RDP 2022-03: Macrofinancial Stress Testing on Australian Banks 1. Introduction

The failure of systemically important banks can have devastating consequences for economic activity and people's welfare. According to the Bank for International Settlements (BCBS 2010), financial crises have on average resulted in a cumulative loss of economic output (over several years) equivalent to over 60 per cent of annual GDP. Recessions associated with financial crises are deeper than those that occur without a financial crisis, and the recovery from financial crises is also much slower (Jordà, Schularick and Taylor 2013). Understanding how resilient banks are to economic shocks – and whether they require more capital to withstand ‘severe but plausible’ economic scenarios – is therefore of considerable importance to prudential regulators and central banks.

One of the tools that is often used to assess the resilience of banks is stress testing. Regulatory stress tests enable supervisors and central bankers to assess how bank capital ratios might evolve in response to certain economic scenarios. These models can range from simple back-of-the-envelope calculations to highly sophisticated models with hundreds of equations. Stress testing can also differ along the dimension of being ‘bottom-up’ or ‘top-down’. In bottom-up models, multiple banks are given a common scenario but use different models to forecast the resulting loss of capital, which are then aggregated. This is the approach most commonly used by prudential regulators, including the Australian Prudential Regulation Authority (APRA). In contrast, top-down (or ‘desktop’) models involve a modeller (or team of modellers) running one model for multiple banks, often with a common set of equations for all banks in which the results for each bank only vary based on their balance sheet structure. Both types of models are valuable tools to assess bank resilience, since they each have different strengths. Top-down models cannot typically include the complexity of credit loss modelling that can be captured in bottom-up models. However, they have some clear advantages over bottom-up models: they are very quick to run; allow the exploration of multiple scenarios; and permit the modeller to more easily incorporate contagion risk. Top-down models also enable the use of ‘reverse stress tests’, in which the output is the size of the shock that could create a certain amount of stress for the banking system, rather than a projected capital ratio for a given shock.

The Reserve Bank of Australia's Financial Stability Department has been developing a top-down stress testing model over recent years to strengthen its analysis of the resilience of the Australian banking system. Our approach draws on many of the characteristics that the International Monetary Fund includes in the models it uses to assess financial sector vulnerabilities (Adrian, Morsink and Schumacher 2020). We also employ many of the features included in other central banks' stress testing models, tailored to the characteristics of Australian banks.[1] Most importantly, our model is driven by three principles:

  1. A focus on the systemic aspects of risks to banks' balance sheets, as well as features such as contagion across banks. This reflects that one of the key advantages of top-down models compared with bottom-up ones is the ability to capture interaction effects.
  2. A philosophy of not over-engineering the model and keeping it relatively simple and transparent. To some degree this approach is forced by the absence of relevant historical periods from which to empirically estimate coefficients, meaning we typically have to calibrate them from various available sources. However, this philosophy more deeply reflects our desire for the model to produce insights that can be easily interpreted and understood, rather than seeking potentially precise estimates of the loss of capital under certain scenarios, and these are more easily obtained when the model is kept transparent.
  3. An ability to easily develop the model over time, as understanding of banking system risk and the ways in which to model it evolve. This can be particularly useful when new features are required to better understand a changing economic environment.

The model is separate, but closely related to one developed by Brassil, Major and Rickards (2022); in fact, many of the calibrations in that model are deliberately chosen to match those in our macrofinancial model. However, these two models have quite different motivations that influence the way in which they have been developed. Most notably, Brassil et al''s model is designed to improve the accuracy of macroeconomic forecasts during times of banking sector stress, while the focus of this model is the effect of economic shocks on the banking sector. Accordingly, our treatment of the macroeconomy is significantly less rich than Brassil et al, but our modelling of the banking sector is much richer. This different focus has underpinned their joint development along very related, but slightly different paths, and the two models should be viewed as complementary. The choice of which is most suitable for any particular purpose therefore depends on the question to be addressed.

This paper documents the key features of the model, including plans for how it will most likely evolve in the future. We discuss the broad structure of the model in Section 2, before documenting how the model estimates credit losses for various portfolios in Section 3 and how capital and total assets evolve in Section 4. Section 5 then sets up various ways in which the model captures systemic risk (that is, risk arising from the interaction between banks as losses mount). For readers that are only interested in understanding how our model is used in practice, Section 6 explains how the model was used to quickly produce policy insights during the COVID-19 pandemic. Section 7 provides some avenues for future development.


These include the Bank of England's RAMSI model (Burrows, Learmonth and McKeown 2012), the European Central Bank's BEAST model (Budnik et al 2020), the US Federal Reserve's FLARE model (Correia et al 2020), the Bank of Canada's FRIDA model (MacDonald and Traclet 2018) and the Bank of Japan's FMM model (Bank of Japan 2020). [1]