Linyi Qian

Physics-Informed Koopman Neural Estimation of the Heston Model from High-Frequency Observations

We propose a physics-informed learning framework, called Koopman-PINN, to estimate the parameters of the Heston stochastic volatility model with high-frequency price data in financial markets. The method integrates a nonparametric volatility estimation (known as ART-filter in the literature), moment