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The contribution of Indian manufacturing small and medium enterprises to the gross domestic product has been consistently falling across the last few years as they have been struggling to survive and scale up due to a host of factors. This study aims to provide important implications to improve their competitive position by trying to identify the determinants which influence the two critical indicators of sustainable firm performance-sustainable financial performance and sustainable innovation performance. To do so, we empirically analyse 486 R&D conducting SME manufacturing firms from the firm-level data for the period of 2006-2017. Our results indicate that the determinants have a disparate impact on both types of sustainable firm performance. The study reveals important theoretical as well as practical insights for the long-term sustainability and competitiveness of manufacturing SMEs.

Purpose: The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets. Design/methodology/approach: The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)). Findings: The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases. Research limitations/implications: In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets. Originality/value: The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

This 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.

The paper uses methods proposed by Machado and Silva (J Econom 213 (1): 145–173, 2019) to analyze the determinants or factors associated with informal credit access. The advantage of this approach is that it allows the use of methods that are only valid in the estimation of conditional means, while still providing information on how the regressors affect the entire conditional distribution. Our analysis shows how important it is to consider endogeneity as it is observed from the difference in the estimated value of the effect of rate of interest on credit availability.

Agricultural input industries in India have gone through a vital transformation in the last 20 years. Multinational corporations have swiftly increased their R&D expenditure in the pesticide, tractor and seed industries, thereby increasing competition in these input industries. Rapid growth in demand and improvement in technology implementations have lead us to study the performance and transformation of these input industries. Empirical analysis was performed on the listed firms during 2001-2018 to know the major factors affecting the firm performance in the seed, pesticide, fertiliser and agricultural machinery industries. The estimated panel regression models reveal that financial performance of these firms is positively related to the R&D expenditure along with market growth and working capital efficiency. Industry effects model, used to capture the industry-level heterogeneity shows that fertiliser and pesticide industries are seen to negatively impact the firm performance relative to machinery industry. Among the time-specific effects, only the time period 2006-2010 is found to be positive and statistically significant, confirming that the performance of agricultural input firms has significantly improved during the period 2006-2010. The study indicates that the policymakers should foster the spirit of innovation and outward orientation in agricultural input firms through policy incentives.

This study attempts to assess the empirical relationship between corporate governance and the financial performance of firms in the Indian context by using a set of board and ownership characteristics as representatives of corporate governance practices. We employ a panel dataset comprising of a large sample of 1,347 Indian non-financial companies for the period 2010 to 2018. For studying firm performance, we use both accounting-based performance measures like return on assets (ROA) as well as market-based performance measures like Tobin's Q (TQ). Starting from the basic static panel estimation approach using the fixed-effects estimator, we gradually move on to dynamic panel estimation using different GMM and system GMM estimators to examine the various board characteristics that influence firm performance in India. Based on our final model estimated using system GMM, we infer that on one hand, board size, board activity, and promoter ownership are found to positively influence firm performance while on the other hand, board meetings are found to be negatively related to firm performance. For policymakers and corporates, our study provides valuable insights into the specific aspects of corporate governance that can be targeted and implemented effectively to improve the financial performance of firms.

This paper aims to study female participation on bank boards and the effect of gender diversity on publicly listed Indian bank's performance. Banks were subdivided into large and small banks based on market capitalisation. Gender diversity is measured using various variables. Return on assets (ROA) and return on equity (ROE) are taken as a proxy to assess bank performance. A balanced panel data set of 20 banks with duration of 2008-2018 was executed to evaluate the relationship between gender diversity and performance of the banks. The result suggests that there is a value addition to the bank due to the existence of a female CEO in large banks and the presence of women on board in small banks. Few banks seem to meet the minimum requirement set by the regulator. These findings bear policy implications for the banking sector. Emphasis should be put on ways for women to undertake roles in top echelons of management which can enhance the framework of corporate governance.

