Technical paper/Neural networks
Multi-horizon forecasting for limit order books
A multi-step path is forecast using deep learning and parallel computing
An artificial neural network representation of the SABR stochastic volatility model
In this paper the universal approximation theorem of artificial neural networks (ANNs) is applied to the stochastic alpha beta rho (SABR) stochastic volatility model in order to construct highly efficient representations.
Deep learning profit and loss
The P&L distribution of a complex derivatives portfolio is computed via deep learning
Comprehensive Capital Analysis and Review consistent yield curve stress testing: from Nelson–Siegel to machine learning
This paper develops different techniques for interpreting yield curve scenarios generated from the FRB’s annual CCAR review.
Axes that matter: PCA with a difference
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Deep learning for discrete-time hedging in incomplete markets
This paper presents several algorithms based on machine learning to solve hedging problems in incomplete markets.
An interpretable Comprehensive Capital Analysis and Review (CCAR) neural network model for portfolio loss forecasting and stress testing
This paper proposes an interpretable nonlinear neural network model that translates business regulatory requirements into model constraints.
Generating financial markets with signatures
Signatures can provide the synthetic data to train deep hedging strategies
Nowcasting networks
The authors devise a neural network-based compression/completion methodology for financial nowcasting.
Neural network middle-term probabilistic forecasting of daily power consumption
The authors propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature.
Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
A k-means++-improved radial basis function neural network model for corporate financial crisis early warning: an empirical model validation for Chinese listed companies
This paper aims to simplify the early warning model for financial crises by collecting and analyzing the financial data of Chinese special treatment (ST) companies, normally listed companies and cancel special treatment (CST) companies.
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
Scoring models for roboadvisory platforms: a network approach
In this paper, the authors show how to exploit the available data to build portfolios that better fit the risk profiles of investors. This is made possible, on the one hand, by constructing groups of homogeneous risk profiles based on user responses to…
The market generator
A generative neural network is proposed to create synthetic datasets that mantain the statistical properties of the original dataset
Interpretability of neural networks: a credit card default model example
Recently developed techniques aimed at answering interpretability issues in neural networks are tested and applied to a retail banking case
Optimal posting of collateral with recurrent neural networks
Pierre Henry-Labordère applies neural networks to a control problem approach for managing collateral
Ensemble models in forecasting financial markets
In this paper, the authors study an evolutionary framework for the optimization of various types of neural network structures and parameters.
CVA and IM: welcome to the machine
Henry-Labordere proposes a neural networks-based technique to price counterparty risk and initial margin
Dilated convolutional neural networks for time series forecasting
In this paper, the authors present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture.
An empirical study on credit risk management: the case of nonbanking financial companies
The aim of this paper is to predict future default behaviors of nonbank financial company customers using credit scores.
Chaotic behavior in financial market volatility
In this paper, the authors present a robust method for the detection of chaos based on the Lyapunov exponent, which is consistent even for noisy and finite scalar time series.
Curve dynamics with artificial neural networks
Artificial neural networks can replace PCA for yield curves analysis