This study has focused to analyze the influence of agricultural trade on economic growth in India obtaining annual time series data from 1990-91 to 2016-17. This study estimates the relationship between agricultural exports and economic growth in India employing the Error Correction Model. This study has used three variables such as, Gross Domestic Product (GDP), agricultural exports and non-agricultural exports. According to empirical estimates, the Error Correction Model, that is to say catch can tend towards the long run relationship, has been validated. The variables are converging to equilibrium value and the change in agricultural exports and non-agricultural exports are directly affecting the real GDP in India. Furthermore, this study found that there is short-run uni-directional causality between agriculture exports, non-agriculture exports to GDP in India. The main finding of this study is that the agricultural exports and non-agricultural exports are important variables to stimulate economic growth in India. This study recommends an increase effort to be directed towards policies that will expand the volume of a country's agricultural productivity and trade for the economic growth in the country. Highlights m This study identified that the agricultural exports stimulate economic growth in India. This study recommends an increase effort to be directed towards policies that will expand the volume of agricultural productivity and trade for the economy.

The financial sector reforms, role of regulatory authorities and increase in savings attracted investors towards mutual funds. Stock market developments created popularity for equity oriented schemes. In the face of availability of multitudinous schemes, growth schemes introduced in the year 1993 has been studied. Sharpe, Treynor, Jensen and Fama's measures reveals that all the seven schemes showed negative risk premium, scheme s performance was in line with that of market performance, existence of a high degree of positive correlation in weekly time lag while the impact gets reduced as the time lag increases.

This research demonstrates financial derivative trade of unprocessed materials, for the mining industry through legal smart contracts. Within the mining supply chain, a stock of mined resources can reside in a mineral stockpile for over twenty years without gaining financial interest and without undergoing the mineral extraction process to derive value from the asset. This research elaborates on a blockchain solution implemented to increase miners’ short-term cash flow for business operations through the issuance of derivative assets on mineral stockpiles which can be traded through legally binding smart contracts. The system is the first to enable mining companies’ access to the underlying asset’s value earlier in the production lifecycle through smart contract technology whilst providing hedge funds with access to new financial products for investment portfolios.

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Thanks to numerous empirical research studies, a general consensus has been reached on examining the various alternative models for forecasting and modeling volatility in stock futures contracts by using out-of-sample forecast according to statistic and risk management evaluation criteria. The dataset were retrieved from National Stock Exchange (NSE) website terminal for the period from April 1, 2003 and ending on December 31, 2008. The forecasting models that are considered here ranges from Random Walk, Linear Regression, Moving Average, Autoregressive Models. In order to evaluate the forecasting performance of different models we use two forecasting error statistics by considering the root mean square error (RMSE) and the mean absolute percentage error (MAPE) for testing the return characteristics. Our findings suggest that, according to RMSE statistics the autoregressive model and linear regression models rationally shared and ranked first for out-of-sample forecasts in the linear models. In addition, one cannot conclude that the success or failure of a particular type of forecasting model applied to one market carries over to a different market, because the size and liquidity of a market can affect the quality of volatility forecasts. Finally, one can learn more about the market movement and return volatility through studying the non linear approach.