Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource PHD PhD PhD Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Relation A related resource No Thesis Title A name given to the resource Forecasting volatility evidence from the futures market in India Subject The topic of the resource Management Studies Description An account of the resource This thesis focuses on modelling and forecasting of select products in the Indian futures market using econometric time series models and artificial neural network based models. These models have been compared for their forecasting accuracy to determine the best forecasting model for a particular futures series. This study applies GARCH, EGARCH, PARCH, TARCH, and Artificial Neural Networks (ANN) to assess the best predicting model for exchange rate futures, commodity index futures and stock index futures. After testing for stationarity of data series, GARCH, EGARCH, PARCH and TARCH models are developed. In addition to in-sample forecasts, 1-day, 5-day, 10-day, 15-day and 30-day out-of-sample forecasts have been carried out. For ANN, data is scaled using the minmax scaling methodology to ensure that newlinethe data series is normalised and in the range of 0 to 1. ANN is developed using the feedforward methodology. While the basic neural network architecture has one input layer, one hidden layer and one output layer, the number of neurons in the input and hidden layers vary from 1 to 20. The optimum number of input and hidden neurons in their respective layers are then selected based on the combination which gives the least error. These network combinations are used for out-of-sample forecasting and errors are compared with the forecast output of the GARCH models. RMSE, MAE, MAPE, Theil s-U statistic and Correlation coefficient is computed for error newlinecomparison. Results indicate that for currency futures and commodity index futures, ANN provides better forecast accuracy. For stock index futures, GARCH family models work better in some cases. Creator An entity primarily responsible for making the resource A S, Veena. Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2018-01-01 Contributor An entity responsible for making contributions to the resource Mathew, Jain. Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/426707" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/426707</a>