Higher Macroeconomic Uncertainty Research Articles

Financial conditions, macroeconomic uncertainty, and macroeconomic tail risks

This paper investigates how financial conditions and macroeconomic uncertainty jointly affect macroeconomic tail risks. We first document that tight financial conditions decrease all conditional quantiles of future output growth in the near term, while high macroeconomic uncertainty only stretches the interquartile range. Because financial conditions and uncertainty comove substantially, the conditional means and variances shift simultaneously in the opposite direction. Consequently, the downside risk varies much more than the upside risk. A quantile impulse response analysis indicates that both financial and uncertainty shocks are responsible for the asymmetric behaviors in the downside and upside risks.

How does the central bank stabilize the stock market under macro uncertainty: Evidence from China stock market

This paper establishes mathematical models and PVAR models to verify the micro transmission path and the validity of central bank communication and actual interest rate intervention on China stock market. Through heterogeneity analysis, we further test whether the level of macroeconomic uncertainty affects their effect. We confirmthat both central bank communication and actual interest rate intervention have a significant effect. However, central bank communication is ineffective in the high macroeconomic uncertainty sample while the interest rate intervention can have a greater effect. Overall, the results suggest that central bank communication is a more effective monetary policy tool on stock market in low macroeconomic uncertainty period, while interest rate intervention is a better choice in high macroeconomic uncertainty period.

Macroeconomic uncertainty and non-GAAP disclosure

This study examines the relation between macroeconomic uncertainty and firms’ non-GAAP disclosure. Using the dispersion of GDP forecasts and the Chicago Board Options Exchange (CBOE) VIX Index to proxy for macroeconomic uncertainty, this study finds that non-GAAP reporting likelihood decreases during periods of high macroeconomic uncertainty. However, for firms that choose to disclose non-GAAP earnings, their non-GAAP exclusions become more transitory, indicating an increase in non-GAAP reporting quality. Further evidence suggests that the increase in non-GAAP reporting quality is driven by the decrease in the persistence of excluded special items. Overall, the findings of this study advance the knowledge of how non-GAAP disclosure changes as macroeconomic uncertainty varies.

Fiscal policy and uncertainty

We investigate the impact of macroeconomic uncertainty on the stimulative effect of government spending. Using historical macroeconomic time series, we show that government spending multipliers are smaller in episodes characterized by high macroeconomic uncertainty. This state dependence is found to be statistically and economically significant and robust to several alternative specifications. To provide structure, we then build a DSGE model with an agency problem and a forecast-error based uncertainty index. We solve the model via a higher-order perturbation method and use the model to evaluate state-dependent government stimulus. In simulations, we find significant volatility in multipliers as well as co-movements that qualitatively match our empirical results. We find that these results are generated by a reallocation away from risky assets following the government spending shock which triggers a financial accelerator mechanism in our model; we then show that this channel is also active in the data. We conclude that uncertainty plays a significant role in determining the effectiveness of fiscal policy and that business cycle models which abstract from uncertainty can produce misleading policy recommendations.

How is machine learning useful for macroeconomic forecasting?

SummaryWe move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how. The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. To the contrary, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so‐called features (nonlinearities, regularization, cross‐validation, and alternative loss function) and study their behavior in both the data‐rich and data‐poor environments. To do so, we design experiments that allow to identify the “treatment” effects of interest. We conclude that (i) nonlinearity is the true game changer for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) K‐fold cross‐validation is the best practice, and (iv) the is preferred to the ‐insensitive in‐sample loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. Furthermore, ML nonlinearities are helpful when considering density forecasts.

Macroeconomic uncertainty and management forecast accuracy

This study examines the effect of macroeconomic uncertainty on the accuracy of management earnings forecasts. Focusing on Japanese management earnings forecasts, which are effectively mandated, I find that during periods of high macroeconomic uncertainty, firms tend to report accurate earnings forecasts. I also find that macroeconomic uncertainty lessens optimistic but not pessimistic errors. These findings are consistent with the scenario that managers try to avoid missing their forecasts or revising their forecasts downward because investors place greater weight on bad news when macroeconomic uncertainty is high. Consistent with this scenario, additional analyses reveal that firms experience a larger decrease in stock prices when they miss their forecasts or revise their forecasts downward under high macroeconomic uncertainty. Moreover, these findings are robust after controlling for the effect of earnings management. These results suggest that the usefulness of management forecasts does not decrease even when macroeconomic uncertainty is high.

Impact of idiosyncratic and macroeconomic risks on capital structure: evidence from SADC countries

PurposeThe purpose of this study is to investigate the impact of idiosyncratic and macroeconomic risks on capital structure on SADC countries.Design/methodology/approachEmploying data from the African Financials database, the analysis is conducted over a ten year period spanning from 2009 to 2018 for 309 companies. Unit Root Fisher Chi-Square test and Granger Causality test were employed to test for unidirectional and bidirectional relationships cross-sectionally. To resolve endogeneity issues, System GMM was used as main topology for panel regression analysis.FindingsThe study confirmed that companies become risk averse when there is an increase in idiosyncratic and macroeconomic risk and therefore take less leverage. According to the perking order theory, a higher variability in earnings shows that the bankruptcy probability amplifies. Hence, institutions with high income employ more internal finance during periods of high idiosyncratic and macroeconomic uncertainty thereby lowering leverage. A positive significant and statistically relationship is also confirmed between idiosyncratic risk and leverage in Botswana, South Africa and Tanzania. Companies with higher leverage make riskier investment in line with the trade-off theory. In short, executives from the SADC region consider more importance to fluctuations in risk while accelerating or diminishing leverage in their capital structure.Originality/valueThe study is among one of the pioneering work underpinning the idiosyncratic risk and macroeconomic risk on capital structure and relying on a large number of companies across the SADC region. In this respect, it adds contribution to the existing literature on risks and capital structure to the socio-economic goals of the SADC region.