Purpose: This paper aims to analyse the impact of innovation on the productivity of firms in India using the data from the World Enterprise Survey. This paper first classifies three different types of innovation measures then further analyses their relation with the productivity of the firms. Design/methodology/approach: The methodology used for this study has incorporated the structural Crépon-Douget-Mairesse (CDM) model wherein productivity is measured using both the innovation inputs and the innovation outputs. Three main equations have been used to quantify this relation includes the knowledge intensity function, innovation function and the productivity equation. Findings: Findings indicate that decision to invest in research and development (R&D) is influenced negatively by financial obstacles and trade obstacles and positively influenced by telecommunication obstacles, government obstacles and the size of the firm in India. Similarly, financial obstacles and the size of the firm are affecting the firm’s research expenditure per employee. Also, financial obstacles seem to hinder the research intensity and larger firms seem to have higher research intensity. The size of the firm contributes significantly to product innovation. However, R&D spending seems to be negatively related to the innovation outcome. The findings relating to productivity shows neither product nor process innovation outputs, independently are not contributing significantly to the productivity of firms. However, product and process innovation, together serve as innovation outputs is a significant contributor to firm productivity. On the other hand, organisational innovation contributes significantly to the productivity of the firms in a negative manner. Originality/value: The findings relating to productivity shows neither product nor process innovation outputs, independently are not contributing significantly to the productivity of firms (which has been measured by sales per worker is impacted by the capital and the labour inputs). However, product and process innovation, together serve as innovation outputs is a significant contributor to firm productivity. On the other hand, organisational innovation contributes significantly to the productivity of the firms in a negative manner. The reason could be due to the fact that the definition of organisational innovation incorporates both dissolutions and mergers.

Purpose: The study aims to analyze the impact of Research & Development (R&D) intensity on the firm’s performance, measured by growth of sales in the emerging market like India. Innovation strategy and its outcomes for firms may be different in developing countries as compared to developed countries. Thus, a study that focuses on the emerging economy like India, with a majority of the population dependent on agriculture, is of prime importance to the firm performance in the food and agricultural manufacturing industry. For this study, the broader focus will be on one widely recognised factor which may influence the growth rate of firms, i.e. investment in innovations which is in terms of R&D expenditure. Design/methodology/approach: The paper investigates the relationship between the R&D efforts and growth of firms in the Indian food and agricultural manufacturing industry during 2001–2019. To empirically test the relationship between firm’s growth (FG) and R&D investments, system generalised method of moments technique has been used, hence enabling to avoid problems related to endogeneity and simultaneity. Findings: The findings reveal that investments in innovations have a positive effect on the growth of firms in the Indian food and agricultural manufacturing industry. Investment in R&D also enables the firms to reap benefits from externalities present in the industry. Further analysis reveals that younger firms grow faster when they invest in R&D. More specifically, this paper finds evidence in the case of the food and agricultural industry that import of raw materials negatively affects the FG and export intensity positively affects the growth in the case of R&D firms. Research limitations/implications: This study suggests that the government should encourage the industries to invest optimally in R&D projects by providing favourable fiscal treatments and R&D subsidies which are observed to have positive effects in various developed countries. Originality/value: To the best of the author’s knowledge, the current paper is the first to analyse the impact of innovation in food and agricultural industry on firm’s performance in an emerging economy context with the latest data. This paper agrees that a government initiative to increase private R&D expenditure would have favourable effects on FG as growing investments in R&D lead to further growth of the firms.

Food price fluctuations can impact both producers and consumers. Forecasting the prices of the agricultural commodities is of prime concern not only to the government but also to farmers and agribusiness firms. In developing countries like India, management of food security needs competent and efficient forecasting of food prices. With the availability of data, recent innovation in deep‐learning models provides a feasible solution to accurately forecast the prices. In this study, we examine the superiority of these models using the daily spot prices of five major commodities traded on the National Commodity and Derivatives Exchange: cotton seed, castor seed, rape mustard seed, soybean seed, and guar seed. The results were obtained from the application of the traditional univariate autoregressive integrated moving average model and deep‐learning techniques like the time‐delay neural network (TDNN) and long short‐term memory (LSTM) network. The empirical results indicate that the LSTM model is indeed suitable for the financial domain and captures the directional movement of the spot price changes with high accuracy compared with the TDNN and other linear models. Accuracy of the performance of these models has been compared using out‐of‐sample performance measure. The overall objective of this paper is to demonstrate the utility of spot price forecasting for farmers and traders in offering them the best predictions of the price movements. Our results provide a possibility of developing pricing models that can help in fairly regulating agricultural commodity prices.

Purpose: Price 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/approach: This 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). Findings: The 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/implications: These 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 implications: The 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/value: There 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.

