Technical paper/Machine learning
Solving final value problems with deep learning
Pricing vanilla and exotic options with a deep learning approach for PDEs
Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
Machine learning hedge strategy with deep Gaussian process regression
An optimal hedging strategy for options in discrete time using a reinforcement learning technique
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
The data anonymiser
Non-parametric approaches anonymise datasets while reproducing their statistical properties
Integrating macroeconomic variables into behavioral models for interest rate risk measurement in the banking book
This paper proposed a nonparametric approach to decompose a macroeconomic variable into an interest-rate-correlated component and a macro-specific component.
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
An advanced hybrid classification technique for credit risk evaluation
In this paper, the authors employ a hybrid approach to design a practical and effective CRE model based on a deep belief network (DBN) and the K-means method.
Optimal posting of collateral with recurrent neural networks
Pierre Henry-Labordère applies neural networks to a control problem approach for managing collateral
CVA and IM: welcome to the machine
Henry-Labordere proposes a neural networks-based technique to price counterparty risk and initial margin
Global perspectives on operational risk management and practice: a survey by the Institute of Operational Risk (IOR) and the Center for Financial Professionals (CeFPro)
This paper presents survey results which represent comprehensive perspectives on operational risk practice, obtained from practitioners in a wide range of countries and sectors.
Predictive fraud analytics: B-tests
In this paper, the authors look at B-tests: methods by which it is possible to identify internal fraud among employees and partners of the bank at an early stage.
Curve dynamics with artificial neural networks
Artificial neural networks can replace PCA for yield curves analysis
Evolutionary algos for optimising MVA
Alexei Kondratyev and George Giorgidze apply two evolutionary algos to MVA optimisation
Machine learning for trading
Gordon Ritter applies reinforcement learning to dynamic trading strategies with market impact
Model calibration with neural networks
Andres Hernandez presents a neural network approach to speed up model calibration