We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. (2021) with the pointwise two-stage calibration of Bayer and Stemper (2018). Our methodology inherits robustness from the former while not...

The stochastic nature of investment process implies that it should be treated not unambiguously. Instead of concentrating only on possible return, it is worth analysing three parameters when we discuss the future investment results. These parameters are return possibility, reliability of this...