Economic and Policy Uncertainty
Sources: Baker, Bloom, and Davis 2016; Husted, Rogers, and Sun 2020; IMF, Global Data Source and International Financial Statistics databases; Organisation for Economic Cooperation and Development, Main Economic Indicators database; and IMF staff calculations. Note: Panel 1 shows values on a real economic uncertainty index for the United States and other economies based on the approach in Ludvigson, Ma, and Ng (2021). The index is standardized over its historical mean (January 1990 through September 2023) and averaged across different periods. Non-US real economic uncertainty index is a GDP-weighted average across non-US economies. In panel 2, “Global economic policy uncertainty index” is a GDP-weighted index from Baker, Bloom, and Davis (2016) averaged over the indicated time period. For additional details on the data sources, see Online Annex 2.1. The monetary policy uncertainty indices pertain to the United States and are compiled by Baker, Bloom, and Davis (BBD, 2016) and Husted, Rogers, and Sun (HRS, 2020), respectively.
Increased macroeconomic uncertainty can profoundly affect macrofinancial stability. High macroeconomic uncertainty can potentially affect macrofinancial stability—or systemic risk—through three key channels.3 First, it can exacerbate downside market tail risks in the event of an adverse shock (the market channel). Second, it can delay private sector consumption and investment decisions, slowing economic activity and raising credit risks for financial institutions that can in turn trigger an adverse macrofinancial feedback loop (the real channel). And third, it can reduce the supply of domestic credit by financial institutions by exacerbating challenges in determining the creditworthiness of new borrowers (the credit channel). These three channels can interact and mutually reinforce each other, amplifying the effect of macroeconomic uncertainty on macrofinancial stability.4
3Macrofinancial stability is defined in terms of systemic risk, that is, the risk of disruption to the financial system that can have serious negative consequences for the real economy, and is measured by downside tail risks to future real GDP growth. 4While studies using well-known measures of macroeconomic uncertainty generally find it to be negatively associated with asset returns and volatility (Asgharian, Christiansen, and Hou 2015; Bali, Brown, and Tang 2017), the effect could also be positive. For example, hightech revolutions promising future productivity gains can be a source of positive or “good” uncertainty, while geopolitical conflicts can be considered as a source of negative or “bad” uncertainty (Bloom 2014; Segal, Shaliastovich, and Yaron 2015; Dew-Becker and Giglio 2023).
Macroeconomic uncertainty can interact with potential vulnerabilities in the real and financial sectors to magnify the effects of adverse shocks. For example, in the presence of high levels of public debt relative to GDP, investors may react more strongly to an expansionary fiscal shock when uncertainty regarding the economic outlook is high instead of low, leading to a sharp increase in sovereign bond yields (see the October 2024 Fiscal Monitor). Periods of high macroeconomic uncertainty may also make the corporate debt market more vulnerable to adverse shocks, particularly when leverage in the corporate sector is high or credit spreads are perceived by investors to be overly compressed. Equity markets are also likely to experience larger price corrections in the face of adverse shocks when uncertainty about the macroeconomic outlook is high and valuations are stretched relative to fundamentals.5 These considerations may be particularly pertinent at the current juncture as, along with macroeconomic uncertainty, macrofinancial vulnerabilities remain elevated (Online Annex Figure 2.1.1).
The effect of macroeconomic uncertainty can spill over across borders. Global financial and real interconnectedness implies that increased macroeconomic uncertainty can have cross-border implications through the aforementioned channels. For example, an increase in macroeconomic uncertainty that imposes losses on investors in a particular region may force them to sell assets in other countries, leading to large asset price declines and triggering international financial contagion.6 Similarly, by reducing domestic consumption and investment, macroeconomic uncertainty can weaken the demand for imports, raising downside risks to economic activity in trading partner countries.
