Search Results for author: Matthew Dixon

Found 7 papers, 2 papers with code

Beyond Surrogate Modeling: Learning the Local Volatility Via Shape Constraints

1 code implementation20 Dec 2022 Marc Chataigner, Areski Cousin, Stéphane Crépey, Matthew Dixon, Djibril Gueye

We explore the abilities of two machine learning approaches for no-arbitrage interpolation of European vanilla option prices, which jointly yield the corresponding local volatility surface: a finite dimensional Gaussian process (GP) regression approach under no-arbitrage constraints based on prices, and a neural net (NN) approach with penalization of arbitrages based on implied volatilities.

A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives

no code implementations9 May 2022 Dixon Domfeh, Arpita Chatterjee, Matthew Dixon

Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing.

MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

no code implementations18 Nov 2021 Sylwester Klocek, Haiyu Dong, Matthew Dixon, Panashe Kanengoni, Najeeb Kazmi, Pete Luferenko, Zhongjian Lv, Shikhar Sharma, Jonathan Weyn, Siqi Xiang

We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product.

Optical Flow Estimation

Deep Local Volatility

1 code implementation20 Jul 2020 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.

Experimental Design

G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning

no code implementations25 Feb 2020 Matthew Dixon, Igor Halperin

Our approach is based on G-learning - a probabilistic extension of the Q-learning method of reinforcement learning.

Management Q-Learning +2

OSTSC: Over Sampling for Time Series Classification in R

no code implementations27 Nov 2017 Matthew Dixon, Diego Klabjan, Lan Wei

The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling methodology implemented by the package.

Classification General Classification +2

Classification-based Financial Markets Prediction using Deep Neural Networks

no code implementations29 Mar 2016 Matthew Dixon, Diego Klabjan, Jin Hoon Bang

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers.

Algorithmic Trading Classification +1

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