In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

In recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving...

We present the non-Gaussian extension of the traditional Merton framework, which takes into account slowly relaxing fluctuations of the volatility of the firm's market value of financial assets. The minimal version of the model depends on the Tsallis entropic parameter q and the generalized...

The Turkish version of this paper can be found at: "http://ssrn.com/abstract=2222071" http://ssrn.com/abstract=2222071The study aims to investigate linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models and their nonlinear counterparts based on Support Vector...

This paper proposes a novel algorithm called Persistent Homology for Realized Volatility (PH-RV), which aims to effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting realized volatility (RV). This paper also proposes a novel...