Session 17: The Macroeconomics of Uncertainty and Volatility

This paper proposes a novel framework for analyzing the risks surrounding the public debt outlook, the “Debt-at-Risk.” It employs a quantile panel regression framework to assess how current macro-financial and political conditions impact the entire spectrum of possible future debt outcomes. Many of these factors—including financial conditions and economic variables such as initial debt and GDP growth—predict both the expected level and the uncertainty of future debt, implying pronounced variations in risks, especially in the upper tail of the distribution. By combining the roles of these factors, we find that in a severely adverse scenario—the 95th percentile of the future debt distribution, or debt-at-risk—global public debt could be approximately 20 percentage points higher than currently projected. The magnitudes and sources of debt risks vary over time and across countries, with high initial debt amplifying the effects of economic and financial conditions on debt-at-risk. Furthermore, empirical estimates indicate that debt-at-risk is a key variable for predicting fiscal crises.

Under areal options framework, we show that healthy firms become reluctant to invest and disinvest in anticipation that uncertainty induces creditors to convert their defaulting industry rivals into zombies. We validate our theory using dynamic, industry-specific estimates of expected uncertainty-induced zombification together with loan contract-level data. Empirically, higher expected uncertainty-led rival zombification prompts healthy firms to reduce their costly-to-reverse capital investment and disinvestment, hiring, and establishment-level openings and closures (intensive and extensive margins are affected). Uncertainty-led rival zombification further depresses healthy firms’ productivity and market valuations. We confirm those dynamics using granular, near-universal data on the asset allocation decisions of global shipping firms. Our results reveal nuanced effects on creative destruction: while healthy firms’ asset allocation slows down, their innovation activity accelerates. Our findings highlight a novel channel through which uncertainty shapes industry capital accumulation, distorting firms’ real policies and performance.

Government announcements about future actions can influence agents’ economic choices today. How do beliefs about future government policy affect households’ borrowing, spending and debt paydown behavior? We study this question through the lens of student loan forgiveness in the United States, which following electoral promises, was announced in 2022 but never implemented due to judicial rulings. We conduct a customized information provision experiment embedded in a survey eliciting real-time beliefs about future debt forgiveness and repayment, which we link to credit bureau data, employment verification data, and nondurables consumption. Borrowers who are more optimistic about forgiveness reduce payments on student loans and increase non-durable spending, but postpone durable spending while they wait for uncertainty to resolve. Our results provide micro-evidence on the role of policy uncertainty in household decisionmaking, and have implications for government announcements and commitment policy.

This paper develops daily market-wide sentiment and attention indexes derived from millions of posts across major investor social media platforms. We find that sentiment extrapolates from past market-wide returns and exhibits a strong reversal. In contrast, attention predicts negative returns as a continuation of previous trends. The two indexes have distinct predictions for aggregate trading: abnormal trading rises when sentiment is low and attention is high. To identify the drivers of attention and sentiment, we use a shock to data sharing networks: We find sentiment spreads through real firm connections while attention does not. Moreover, attention rises after abnormally high trading, while sentiment rises after abnormally high returns. This extrapolative return pattern is asymmetric, primarily driven by negative market jumps. These findings provide new evidence on the daily market dynamics of sentiment and attention.

We study the asset pricing implications of geopolitical tensions using nearly 100 years of data. Leveraging widely adopted news-based geopolitical risk indices, we find that geopolitical threats (GPT) and acts (GPA) have markedly different effects. GPT aligns closely with investors’ risk perceptions from ratings and surveys and predicts long-run consumption disasters. It is priced across individual US stocks, equity anomalies, and international equity and bond indices. GPT also captures time variation in country- level equity premia and firm investment. In contrast, GPA exhibits weaker and less stable links to risk perceptions, risk premia, and investment. We demonstrate that these findings are consistent with an Epstein-Zin investor facing time-varying disas-ter probabilities. Importantly, our results are incremental to news-based risk indices capturing war-related discourse, market volatility, economic and trade policy, and general macro-financial uncertainty. Overall, our findings underscore the importance of forward-looking measures like GPT for understanding how news-based uncertainty affects asset prices.

