The goal of a time series regression problem is best explained by a concrete example. Suppose you own an airline company and you want to predict the number of passengers you'll have next month based ...

This paper examines the application of various stochastic volatility models to real data and demonstrates their effectiveness in calibrating a wide range of options, including those with short-term ...

The paper presents a Bayesian framework for the calibration of financial models using neural stochastic differential equations (neural SDEs), for which we also formulate a global universal ...

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. The market price of risk is taken to be λ=0. Automatic differentiation is ...