In this article, we present the analytical approximation of zero-coupon bonds and swaption prices for general short rate models. The approximation is based on regular and singular expansions with respect to the small volatility and contains a low-dimensional integration. The model in hand...

Artificial Neural Networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between...

The modeling of tenor basis spreads is of central importance to CVA for tenor basis swaps. Such spreads are typically positive, suggesting a natural lower bound. We introduce a multi- curve Cheyette-style model with lower bounds enforced through level dependence in spread volatilities. The model...

In this article we present an efficient optimization for calculating the exposure and CVA for large portfolios of vanilla swaps. It is based on a "thin-out" procedure applied to fixed payment streams, which reduces a very frequent stream of payments to a much less frequent one. The procedure...

We develop a systematic approach to Markovian projection onto an effective displaced diffusion, and work out a set of computationally efficient formulas valid for a large class of non-Markovian underlying processes. The generic derivation is followed by applications, including the calculation of...

A model/hedging performance is relatively poorly covered in the literature. This is particularly valid for general portfolios including both vanilla and exotic instruments. Practitioners generally use so called \pnl explain which measures whether portfolio price movements can be explained by...

We develop two neo-classical methods for function approximations, the generalized stochastic sampling (gSS) and the functional tensor train (fTT) methods, that are high-performing alternatives to generic deep neural networks (DNNs) currently routinely proposed for function approximations in...