Though geopolitical risks can dampen business sentiment – constraining firm investment and international trade – there is less evidence of how such tensions affect households. This column examines how geopolitical risks spill over into consumer sentiment and spending behaviour. The authors find that such factors lead to pessimistic economic expectations and elevated income uncertainty. Consumers worry that geopolitical friction can spread to stock prices and other financial assets, but expect housing prices to remain relatively immune, even amid widening armed conflict and a significant deterioration in the geopolitical landscape.

We develop a novel measure of climate policy uncertainty based on newspaper coverage. Our index spikes during key U.S. climate policy events—including presidential announcements on international agreements, congressional debates, and regulatory disputes—and shows a recent upward trend. Using an instrument for plausibly exogenous uncertainty shifts, we find that higher climate policy uncertainty decreases output and emissions while raising commodity and consumer prices, acting as supply rather than demand shocks. Monetary policy counteracts these inflationary pressures, affecting the transmission of climate policy uncertainty. Firm-level analyses show stronger declines in investment and R&D when firms have higher climate change exposure.

Using loan-level regulatory data, this paper finds that banks reduce risk-taking in their loan portfolio after bank-level uncertainty rises. Banks also increase interest rates and require collateral in loan contracts after positive uncertainty shocks, especially among banks with less regulatory capital that are also subject to mark-to-market accounting regulations. In response, public firms dependent on bank credit hold more cash and reduce investment and employment. Riskier private firms turn to shorter duration and higher cost credit, such as credit cards and trade credit, increasing illiquidity. Local employment also declines after bank uncertainty increases. Taken together, uncertainty shocks in the banking system significantly affect the economy.

We present a microfounded New Keynesian model that features financial vulnerability. Financial intermediaries’ occasionally binding value at risk constraints give rise to variation in the pricing of risk that generates time varying conditional moments of output. The conditional mean and volatility of the output gap are negatively related: during times of easy financial conditions—when the price of risk is low—growth tends to be high, and risk tends to be low. Monetary policy a!ects output directly via the intertemporal substitution of savings, and also via the pricing of risk that relates to the tightness of the value at risk constraints. The optimal monetary policy rule always depends on financial vulnerability in addition to the output gap, inflation, and the natural rate. We show that a classic Taylor rule exacerbates deviations of the output gap from its target value of zero relative to an optimal interest rate rule that includes vulnerability. The model provides a microfoundation for optimal monetary policy that takes financial vulnerability into account.

Subjective expectations and asset prices both revolve around distorted probabilities. Subjective expectations are expectations under biased probabilities, and asset prices are expectations under risk-neutral probabilities. Given this link, asset pricing techniques designed to estimate a Stochastic Discount Factor (SDF) can be used to estimate a Subjective Belief Factor (SBF) a distortion that characterizes many subjective expectations, even for non-financial variables. Using the Survey of Professional Forecasters and Blue Chip, we find that differences between subjective expectations and statistical expectations for 24 macroeconomic variables can be summarized (average R2 of 50%) by a single SBF related to real GDP growth and the T-bill rate. This SBF also accurately replicates differences across the 24 variables in the serial correlation of forecast errors and under/overreaction. Further, the SBF can be used to succinctly incorporate subjective beliefs data into asset pricing models, even those involving many expectations. Applying our measured SBF to a model of cross-sectional stock returns, we estimate that distorted beliefs account for the majority of excess returns for the Fama-French factors and explain about two thirds of the variation in returns across 176 anomalies, while the remaining third is attributed to preferences/risk. Our results support models like diagnostic expectations and robust control in which agents' beliefs across different variables are characterized by a single probability distortion.

We propose a model in which risk, at both the micro and the macro levels, is endogenous. In the model, each firm can choose the mean and the variance of its productivity process, as well as how it covaries with the productivity of other firms. To study the aggregate impact of these decisions, we embed the firms into an otherwise standard production network economy. Through their impact on risk-taking decisions, distortions such as taxes and markups can make GDP more volatile in equilibrium. The theory also predicts that the productivity of larger firms and those with smaller markups is less volatile and less correlated with aggregate productivity. We find support for these predictions in the data. In a calibrated version of the model, removing distortions significantly reduces GDP volatility.

