Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A hybrid model is analyzed to predict oil price return volatility.The hybrid model used is an ANN-GARCH model.The ANN improves forecasting accuracy over the GARCH and ARFIMA model prediction.The precision of the price return volatility forecasting increases by 30%.The main financial variables to improve the forecast re determined.  builds on previous research and seeks to determine whether improvements can be achieved in the forecasting of oil price volatility by using a hybrid model and incorporating financial variables. The main conclusion is that the hybrid model increases the volatility forecasting precision by 30% over previous models as measured by a heteroscedasticity-adjusted mean squared error (HMSE) model. Key financial variables included in the model that improved the prediction are the Euro/Dollar and Yen/Dollar exchange rates, and the DJIA and FTSE stock market indexes.
['Werner Kristjanpoller', 'Marcel C. Minutolo']
I am writing a research paper on a idea Forecasting volatility of oil price using an artificial neural network-GARCH model
[{"from": "human", "value": "I am writing a research paper on a idea Forecasting volatility of oil price using an artificial neural network-GARCH model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A hybrid model is analyzed to predict oil price return volatility.The hybrid model used is an ANN-GARCH model.The ANN improves forecasting accuracy over the GARCH and ARFIMA model prediction.The precision of the price return volatility forecasting increases by 30%.The main financial variables to improve the forecast re determined.  builds on previous research and seeks to determine whether improvements can be achieved in the forecasting of oil price volatility by using a hybrid model and incorporating financial variables. The main conclusion is that the hybrid model increases the volatility forecasting precision by 30% over previous models as measured by a heteroscedasticity-adjusted mean squared error (HMSE) model. Key financial variables included in the model that improved the prediction are the Euro/Dollar and Yen/Dollar exchange rates, and the DJIA and FTSE stock market indexes."}]