Recursive marginal quantization (RMQ) allows the construction of optimal discrete grids for approximating solutions to stochastic differential equations in d-dimensions. Product Markovian quantization (PMQ) reduces this problem to d one-dimensional quantization problems by recursively...

We consider the application of a control variate technique for Deep Learning. In analogy to applications for Monte Carlo simulation or Fourier integration methods, this technique improves the quality of deep learning applied to option pricing problems. Many well known approximation methods are...

Deep learning is a powerful tool, which is becoming increasingly popular in financial modeling. However, model validation requirements such as SR 11-7 pose a significant obstacle to the deployment of neural networks in a bank's production system. Their typically high number of (hyper-)parameters...

There are numerous models for specifying the uncertainty of future instantaneous volatility or variance, including the Heston, SABR and ZABR models. Often it is observed that a specific stochastic volatility model is chosen not for particular dynamical features, relevant for exotic payoff...