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...

Lecture notes for a short course on FX option valuation. Includes: - Mathematical framework for FX valuation - Handling the smile and term structure for vanilla options (calls and puts): --- Interpolation issues and techniques --- Handling business time --- Handling market conventions - Pricing...

Tutorial on valuation of mortgage backed securities and collateralized mortgage obligations, including: - Structure of the mortgage market - Prepayment modeling - OAS analysis - Interest rate modeling - Numerical methods - Parallelization

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We...

This paper analyses the implementation and calibration of the Heston Stochastic Volatility Model. We first explain how characteristic functions can be used to esti-mate option prices. Then we consider the implementation of the Heston model, showing that relatively simple solutions can lead to...

This paper introduces the Inverse Gamma (IGa) stochastic volatility model with time-dependent parameters, defined by the volatility dynamics dVt = κt.(θt − Vt).dt λt.Vt.dBt. This non-affine model is much more realistic than classical affine models like the Heston stochastic volatility...

This paper analyses the implementation and calibration of the Heston Stochastic Volatility Model. We first explain how characteristic functions can be used to estimate option prices. Then we consider the implementation of the Heston model, showing that relatively simple solutions can lead to...

The pricing of vanilla options on underliers with cash dividends is a surprisingly contentious and active research subject, for both European or American exercise style. Neither on the listed options side (calls and puts) nor on the flow/structured side of longer-term vanillas or light exotics...

This article proposes a simple and intuitive framework to combine a discrete volatility forecast series produced by a GARCH model with the binomial tree methodology to price path-dependent options. The framework exploits the premise of the path integral methodology of combining the terminal...