Neural network regression for Bermudan option pricing

Stochastic Kriging for Bermudan Option Pricing

We investigate three new strategies for the numerical solution of optimal stopping problems within the Regression Monte Carlo (RMC) framework of Longstaff and Schwartz. First, we propose the use of stochastic kriging (Gaussian process) meta-models for fitting the continuation value. Kriging offers a flexible, nonparametric regression approach that quantifies approximation quality. Second, we co...

Importance Resampling with MRAS algorithm for Bermudan option pricing

Numeraire-invariant Option Pricing & American, Bermudan, and Trigger Stream Rollover

Part I proposes a numeraire-invariant option pricing framework. It defines an option, its price process, and such notions as option indistinguishability and equivalence, domination, payoff process, trigger option, and semipositive option. It develops some of their basic properties, including price transitivity law, indistinguishability results, convergence results, and, in relation to nonnegati...

NN-OPT: Neural Network for Option Pricing Using Multinomial Tree

We provide a framework for learning to price complex options by learning risk-neutral measures (Martingale measures). In a simple geometric Brownian motion model, the price volatility, fixed interest rate and a no-arbitrage condition suffice to determine a unique riskneutral measure. On the other hand, in our framework, we relax some of these assumptions to obtain a class of allowable risk-neut...

Option Pricing using Neural Networks

The first attempt was made by Hutchinson, Lo and Poggio (1994) who used three different network architectures: Radial Basis Functions (RBF), Multi Layer Perceptron (MLP), Projection Pursuit Regression (PPR) to fit both Monte-Carlo simulated Brownian underlier and Black-Scholes option data, as well as S&P500 futures and options thereof. They used a minimalistic approach in their input selection,...