MARTIN Gets a Bank Account: Adding a Banking Sector to the RBA's Macroeconometric Model

Abstract

We add a simplified banking sector to the RBA's macroeconometric model (MARTIN). How this banking sector interacts with the rest of the economy chiefly depends on the extent of loan losses. During small downturns, losses are absorbed by banks' profits and the resulting effect on the broader economy is limited to that caused by the lower shareholder returns (which is already part of MARTIN). During large downturns, loan losses reduce banks' capital, and banks respond by reducing their credit supply. This reduction in supply reduces housing prices, wealth and investment; thereby amplifying the downturn (which leads to further losses). Our state-dependent approach is a significant advance on the treatment of financial sectors within existing macroeconometric models. Having a banking sector in MARTIN allows us to explore important policy questions. In this paper, we show how the effectiveness of monetary policy depends on the state of the economy. During large downturns, monetary policy is more effective than usual because it can reduce loan losses and therefore moderate any reduction in credit supply. But at low interest rates, the zero lower bound on retail deposit interest rates reduces policy effectiveness. We also investigate how one of the more pessimistic economic scenarios that could have resulted from COVID-19 might have affected the banking sector, and subsequently amplified the resulting downturn.

Macrofinancial Stress Testing on Australian Banks

“…25 Banks under this threshold for reinvestment restrict their asset growth by default; assets for banks in this situation are grown by just the amount of retained earnings without the usual leverage. Unlike in Brassil et al (2022), we do not explicitly model credit demand or lending spreads (which could be used to imply demand for a given quantum of credit growth), given our different aims to Brassil et al and the simple nature of our macroeconomic block. Accordingly, the implicit assumption in our model is that loan demand softens due to the macroeconomic shock that also lowers bank capital ratios, but that the volume of loans demanded does not actually shrink.…”

“…The magnitude of this effect is highly uncertain, given the caveats around calibrating the appropriate coefficients. However, the direction of the result -that capital ratios are lower when banks restrict credit supply during times of stress -is often found in the literature, including in Brassil et al (2022). An understanding of the potential for this also underlies the common message from regulators globally during the COVID-19 pandemic that banks should choose to expand lending to support the economy, even if that required them to enter their CCB, rather than seek to restrict lending in an attempt to keep their capital ratios above the CCB.…”

“…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.…”

The Consequences of Low Interest Rates for the Australian Banking Sector

“…The apparent stability of spreads in Australia therefore suggests the pass-through of monetary policy to lending rates may have been more muted than what would be predicted by the literature. Previous estimates suggest that if the cash rate were to fall 25 basis points from zero per cent, the deposit lower bound would cause only 20 basis points of this to feed through to lending rates (Brassil, Major and Rickards 2022).…”

“…With the new banking sector addition to MARTIN (Brassil et al 2022) -henceforth, BA-MARTIN -it is possible to investigate whether a reversal rate can exist in Australia, and if it can, what determines its existence. BA-MARTIN is more detailed than the highly stylised model of Brunnermeier and Koby (2018), thereby enabling exploration of both the reversal rate theorised by Brunnermeier and Koby and additional channels that might lead to a reversal rate.…”