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Found 7 papers, 2 papers with code
Beyond Surrogate Modeling: Learning the Local Volatility Via Shape Constraints
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
Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing.
MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather
We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product.
Deep Local Volatility
Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks.
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning
Our approach is based on G-learning - a probabilistic extension of the Q-learning method of reinforcement learning.
OSTSC: Over Sampling for Time Series Classification in R
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-based Financial Markets Prediction using Deep Neural Networks
Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers.