This paper studies whether households are selectively inattentive to interest rates and examines its macroeconomic implications. We first use a combination of existing and newly-designed surveys to establish that households close to durables purchases actively acquire more information about interest rates and have more accurate, less dispersed, and less uncertain interest rate expectations. Next, we use this evidence to calibrate a partial equilibrium incomplete markets model with durable consumption and endogenous information acquisition about interest rates. Finally, we show how selective inattention changes aggregate responses to interest rates. Relative to exogenous inattention, selective inattention shifts the composition of spending responses to interest rate cuts, accelerates the impact of larger cuts, and generates dampened responses to changes in volatility that are closer to empirical evidence.

Uncertainty affects business cycles and asset prices. We estimate firm-level productivity and decompose total uncertainty risk measured as cross-sectional productivity dispersion into macro uncertainty (an aggregate component) and micro uncertainty (an idiosyncratic component). We find that macro uncertainty is multidimensional, strongly countercyclical, and priced among stocks, but micro uncertainty is acyclical and not priced. Moreover, we show that the expected investment growth factor proposed in Hou, Mo, Xue, and Zhang (2021) captures macro uncertainty risk which helps us understand the success of the q 5-model.

We analyze inflation uncertainty in the US over the last several decades. We begin by constructing a novel composite indicator of inflation uncertainty (CIU) from two components: a news-based measure derived from textual analysis of newspaper articles and a market-based measure that draws on bond ETF option prices and commodity price volatility. Unlike survey or inflation option-based measures, our index is available in real time and extends back to 1926. CIU reveals that inflation un-certainty spiked during the Great Depression, World War II, the 1970s and 1980s, following the Global Financial Crisis, and in the post-pandemic period. We highlight the driving forces behind fluctuations in uncertainty – including supply-side factors, fiscal and trade policy and monetary policy. Finally, we show that a rise in inflation uncertainty is associated with higher costs of capital for firms through wider credit spreads as well as cost of equity financing. Higher inflation uncertainty is also associated with significant declines in private investment activity.

Using novel measures of information acquisition, we document causal evidence of a feedback loop between firms’ credit access and information acquisition. To examine the macroeconomic implications of this feedback loop, we develop a tractable general equilibrium framework with financial frictions and endogenous information acquisition. In line with the empirical evidence, the model predicts that a rise in information costs raises the level of uncertainty and reduces a firm’s equity value, hampering its credit access. On the other hand, tightened credit constraints restrain activity of high-productivity firms, leading to misallocation that reduces aggregate productivity and firm profits, and discouraging information acquisition. This feedback loop creates a finance-uncertainty trap that substantially amplifies and prolongs business cycle fluctuations.

This paper introduces a new approach to measuring uncertainty shocks by exploiting the term structure of VIX futures. We document that VIX futures display notable within-month dynamics, including frequent episodes of inversion. Harnessing these dynamics allows us to assess the causal effects of uncertainty shocks, which can be either expansionary or contractionary depending on how the term structure rotates. Furthermore, we show that correlations between existing uncertainty measures—such as the Economic Policy Uncertainty index—and VIX futures vary across horizons: at times stronger with short-term futures and at other times with longer maturities. This variation provides evidence consistent with the potential decoupling of uncertainty indices, a phenomenon frequently emphasized in the literature.

There is increasing awareness of how local media affects financial markets, but also of the endogeneity of media coverage. We separate the causal impact of local media on financial markets from selection effects using a new, hand-collected database of news-paper closures. We find that at least 29% of local newspaper closures are driven by distress, and thus, likely endogenous to local economic conditions. Return volatility and idiosyncratic risk decrease significantly after non-distress-driven newspaper closures, but increase after distress-driven closures, suggesting the presence of substantial selection effects. We find similar patterns for liquidity and trading. Once we account for selection, the estimated impact of local newspapers on volatility increases by over 40%. The reduction in volatility after non-distress-driven newspaper closures is larger for stocks subject to greater information frictions, lower national media coverage, firms located in remote areas, and during recessions. All these tests suggest that investor information processing is the main channel that drives our results. Our findings highlight that the effect of media on financial markets may be larger than previously documented.

This paper develops a theory of the elasticity of preference for early resolution of uncertainty (PER) that parallels the Arrow-Pratt measure of risk aversion in expected utility theory. We demonstrate that the local welfare gain of early resolution of uncertainty is equal to the product of the elasticity of PER and the conditional variance of continuation utility. We illustrate how asset market data can be used to estimate the elasticity of PER and how this measure can be used to compute the welfare gain for various experiments of early resolution of uncertainty.