Neural networks
Robust pricing and hedging via neural stochastic differential equations
The authors propose a model called neural SDE and demonstrate how this model can make it possible to find robust bounds for the prices of derivatives and the corresponding hedging strategies.
Rising star in quantitative finance: Sigurd Emil Rømer
Risk Awards 2023: Doctoral dissertation outlines more efficient way to simulate rough volatility models
Podcast: Piterbarg and Antonov on alternatives to neural networks
Two novel approximation techniques can overcome the curse of dimensionality
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Deep learning for efficient frontier calculation in finance
The author puts forward a means to calculate the efficient frontier in the Mean-Variance and Mean-CVaR portfolio optimization problems using deep neural network algorithms.
Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange
This paper investigates how input window length and forecast horizon affect the predictive performance of a trading signal prediction system.
Pricing barrier options with deep backward stochastic differential equation methods
This paper presents a novel and direct approach to solving boundary- and final-value problems, corresponding to barrier options, using forward pathwise deep learning and forward–backward stochastic differential equations.
Predicting financial distress of Chinese listed companies using a novel hybrid model framework with an imbalanced-data perspective
In this paper a novel hybrid model framework is constructed to solve the problem of predicting the financial distress of Chinese listed companies using imbalanced data.
Banks strive for machine learning at quantum speed
Embryonic work on quantum neural networks raises hope of faster, more accurate models
Derivatives house of the year: JP Morgan
Risk Awards 2022: Big bet on AI is delivering results
Customer churn prediction for commercial banks using customer-value-weighted machine learning models
In this paper the authors propose a framework to address the issue of customer churn prediction, and they quantify customer values with the use of an improved customer value model.
Covariance estimation for risk-based portfolio optimization: an integrated approach
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.
Multi-horizon forecasting for limit order books
A multi-step path is forecast using deep learning and parallel computing
Neural networks show fewer false positives on bad loans – study
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers
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.
An ‘optimal’ way to calculate future P&L distributions?
Quants use neural networks to upgrade classic options pricing model
More banks flirt with machine learning for CCAR
Superior computational grunt of neural networks is attractive to lenders. Lack of explainability is the downside
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.
Wells touts new explainability technique for AI credit models
Novel interpretability method could spur greater use of ReLU neural networks for credit scoring
Show your workings: lenders push to demystify AI models
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy