Author: Will Towning, Central Banks and Academia Programme Manager

 

What is CBDC Simulation?

 

The growth of Central Bank Digital Currencies (CBDCs), a fiat currency issued by a Central Bank in digital form, is undisputed. Nearly 70 different CBDCs are either under research, in development or in pilot phases across the world today. But despite featuring broadly in Central Bank strategies, the impact on the financial and economic system is still relatively unknown.

 

That’s where CBDC simulation comes in.

 

By creating an accurate, virtual model of a Central Bank’s payment system, it is possible to then examine the impact of introducing a CBDC by testing and simulating different scenarios to investigate the economic and financial stability implications.

 

Here, we explain how CBDC simulation works and why it’s a must-have solution for Central Banks seeking clarity on how to understand the impact of introducing a CBDC.

 

How does CBDC Simulation work?

 

CBDC simulation involves introducing a CBDC, under a range of optional configurations and migration scenarios, into a simulated model of a current financial or payment system.

 

The solution then produces a simulation of how the system could behave with the addition of a CBDC.

 

The CBDC simulation uses an agent-based model (best suited to modelling complex systems with large numbers of participants); to combine sub-models representing:

 

  • Agents
  • Payments instruments
  • Behaviours

 

Our approach is to build a simulator with many sub-models that have been validated by FNA research and by existing literature.

The combination of sub-models allows users to capture, in full, the dynamics and network structure of their environment in one visual dashboard.

 

Through the simulation dashboard users can then:

 

  • Tweak the decision-making parameters
  • Set up scenarios for payment migration
  • Gather results on potential outputs including:
        • The value and volume of CBDC transactions
        • End-of-day wallet balances
        • Consumer spending behaviours
        • Frequency of future CBDC wallet top-ups

 

A tailored solution

 

Central Banks operate in and oversee financial systems with different characteristics. A jurisdiction might have a bank-based or market-based system, and the payment environments may be largely digitally based or cash-based. There may also be higher or lower than average levels of cross-border payments.

 

The CBDC Simulation allows for the inclusion of each distinctive feature to mirror the financial system it represents, ensuring that outputs are representative of the reality to reliably support decision-making.

 

Example: Adjusting the demographics profile and associated payment behaviours or more granular payment methods.

 

Policy goals and priorities also vary between Central Banks. Some jurisdictions may have financial inclusion priorities, while others may be more concerned about the disintermediation impact. Through a network effect, Central Banks can use the CBDC Simulation to gather insight into different adoption scenarios or configure different tiered interest rates and translation fees to CBDC holding or stimuli policies.

 

Example: Examining the network effect of whether agents are more likely to adopt CBDCs if their peers are also using it.

 

Central Banks concerned with the implications on financial stability can interactively trigger stresses to the system during the simulation.

 

Example: Testing the impact of unusually high demand as a store of value or the impact of cyber-attacks.

 

 

But why does CBDC Simulation matter?

 

Introducing a CBDC presents significant risks – with potential impacts on issues ranging from monetary policy transmission to financial stability and the smooth functioning of payment systems.

 

In order to reduce these risks, Central Banks must clearly understand the implications of different CBDC configurations.

 

CBDC Simulation aims to provide Central Banks with clarity into the impact on commercial bank deposits, adoption rates and optimal CBDC design and configuration.

 

Example: Impact of disintermediation

With funds held outside traditional bank deposits, introducing a CBDC as a new form of base money creates risks of structural and cyclical disintermediation. By simulating outputs such as the potential amount of CBDC outstanding and payment migration from existing payment methods, Central Banks can gauge the scale of disintermediation. Users can then adjust the simulation’s configurations – interest rates, transaction fees, wallet size and stimuli policies – to model the impact on bank deposits.

 

Example: Payment behaviour of individual agents or groups

Using the simulation dashboard, Central Banks can visualize different scenarios and gather insights into what volumes they could expect and how frequently agents may need to top up CBDC wallets.

 

Example: CBDC in Retail

Retail volumes of CBDC transactions will be much larger than the current large-value payment systems. It is therefore essential to ensure a bank’s architecture can handle the chosen CBDC configuration. The CBDC Simulation solution can model the values and volumes of CBDC payments daily and intraday under various migration scenarios and inform decision-makers about the kind of throughput required. For example, an adoption scenario involving a high volume of debit and credit card payments and cash payments moving to a CBDC may require a more resilient CBDC payment architecture.

 

 

For many Central Banks only just embarking on their digital currency journey, the CBDC Simulation aims to clear a route through the uncertainties of this relatively new and unknown territory.

 

A better understanding of risks and implications through testing various scenarios allows for better, quicker decision making and ultimately, a more successful and safer introduction of digital currencies.

 

FNA has over 20 years of experience simulating payment systems and financial networks working extensively with Central Banks and financial authorities from many countries, including the majority of the G20 jurisdictions. If you would like to learn more about CBDC Simulation with FNA, please don’t hesitate to get in touch with the team.

More News

What is Suptech Analytics?

Author: Will Towning, Central Banks and Academia Programme Manager   What is Suptech Analytics?   Suptech is the application of innovative supervisory technology by central banks and financial authorities. It has traditionally focused on helping authorities collect and manage data more effectively, as well as digitise and automate certain processes. Such early iterations mostly support […]

FNA Talks Data Science in Economics and Finance with the Bank of England

FNA Talks Data Science in Economics and Finance with the Bank of England    Adrian Waddy, Data Consultant at Australian Prudential Regulation Authority and Developer at the Bank of England, joins host Adam Csabay to discuss his contribution to the Risk books publication, Data Science in Economics and Finance for Decision-makers. Adrian’s chapter, Implementing Big […]

Reconstructing and Stress Testing Credit Networks

By Amanah Ramadiah, Fabio Caccioli, Daniel Fricke Financial networks are an essential source of systemic risk. Unfortunately, detailed data on (direct and indirect) interactions between individual financial institutions is often unavailable, and the only the total aggregate position is known. To conduct a stress test, one must resort to network reconstruction methods to infer the […]
Copyright FNA © 2021 | Privacy Policy