A Guide to Behavioural Modelling
First published:
ISBN: 978-1-78272-404-9
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A Benchmark Framework for NMDs: An Application
Antonio Castagna, Antonio Scaravaggi and Bernardo Rapagnetta
Introduction
Insights on Banks’ Recourse to Behavioural Models from a Focused IRRBB Stress Test
Implementing Regulatory Guidance on IRRBB Behavioural Models: Challenges and Opportunities
The Stakeholders of Interest Rate Risk Behavioural Models
Governance of Behavioural Models
The Nature of IRRBB and Typical Metrics Employed
A Framework for Developing NMD Behavioural Models
The Literature on NMD Behavioural Models
Interest Rate Risk of Non-maturity Bank Accounts: From Marketing to Hedging Strategy
NMDs and IRRBB: A Methodological Proposal for a Behavioural Model
NMD Modelling: A Financial Wealth Allocation Approach
A Benchmark Framework for NMDs: An Application
NMD Behavioural Models Used in Marketing
The Validation of NMD Behavioural Models
The Choice of Maturity Profile in NMD Behavioural Models
Acknowledging the Elephant in the Room: The Mismatch Centre
Prepayment Risk Modelling for ALM, Finance and FTP: A Survival Model
Modelling of Prepayment on Fixed Rate Residential Mortgages: A Logistic Regression Approach
A Simple Approach to Modelling Prepayment Events
Integrating Credit Risk within the ALM Framework
Modelling Committed Credit Lines
Accounting of the Sight Deposit and Hedging
This chapter presents an application of the stochastic risk factor approach to model the non-maturing deposits, and sketches a framework to assess the related expected profitability and the liquidity and duration risks of a bank. More specifically, we calibrate the model to artificial data provided by the authors for the sight deposits of two different banks. This artificial set-up has been defined to highlight the flexibility required for behavioural models. These models should be able to describe depositors’, banks’ and market’s characteristics. For this purpose this approach is applied to different customer ’s segments to capture and explain their different behaviour and sensitivities.
The modelling of deposits and non-maturing liabilities is a crucial task for the liquidity and ALM of a financial institution. It has become even much more momentous since the liquidity crisis that struck the interbank money market in 2008–09.
Typically, the ALM departments of banks involved in the management of interest rate and liquidity risks face the task of forecasting deposit volumes so as to design and implement consequent liquidity and reinvestment strategies. Moreover, deposit
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