jump-diffusion stochastic volatility

We propose a method using temporal convolutional networks for model parameter estimation by learning the mapping from sample data to its generating parameters. This map is then used for defining moment conditions in simulation-based inference. Our approach outperforms maximum likelihood estimators for small and moderate samples and effectively estimates a jump-diffusion financial model.