.

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.

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.

The paper provides useful information about the evolution and structures of futures commodity trading in India, related literature and relevant methodology concerning the hypotheses.

Exchanges are designed to be default tolerant. The exchange functions as a counter-party in all transactions. A system of margining ensures that a transaction is always consummated even if either the buyer or the seller fails to honour his/her commitment. Investigations on the payment crisis at the National Spot Exchange Limited (NSEL) in 2013 involving Rs. 5,600 crore revealed that the reasons for the crisis lay in the hubris of the promoters, connivance and collusion of the independent and institutional directors on the board of NSEL, benevolence of the government and ambivalence of the regulator. The failure raises serious concerns about governance at all levels in the country. Fundamental principles of governance were given a short shrift.

After completion of the case study, the students will be able to understand the different risks associated with a business, focusing on price risk and the importance of price risk management in business; understand and evaluate the products available for hedging price risk through exchange-traded derivatives in the Indian scenario; and understand and evaluate the different strategies for price risk management through exchange-traded derivatives in the Indian scenario.

The case study pertains to a small business, M/s Sethi Jewellers. The enterprise is being run by Shri Charan Jeet Sethi and his son Tejinder Sethi. The business is located in Jain Bazar, Jammu, UT, in Northern India. The business was started in 1972 by Charan Jeet’s father. They deal in a wide range of jewelry products and are well-established jewelers known for selling quality ornaments. Tejinder (MBA in marketing) was instrumental in revamping his business recently. Under his leadership, the business has experienced rapid transformation. The business has grown from a one-room shop fully managed by Tejinder’s grandfather to a multistory showroom with several artisans, sales staff and security persons. Through his e-store, Tejinder has a bulk order from a client where the client requires him to accept the order with a small token at the current price and deliver the final product three months from now. Tejinder is in a dilemma about accepting or rejecting the large order. Second, if he accepts, should he buy the entire gold now or wait to buy it later at a lower price? He is also considering hedging the price risk through exchange-traded derivatives. However, he is not entirely sure, as he has a few apprehensions regarding the same, and he is also not fully aware of the process and the instruments he has to use for hedging the price risk on the exchange.

The case study is aimed to cater to undergraduate, postgraduate and MBA students in the field of finance. This case study can be used for students interested in commodity derivatives, risk management and market microstructure.

Teaching notes are available for educators only.

CSS 1: Accounting and finance.

This paper describes some of the benefits of electronic packages manufactured from silicon carbide reinforced aluminium composites. The housings which were analysed and tested consisted of iron‐nickel alloy sidewalls soldered to composite bases. The metal matrix composite bases were produced using Lanxide's PRIMEX™ pressureless metal infiltration process. Hermeticity test results on the base to sidewall seals are presented along with comparative electrical performance of the composite versus conventional base materials. Analysis of the thermal, mechanical and weight performance of this approach is also provided.