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By the end of this month banks need to have implemented Credit Spread Risk in the Banking Book. We notice that many banks are still developing methods to measure CSRBB amidst limited regulatory guidance. To validate or challenge your internal discussions, our colleague Maurits van den Oever has investigated a simple and intuitive method to quantify systemic credit spread shocks. He calculates shocks for government bonds with different ratings and maturities which can be easily implemented in your IRRBB framework. Interested in further expanding your CSRBB framework in 2024? Then don’t hesitate to get in touch with our IRRBB lead Corné Ruwaard to exchange thoughts.
Can we ensure fairness and explainability for AI and ML in insurance? Tools and techniques for ensuring explainable ML, bias measurement and mitigation.
Large Language Models are reshaping Financial Services.
Risicobeheer bij de transitie naar WTP en de rol van de sleutelfunctie daarbij.
The aim of this paper is to extend Variational AutoEncoders (VAE) to allow for heavy tailed distribution of the latent space and apply them to the problem of market risk of large portfolios.
This paper investigates the relationship between news sentiment and corporate bond yield spreads, including assymmetry and sentiment-based bond investing.
In this paper, we investigate the interaction between Refinitiv ESG scores of firms and the performance of corporate bonds issued by these firms. We provide a rather straight-forward analysis of the relationship between ESG scores and corporate bond yields.
This white paper addresses the question of the value of alternative data in the investment process with the Refinitiv News Analytics Data.
In November 2021, we organized a roundtable regarding the application of expert judgment and overrides in behavioral modeling of prepayments and savings, including interest rates, COVID-19, and elasticity of savings.
In this paper, we discuss how reinforcement learning can be successfully applied to hedging of options and show that these machine learning algorithms can “transfer” knowledge obtained from simulated data to the real-world option trading environment.