2 citations · 7 across the 5 of their papers we have counts for
11 papers
A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives
Dixon Domfeh, Arpita Chatterjee, Matthew Dixon
Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertain…
Deep Local Volatility
Marc Chataigner, Stéphane Crépey, Matthew Dixon
Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks. However, many of these approac…
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks
Matthew F Dixon
Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine lea…
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning
Matthew Dixon, Igor Halperin
We present a reinforcement learning approach to goal based wealth management problems such as optimization of retirement plans or target dated funds. In such problems, an investor…
Deep Fundamental Factor Models
Matthew F. Dixon, Nicholas G. Polson
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling. Uncertainty quantification provides interpretability…
Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations
Stéphane Crépey, Matthew Dixon
Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk…