NMD Workshop: Miami Dec 2019

Non-Maturity Deposit Modeling:  Balance Sheet Management Considerations

Overview

In this 2-day workshop, we begin with an exploration of the key role behavioral assumptions around non-maturity deposits (NMDs) play in the measurement and management of interest rate risk (IRR), liquidity risk (LR) and product profitability.

Banks and credit unions are prone to be cavalier or lazy with the assumption setting process for NMDs; some institutions arbitrarily assign durations and liquidity values that are not robust to any material market stress, while others hand the modeling problem to an internal or external “quant” and then just accept whatever values come out of the model.  Neither approach acknowledges the dynamic nature of NMD values or the role the firm itself, i.e. product management, plays in influencing product behaviors.

It is clear that the calibration process that David has designed is at least as important as the model itself; I now believe that this process is mandatory if you want to truly understand the behavior of one’s deposits and how they can positively influence the measurement and management of both risk and profitability. – ALM/FTP Manager, Pinnacle Financial Partners

In the workshop, I demonstrate a behavioral model for NMDs I designed.  It produces monthly ALM feeds which are derived from vintage-level decay functions, rate betas and a dynamic measure of balance volatility.  The model also leverages has an integrated FTP engine which is used to calculate FTP rates and spreads over the full historical time series of data used to calibrate the model, the current position as well as future periods that may be considered for budgeting and forecasting exercises. By computing FTP rates directly within the behavioral model, deposit gatherers have a vested interest in considered how product behaviors (over which they have some control) drive the crediting rate they receive.  They come to learn that the FTP crediting rate is not arbitrary; it reflects the economic value that is generated by each product.

NMD Workshop: London Nov 2019

Non-Maturity Deposit Modeling:  Balance Sheet Management Considerations

Overview

In this 2-day workshop, we begin with an exploration of the key role behavioral assumptions around non-maturity deposits (NMDs) play in the measurement and management of interest rate risk (IRR), liquidity risk (LR) and product profitability.

Banks and credit unions are prone to be cavalier or lazy with the assumption setting process for NMDs; some institutions arbitrarily assign durations and liquidity values that are not robust to any material market stress, while others hand the modeling problem to an internal or external “quant” and then just accept whatever values come out of the model.  Neither approach acknowledges the dynamic nature of NMD values or the role the firm itself, i.e. product management, plays in influencing product behaviors.

It is clear that the calibration process that David has designed is at least as important as the model itself; I now believe that this process is mandatory if you want to truly understand the behavior of one’s deposits and how they can positively influence the measurement and management of both risk and profitability. – ALM/FTP Manager, Pinnacle Financial Partners

In the workshop, I demonstrate a behavioral model for NMDs I designed.  It produces monthly ALM feeds which are derived from vintage-level decay functions, rate betas and a dynamic measure of balance volatility.  The model also leverages has an integrated FTP engine which is used to calculate FTP rates and spreads over the full historical time series of data used to calibrate the model, the current position as well as future periods that may be considered for budgeting and forecasting exercises. By computing FTP rates directly within the behavioral model, deposit gatherers have a vested interest in considered how product behaviors (over which they have some control) drive the crediting rate they receive.  They come to learn that the FTP crediting rate is not arbitrary; it reflects the economic value that is generated by each product.

FTP Workshop: Atlanta Nov 2019

Funds Transfer Pricing:  The Key to Effective Risk and Profitability Management

Overview

In this 2-day workshop, we review lessons learned from the last several business cycles, including what banks missed and why they missed it.  Drawing on these lessons, I describe how risk and profitability management are one in the same problem; they cannot be treated as separate and distinct, else the result will be conflicting stories of how the financial institution (FI) makes money.

Following a brief discussion on the sources of interest rate risk (IRR) and liquidity risk (LR), I show how a well-functioning funds transfer pricing (FTP) process immunizes lenders and deposit gatherers against these risks.  This is a key motivation for lenders and deposit gatherers to embrace FTP.  Without it, they are exposed to risks over which they have no control.

I provide a detailed description of the mismatch center and explain how it should contain all IRR- and LR-related earnings and earnings volatility.  In a well-functioning FTP framework, the earnings risk profile of the FI and the earnings risk profile of the mismatch center should be identical regardless of the risk profile of the FI.

I then walk through several balance sheet examples of IRR and LR to demonstrate why a financial institution’s funding curve must be constructed as the sum of a basis-adjusted swap curve plus the incremental cost of its senior unsecured debt (this is the FI’s credit spread relative to swaps).

The feedback from the FTP Conference participants was nothing short of excellent. Several product managers have reported that the workshop has positively influenced their decisions around which deposit products to promote and how they will price and manage them to maximize their value to their business units AND to the bank.  – Director, Funds Transfer Pricing, Royal Bank of Canada

Given that non-maturity deposits (NMDs) make up a material source of funding at most FIs, I describe a method of calculating the FTP rates on NMDs that follows logically from their IRR and LR cashflow attributes.  This approach compels deposit gatherers to consider how product behaviors (over which they have some control) drive the crediting rate they receive, i.e. the FTP rate is not arbitrarily imposed on them.  This understanding produces significantly more accurate measures of IRR and LR for the FI.

ALM Workshop: Santiago Nov 2019

Asset Liability Management:  Moving Beyond the Model

Overview

In this 2-day workshop, we review lessons learned from the last several business cycles, including what banks missed and why they missed it.  Drawing on these lessons, I describe how risk and profitability management are one in the same problem; they cannot be treated as separate and distinct, else the result will be conflicting stories of how the financial institution (FI) makes money.

Following a discussion on the sources of interest rate risk (IRR) and liquidity risk (LR), I describe key metrics and computations used to quantify these risks.  I then show how a well-functioning funds transfer pricing (FTP) process immunizes lenders and deposit gatherers against these risks.  I provide a detailed description of the mismatch center and how it should contain all IRR- and LR-related earnings and earnings volatility (FYI – this means that mismatch center earnings are highly unlikely to be zero.)  This creates a mechanism by which the effectiveness of ALCO can be judged.

David’s understanding of ALM and FTP is exceptional, which is clearly evident in the way he explained the seemingly complex and difficult topics in an easily understandable way. – Head of ALM, European Public Sector Funding Agency

Given that non-maturity deposits (NMDs) make up a material source of funding at most FIs, I describe a dynamic approach to modeling NMDs that incorporates the calculation of their FTP rates.  This has the unique benefit of compelling deposit gatherers to consider how product behaviors influence their IRR and LR characteristics; this understanding is embodied in the calculation of the FTP rates and produces significantly more accurate measures of IRR and LR for the financial institution as a result.