Behavioural modelling: NMDs and IRRBBView Agenda
Key reasons to attend
- Manage interest rate risk in the banking book (IRRBB) using behavioural models
- Explore various approaches to non-maturity deposit (NMD) modelling
- Acquire the practical skills to apply prepayment models
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About the course
As financial markets become more complex and regulations more stringent, accurately predicting customer behaviour and the impact on the balance sheet becomes increasingly vital. This course offers a thorough exploration of behavioural modelling, focusing on diverse NMD and prepayment models.
Participants will examine the crucial role of behavioural models in managing IRRBB and explore the driving factors behind these models. The course addresses key considerations and challenges in behavioural modelling, as well as providing participants with an understanding of model validation and stress-testing. An emphasis is placed on analysing the influence of these models on FTP decisions.
Attendees will leave the course with the practical skills to apply these models in ALM and enhance strategic decision-making in their organisation.
- Early-bird rate: save up to $800 per person by booking in advance (refer to the booking section for the deadline)
- 3-for-2 rate: save over $2,000 by booking a group of three attendees (applicable to this course)
- Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber* (use code SUB30)
- Season tickets: save over $1,000 per person by booking 10 or more tickets (available on selection of courses)
*The 30% subscriber reward discount is applicable only to current Risk.net subscribers. If this criteria is not met, we reserve the right to cancel the booking and issue an invoice for the correct rate. Discounts cannot be applied to already registered participants.
- Optimise asset-liability management (ALM) with NMD modelling
- Examine the role of behavioural models in funds transfer pricing (FTP)
- Apply models to diverse interest rate environments
- Analyse the impact of prepayment options on IRRBB
- Use behavioural models in stress-testing scenarios
- Gain insights into behavioural model validation
Who should attend
Relevant departments may include but are not limited to:
- Behavioural modelling
- Deposit modelling
- Risk management
- Liquidity risk
- Finance and strategy planning
- Product development
April 17–19, 2024
Time zones: Emea/Americas
- Introduction to interest rate risk in the banking book (IRRBB) and the role of behavioural models
- Non-maturity deposit (NMD) modelling: based and advanced modelling
- Applying simple NMD modelling
- Advanced deposit modelling and balance sheet optimisation
- Simple prepayment models
- Application of simple prepayment models
- Behavioural models and funds transfer pricing (FTP)
- Behavioural models validation and stress-testing IRRBB
Matteo Formenti Risk Learning Faculty
Asset-liability management expert
Dr Formenti was previously Head of the Fund Transfer Pricing team atUniCredit. He used to be a banking book trader for IRR management, and an expert of behavioural models used in ALM in Group Finance. He has also five years of experience modelling market (VaR, SVaR), counterparty credit risk (back-testing, stress testing and general Wrong Way Risk), liquidity risk (ALM behavioural models) and credit VaR (structural Merton model for ICAAP).
He is currently an external professor of asset management (Master and PhD) at Liuc University, Castellanza and of market risk at MPI (Politecnico, Milano). He has previous teaching experience at HEC, Paris and Tor Vergata, Rome. He is a frequent speaker for Risk Learning on the topic of behavioural modelling.
The Risk.net resources below have been selected to enhance your learning experience:
- A Guide to Behavioural Modelling
- Risk modellers navigate fearful new world of depositor behaviour
- Bank runs prompt rethink of IRRBB deposit models
- Estimating maturity profiles of non-maturing deposits
A Risk.net subscription will provide you access to these articles. Alternatively, register for free to read two news articles a month.