is an instantiation of generative models and is closely linked with the theory of causal optimal transport. Neural SDEs … volatility models …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

results show that asymmetric models generally outperform symmetric ones, indicating that a correlation between volatility and … returns plays an important role for volatility forecasting. Additionally, models utilizing a logarithmic transformation of the …

In a recent paper "Deep Learning Volatility" a fast 2-step deep calibration algorithm for rough volatility models was … approach outperforms the two-step one for the data sets and methods published in "Deep Learning Volatility". For our …

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory … behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure … topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility …

Here, we introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets …