RDP 2021-03: Financial Conditions and Downside Risk to Economic Activity in Australia 1. Introduction

Financial stability risks are difficult to quantify and hard to map to economic outcomes. But for central banks with a financial stability mandate, the issue is crucial. For example, when inflation is contained and financial conditions are expansionary, flexible inflation-targeting central banks need to weigh the near-term benefits of easier monetary policy and stronger near-term growth prospects against the costs of doing so: specifically, the build-up of financial vulnerabilities that could pose risks to future growth and employment. The ability to quantify and communicate financial stability risks is also essential for other policymakers, such as prudential regulators and governments, who have a broader range of policy tools available to them to mitigate financial stability risks.

In addition to difficulties associated with identifying financial stability risks, policymakers face the added challenge of quantifying what the economic costs of financial instability might be. In part, this is because it is not easy to disentangle economic and financial risks that often coincide.[1] Much of the literature on this topic focuses on estimating the costs of financial crises after they have occurred. Although there is considerable variation in the way economic costs are measured, there is a broad consensus that financial crises are followed by economically significant and prolonged declines in output and output growth (see Bordo and Meissner (2016) for a comprehensive review).

It is more difficult, however, to estimate the expected costs of financial instability before any instability has been realised. Fundamentally, this requires policymakers to form an assessment of both the estimated economic cost in the event that a crisis were to occur and the estimated probability of the crisis occurring in the first place. A risk-neutral policymaker would simply multiply the expected economic cost of a crisis by the estimated probability of that crisis taking place and focus on the product of these two variables. But in practice, central banks are not risk neutral. Risk-averse policymakers will seek to reduce the probability of extremely bad outcomes occurring, and so the expected economic cost of a crisis will also be a variable of interest in its own right (Cecchetti 2006). The higher the expected cost, the more likely it is that the central bank will judge it to be optimal to sacrifice some amount of expected output so as to avoid a potentially catastrophic scenario, even if the event is not considered to be particularly likely.

While it might be tempting to rely on changes in policymakers' central forecasts to assess changes in the expected economic cost of a crisis, this is likely to be misleading in practice. This is because this approach relies on the assumption that the distribution of possible economic outcomes simply shifts higher or lower over time, without changing shape. The reality is more complex. Financial stability risks are highly nonlinear: for example, asset prices typically fall at a faster rate during post-bubble busts than they rise during the preceding booms. The global financial crisis (GFC) showed that financial stress at a single financial institution can, under certain conditions, spread quickly through the network of interbank exposures (Haldane 2009). This implies that in adverse scenarios, the distribution of economic outcomes is likely to not only shift lower, but also develop a longer left tail. This provides a strong argument for estimating expected economic costs of crises by modelling the relationship between the entire distribution of a chosen measure of financial stability and the entire distribution of economic outcomes.

The ‘growth-at-risk’ (GaR) framework was developed by Adrian, Boyarchenko and Giannone (2019) to do precisely this. The idea behind GaR is similar to ‘value-at-risk’ (VaR), which is a popular measure of risk used in finance. The concept builds on Manzan (2015) and links current financial conditions to the distribution of future economic outcomes. Specifically, it aims to quantify the magnitude of expected losses in economic activity caused by financial conditions. In practice, it achieves this by modelling the lower (left) tail of the distribution of future economic outcomes over time.

Using US data, Adrian et al (2019) show that the distribution of future GDP growth evolves over time, with the left tail (i.e. downside risk) becoming longer as prevailing financial conditions become more restrictive. The GaR approach has subsequently been applied by the Bank of Canada (Duprey and Ueberfeldt 2018), the Bank of Japan (Bank of Japan 2018), the Board of Governors of the Federal Reserve System (Kiley 2018; Loria, Matthes and Zhang 2019), the Bank of England (Aikman et al 2018) and the European Central Bank (ECB) (European Central Bank 2018). The International Monetary Fund (IMF) is also an active contributor in this area, with the GaR approach now included in its macrofinancial surveillance toolkit.

Our work contributes to this literature by developing a GaR model for Australia using a novel quantile regression approach known as ‘quantile spacings’. In addition to focusing on downside risk to aggregate GDP, we also examine other more granular indicators of macroeconomic outcomes – namely, household consumption, non-mining business investment, employment and the unemployment rate. We do this because these more granular variables have important links to financial stability in their own right. For example, excessive household debt can weigh on household consumption (Price, Beckers and La Cava 2019) and the investment of firms with excessive debt is more sensitive to interest rates (Gebauer, Setzer and Westphal 2018; Hambur and La Cava 2018). The financial conditions faced by firms can also affect their employment decisions. For example, Chodorow-Reich (2014) finds that tighter credit conditions for small businesses cause declines in employment. More generally, weak labour market outcomes could have adverse feedback effects on financial stability by creating debt-servicing challenges for some households and leading to higher rates of non-performing loans at financial institutions.

In order to implement the GaR framework for Australia we need to develop a financial conditions index (FCI) to provide a summary measure of prevailing financial conditions. Following the GFC, FCIs emerged as a potentially useful metric of financial conditions, based on the work of Hatzius et al (2010). FCIs are constructed as a weighted average of a broad range of indicators, including asset prices, credit, money, interest rates and the exchange rate. Subsequently, many policy institutions have constructed FCIs to regularly monitor financial conditions, including the IMF, a number of Federal Reserve Banks (Brave and Butters 2010), the ECB, the Bank of England (Kapetanios, Price and Young 2017) and the Bank of Canada (Gauthier, Graham and Liu 2004). One advantage of using a composite indicator such as an FCI in the GaR framework is that it is allows financial conditions to affect risk to economic activity through a variety of channels.

Similar to other economies, we find that our FCI helps to explain downside risks to future GDP growth in Australia at horizons of both one quarter and one year. We also find strong relationships between the FCI and risks to our 2 labour market indicators. However, the FCI is surprisingly of relatively little use in explaining downside risk to household consumption or business investment. While the GaR approach is a flexible and parsimonious framework, there are 2 key caveats to note.

First, it is reduced form in nature and is not well suited to identifying causal relationships. Second, there is estimation uncertainty for some of the results.

We start by describing the methodology used to implement the GaR framework in Australia, including the construction of the FCI, in the next section. Further technical details related to the FCI as well as some additional results are provided in the appendices.


In a recent paper, Falconio and Manganelli (2020) suggest that controlling for economic risk eliminates the direct effect of financial shocks on the real economy. This suggests that financial shocks instead have an indirect effect on the real economy via their effect on economic uncertainty. [1]