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- W4281249572 abstract "Risk permeates more and more financial markets around the world. It is an essential element that all financial market actors attempt to model and manage. This paper proposes a novel model that improves the predictive accuracy of high frequency volatility forecasts. The ANN-MC-GARCH model is therefore developed in this research. The hybrid model enhances the MC-GARCH conditional variance model by including endogenous market variables in a feed forward network which models volatility in terms of past disturbances and variances. The forecasting accuracy of the novel ANN-MC-GARCH is evaluated against the classical MC-GARCH model. The empirical investigation employs the 1-min high frequency observations of four exchange rates (USD/EUR, USD/GBP, USD/JPY & AUD/JPY), three market indices (France 40, UK 100 & USA 500) and two metal commodity indices (Spot Gold & Spot Silver). The backtesting method employs the RMSE and MAE performance metrics, the out-of-sample R 2 $$ {R}^2 $$ , the Diebold-Mariano test and the Model Confidence Set (MCS) procedure. The empirical findings show that the hybrid model is superior to the MC-GARCH model for the nine datasets." @default.
- W4281249572 created "2022-05-23" @default.
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- W4281249572 date "2022-05-20" @default.
- W4281249572 modified "2023-09-25" @default.
- W4281249572 title "High frequency volatility forecasting: A new approach using a hybrid <scp>ANN‐MC‐GARCH</scp> model" @default.
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- W4281249572 doi "https://doi.org/10.1002/ijfe.2640" @default.
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