Financial variables may not fully span macroeconomic uncertainty. Existing approaches to assess macrofinancial stability typically consider selected financial indicators, including those related to financial market uncertainty (for example, the Chicago Board Options Exchange Volatility Index [VIX]), as relevant variables in frameworks to assess systemic risk (Adrian, Boyarchenko, and Giannone 2019; Adrian and others 2019). However, financial indicators may not fully reflect macroeconomic uncertainty, making it useful to consider it in frameworks to assess systemic risk and predict tail risks to markets and economic activity.7 This may be particularly relevant for countries with less developed financial markets or during episodes of “macro-market disconnect”—that is, when macroeconomic uncertainty is high and financial market volatility (realized and implied) is low.8 Against this background, this chapter examines risks to macrofinancial stability posed by macroeconomic uncertainty. The chapter first lays out a simple conceptual framework for discussing the main
6Bond and stock market volatility tend to be positively correlated across major economies, and this correlation seems to have increased since the pandemic (Online Annex 2.1), suggesting that stress in asset markets can spread quickly across the financial system. 7See, for example, Valkanov and Zhang (2018) and Dew-Becker and Giglio (2023). Online Annex 2.2 shows that financial variables explain about 80 percent of the variation in commonly used measures of macroeconomic uncertainty for advanced economies like the United States, and 40 to 50 percent of the variation in those for major emerging markets such as Brazil. This is because available financial instruments may not fully hedge important risks facing households and firms—for example, those related to housing markets (Shiller 2003, 2013; Benford, Ostry, and Shiller 2018). 8Several factors can drive macro-market disconnects, including investor perception that future policy reactions will protect against downside market risks. Bialkowski, Dang, and Wei (2022) show that low-quality political signals, higher divergence in opinions among investors, and strong equity market performance drive disconnects between the VIX and US economic policy uncertainty. Todorov and Vilkov (2024) note the role played by hedging of covered calls in keeping the VIX at a low level in recent years.
channels through which macroeconomic uncertainty can undermine macrofinancial stability, measured by downside risks to real GDP. It then uses panel data from a sample of 43 advanced and emerging market economies since 1990 (or the earliest year for which data are available) to empirically address three key questions.9 First, does macroeconomic uncertainty help predict downside risks to output? Second, how does macroeconomic uncertainty interact with macrofinancial vulnerabilities to affect downside risks to output? Third, does the effect of macroeconomic uncertainty spill over across borders to affect downside risks to economic activity in a country’s major financial and trading partners? The chapter then discusses policy options to mitigate the risks posed by high macroeconomic uncertainty.
To assess the downside risk to future economic activity from macroeconomic uncertainty, the chapter extends the growth-at-risk (GaR) framework. Since the global financial crisis, significant progress has been made in systemic risk analytics. The GaR framework (Adrian, Boyarchenko, and Giannone 2019) has become an operational cornerstone in this regard, providing a quantitative tool to assess the effect of financial conditions on downside tail risks to real GDP growth.10 The chapter builds on this framework in two dimensions. First, it augments the GaR model with measures of macroeconomic uncertainty to examine if these are associated with downside tail risks to real GDP growth. In this context, the chapter considers three types of commonly used macroeconomic uncertainty measures—those based on (1) the accuracy and dispersion of forecasts for key macroeconomic variables, (2) domestic policies, and (3) geopolitical tensions. Second, the chapter implements the augmented GaR framework using machine learning tools, in addition to the standard panel quantile regressions, to exploit their advantages in prediction and improve the forecasting of downside tail risks to future GDP growth.11
9The cross-country sample coverage varies across exercises depending on data availability. See Online Annex 2.1 for information on countries included in the sample and the data sources. 10Downside risks to future GDP growth are typically captured by the 5th or 10th percentile of the distribution, while financial conditions are proxied by a composite indicator of risky asset prices (such as equity and corporate bond returns, real house price growth, etc.) and measures of financial uncertainty (such as the VIX). 11Machine learning models have gained popularity for forecasting economic and financial variables as they can accommodate many predictors and complex, nonlinear relations between variables (Gu, Kelly, and Xiu 2020; Coulombe and others 2022; Lenza, Moutachaker, and Paredes 2023).
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