The objective of this study is to examine the return interconnectedness and asymmetric spillover effects in global commodity futures markets, with a focus on the impact of contagion. A competent asymmetric time-varying parameter vector autoregressive (TVP-VAR) model was employed for highly traded commodity futures (cocoa, coffee, corn, cotton, soy, sugar, wheat, and oil) between January 1, 2000, and March 31, 2024. This study investigates the connectedness of commodities in three dimensions: asymmetric spillovers, the influence of oil and oil substitutes on the network, and the impact of major contagions. The average total connectedness index (TCI) indicates that the connectedness is significant throughout the period and increases during the invasion. The findings imply that contagion effects trigger a potential alteration in the structure of the network integration level of the commodities, amplifying system-wide dynamic connectivity due to disurptions caused by oil and oil substitutes. The net plot depicts corn and soy as the net transmitters, with their magnitude increasing during the contagions. The pairwise connectedness index (PCI) revealed that corn-soy, corn‒wheat, and soy-wheat were the primary interactors, while oil became a significant interactor, particularly during the oil crash and the COVID-19 outbreak. Additionally, compared with other contagions, GFC had a potential asymmetric effect on the network. Positive returns dominate the interaction between the primary transmitter and receivers, whereas negative returns do not significantly dominate the total network. These investigations contribute to the literature on the food-fuel nexus in terms of asymmetries and the impact of contagions on the futures market. It also identified the optimal portfolio allocation based on the hedging effectiveness of three portfolio construction strategies.

We examine the Schumpeterian hypothesis that large firms have an advantage in research and development. Using CMIE Prowess data of S&P BSE index, the firms are segregated as per the market capitalisation into large, mid and small cap firms. We analyse the financial performance of these firms for the post-reform period in India (1992-2017). The study finds that the R&D expenditure has statistically significant effects on firms' total income when either fixed industry effects or fixed year effects are taken into account. The income of the firms is also determined by the export earnings and total imports but with less impact than R&D. The results also show that outward-oriented firms have a greater tendency to spend on research and development expenses. Results from quantile regression suggest that the firms in the higher quantile have statistically higher significant effects of R&D expenditure and outward orientation, on firms' total income than the firms in the relatively lower quantile.

Many emerging Asian economies have developed great global competitiveness in the past two decades and play a crucial role in the world economy. This article uses a novel approach to assess the competitiveness of a nation based on factors such as governance, infrastructure and innovation along with labor and capital endowments. Based on the IMF’s classification of 11 emerging Asian economies, panel data analysis is conducted for the period 1996–2011 to identify important determinants of Outward FDI flows and Export of goods and services. The results are quite robust and offer valuable economic insights for policymakers and particularly in the context of India, there are several challenges in these two fronts—governance and infrastructure bottlenecks—that need to be overcome if it is to truly establish itself as a global manufacturing destination.

Many emerging Asian economies have developed great global competitiveness in the past two decades and play a crucial role in the world economy. This paper studies these emerging economies systematically on four pillars of competitiveness. Based on the IMF's classification of 11 emerging Asian economies namely China, Hong Kong, India, Indonesia, South Korea, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka and Thailand, panel data analysis is conducted for these countries over the period 1996-2011. The results presented offer insights into which factors are most crucial to the competitiveness of a nation. Important determinants of GDP, outward FDI flows and export of goods and services are identified. Rigorous analysis is conducted to justify the results and, considering these factors, we suggest important policy implications for these nations. Countries are required to focus on the right factors and implementation of economic and institutional reforms to enhance their global competitiveness.

Asian options is a contract which gives right to the holder to buy/sell the underlying asset for its average price over prescribed period and it has a lower unpredictability, hence exposing cheaper relative to their European counterparts. Asian options are commonly traded on currencies and commodity products which have low trade volumes. Therefore, the pricing of such options become one of the most interesting fields. Much research work has been done on the European and American options using various techniques like Black-Scholes, binomial tree, finite difference, Monte Carlo and Quasi-Monte Carlo model. But none of the studies compares and identified the best model for options pricing, particularly for Asian options pricing. This article aims to evaluate the effectiveness of existing models on Asian options pricing and to suggest a suitable model for forecasting Asian options prices. Findings of the study indicate that the Quasi-Monte Carlo technique is more superior to any other techniques due to its results with high precision and low standard deviation.