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SARON hedging strategies

Hedging of SARON exposure, Part II

On 5 March 2021, the Financial Conduct Authority (FCA) announced the official dates of the cessation and loss of representativeness of the LIBOR rates. As a result, 31 December 2021 will be the last day on which the CHF LIBOR will be published. During the last months, a multitude of informative documents were provided by the Alternative Reference Rates Committee (ARRC) and the International Swaps and Derivatives Association (ISDA). As a follow-up from the previous article Hedging of SARON exposure, Part I: Explanatory power of SARON term and actual rates, this article assesses the different compounding methodologies with respect to hedging strategy.

Explanatory power of SARON term and actual rates

Hedging of SARON exposure, Part I

On 5 March 2021, the Financial Conduct Authority (FCA) announced the official dates of the cessation and loss of representativeness of the LIBOR rates. As a result, 31 December 2021 will be the last day on which the CHF LIBOR will be published. During the last months, a multitude of information were provided by the Alternative Reference Rates Committee (ARRC) and the International Swaps and Derivatives Association (ISDA). This article aims to provide an analysis of the prediction power of term and in advance rates.

IBOR transition: fallback solutions in illiquid markets

At the end of this year, the LIBOR we currently know will be discontinued. For some currencies, the calculation methodology will be adjusted, while others will move to a brand new or alternative risk-free rate (RFR). This also holds for the dollar LIBOR, which will be replaced by the Secured Overnight Financing Rate (SOFR). However, some currencies use the USD LIBOR as a basis for their current reference rates, mainly due to liquidity concerns. Therefore, these FX implied rates face an additional challenge.

Machine learning in risk management

Machine learning (ML) models have already been around for decades. The exponential growth in computing power and data availability, however, has resulted in many new opportunities for ML models. One possible application is to use them in financial institutions’ risk management. This article gives a brief introduction of ML models, followed by the most promising opportunities for using ML models in financial risk management.

“It all depends on expert opinion, data, and common sense”

Two ING experts share their views on deposit modelling

The low interest rate environment has faced banks with structural changes in customer behavior and converging products such as savings and current accounts. ING, one of Europe’s largest players in the savings market and a long-term client of Zanders, has positioned itself as one of the frontrunners in this environment. We sat down with Tom Tschirner (head of market risk at ING Germany) and Maarten Hummel (financial risk officer at ING Group) to gather their view on modeling and balance sheet management after these structural shifts.