Oxford-Man Institute is among those asking: could algorithms gang up and squeeze customers?
The authors summarise ways that machine learning can help categorize textual descriptions of operational loss events into Basel II event types.
Forecasting the loss given default of bank loans with a hybrid multilayer LGD model by extending multidimensional signals
The authors employ signaling theory and machine learning methods to investigate loss given default predictions of commercial banks and propose a method to improve the accuracy of these predictions.
But supervisors cautiously welcome next-gen model risk management
Using reinforcement learning to help replicate asset managers' allocation strategy
The author outlines characteristics of machine learning classifiers, compares methods for dealing with imbalanced data issues, and proposes terms of best practice in model development, evaluation, and validation.
Statistical risk models face issues of validity as unprecedented events and social phenomena occur. However, artificial intelligence (AI) and machine learning can assist models in maximising accuracy. By Tiziano Bellini, head of risk integration…
The authors put forward an explainable machine learning model predicting credit default using a real-world data set provided by a Norwegian bank.
Machine learning tool forecasts effect of shocks on implied volatility surfaces in minutes
In an exclusive Risk.net webinar, convened in collaboration with Cboe Global Markets, experts discussed the expanding world of equity options data, the rise of retail investment within it, and the technological challenges and opportunities associated…
Two methods to approximate complex functions in an explainable way are presented
With increasing regulatory scrutiny and market volatility, trading desks are seeking tools to help improve operational efficiency, streamline decision making and successfully manage risk. In this Risk.net webinar, viewers will learn about the front…
With a sharper focus, AI readers could help detect hidden exposures for investors
Machine learning models are seeing increasing demand across the capital markets spectrum. But how can firms improve their chances of gaining internal and regulatory approval for these type of models?
The authors propose a new method to design credit risk rating models for corporate entities using a meta-algorithm which exploits information embedded in expert-assigned credit ratings to rank customers.
In this Asia Risk webinar, experts in artificial intelligence (AI) and machine learning examined the growing applications being seen in the field and their merit in credit risk modelling
Joseph Simonian believes game theory and reinforcement learning could improve matters
The authors apply k-means clustering to low interest rate periods in order to analyze the equity hedging property of government bonds.
Banks and VCs believe inflation and rising interest rates will result in winners as well as losers
Predicting the odds of a recession, and how markets will respond, is still a step too far for machines
New model uses Bellman technique to learn general derivatives hedging strategies
The authors find that machine learning methods can generate satisfactorily performing credit score models based on data from the 90-days prior to the score date, where traditional models can perform poorly.
QTS has built a machine to show whether a strategy is likely to succeed or flop
A data-driven approach to computing expectations for the pricing and hedging of exotics