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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...
Persistent link: https://www.econbiz.de/10003636113
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...
Persistent link: https://www.econbiz.de/10012966267
estimation plays a key role in its evaluation. Assuming a structural credit risk modeling approach, we study the impact of … effects of different non-parametric estimation techniques on default probability evaluation. The impact of the non …
Persistent link: https://www.econbiz.de/10011506497
We test whether a simple measure of corporate insolvency based on equity return volatility - and denoted as Distance to … Insolvency (DI) - delivers better predictions of corporate default than the widely-used Expected Default Frequency (EDF) measure …
Persistent link: https://www.econbiz.de/10013448706
successfully used as a nonparametric tool for regression estimation and forecasting time series data. In this thesis, we deal with …
Persistent link: https://www.econbiz.de/10013100878
Persistent link: https://www.econbiz.de/10014250993
Persistent link: https://www.econbiz.de/10003989791
In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the...
Persistent link: https://www.econbiz.de/10014480965
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...
Persistent link: https://www.econbiz.de/10014354048
We propose a novel methodology for modeling and forecasting multivariate realized volatilities using graph neural networks. This approach extends the work of Zhang et al. [2022] (Graph-based methods for forecasting realized covariances) and explicitly incorporates the spillover effects from...
Persistent link: https://www.econbiz.de/10014265206