The effects of uncertainty shocks on global housing markets

PurposeThis paper aims to investigate how different uncertainty shocks affect international housing prices.Design/methodology/approachThe authors set up a model of housing price instability with four uncertainty variables and apply the panel generalized method of moments method and quantile regression to estimate the linear and non-linear linkages among the variables based on data of 56 countries from 2001Q1 to 2018Q2.FindingsSome empirical findings are as follows. Higher macroeconomic uncertainty and global economic policy risk increase housing price instability, whereas greater financial uncertainty and geopolitical risk present reverse effect. Four uncertainty variables are good signals for housing price changes in Asia, and geopolitical risk takes leading role in Europe. Macroeconomic uncertainty positively impacts housing price instability only at a low or middle level in all regions, as financial uncertainty, global economic policy uncertainty and geopolitical risk effects in all regions are smaller at the middle or high level of housing price instability; this confirms the existence of non-linear correlation between each variable.Originality/valueThe findings help investors and policymakers gain a better notion of housing price instability and control into uncertainty signal that could cause housing price instability crash.

Macroeconomic Uncertainty and Management Forecast Accuracy

This study examines the effect of macroeconomic uncertainty on the accuracy of management earnings forecasts. Focusing on Japanese management earnings forecasts, which are effectively mandated, I find that during periods of high macroeconomic uncertainty, firms tend to report accurate earnings forecasts. I also find that macroeconomic uncertainty lessens optimistic but not pessimistic errors. These findings are consistent with the scenario that managers try to avoid missing their forecasts or revising their forecasts downward because investors place greater weight on bad news when macroeconomic uncertainty is high. Consistent with this scenario, additional analyses reveal that firms experience a larger decrease in stock prices when they miss their forecasts or revise their forecasts downward under high macroeconomic uncertainty. Moreover, these findings are robust after controlling for the effect of earnings management. These results suggest that the usefulness of management forecasts does not decrease even when macroeconomic uncertainty is high.

Fiscal Policy and Uncertainty

We investigate the impact of macroeconomic uncertainty on the stimulative effect of government spending. Using historical macroeconomic time series, we show that government spending multipliers are smaller in episodes characterized by high macroeconomic uncertainty. This state dependence is found to be both statistically and economically significant. We then build a DSGE model with an agency problem subject to time varying levels of uncertainty. We solve the model via higher-order perturbation and use the model to evaluate state-dependent government stimulus. We find significant state-dependence in multipliers which qualitatively matches our empirical results. We conclude that uncertainty plays a significant role in determining the effectiveness of fiscal policy and that business cycle models which abstract from uncertainty can produce misleading policy recommendations.

Monetary Policy Transmission Over the Leverage Cycle: Evidence for the Euro Area

We study state dependence in the impact of monetary policy shocks over the leverage cycle for a panel of 10 euro area countries. We use a Bayesian Threshold Panel SVAR with regime classifications based on credit and house prices cycles. We find that monetary policy shocks trigger a smaller response of GDP, but a larger response of inflation during low states of the cycle. The shift in the inflation-output trade-off may result from higher macro-economic uncertainty in low leverage states. For an alternative regime classification based on turning points we find larger effects on GDP during contractions. JEL Classification: C32, E32, E44

Financial Reporting and Entrepreneurial Finance: Evidence from Equity Crowdfunding

I study the role of accounting and financial reporting in entrepreneurial finance by examining whether financial statement disclosure increases capital raised through equity crowdfunding. On average, I find a positive association between financial reporting and capital raised, suggesting that accounting reduces information asymmetry with potential investors. Additionally, the importance of financial reporting in equity crowdfunding varies predictably in the cross-section. Specifically, financial reporting is associated with greater capital raised when the firm has longer historical operations, during periods of higher macroeconomic uncertainty, and when complemented by detailed shareholder agreements. Finally, using a mediation analysis, I find evidence that financial reporting is indirectly associated with better ex-post performance by increasing the likelihood of raising capital. These results provide insight into the role of financial reporting in entrepreneurial finance and inform the ongoing debate over regulation and disclosure in the equity crowdfunding market.

Volatility forecasting in commodity markets using macro uncertainty

In this paper, we empirically examine the predictive power of macroeconomic uncertainty on the volatility of agricultural, energy and metals commodity markets. We find that the latent macroeconomic uncertainty measure of Jurado et al. (2015) is a common volatility forecasting factor for commodity markets, which provides statistically significant volatility predictions for forecasting horizons up to twelve months ahead. The results indicate that the forecasting power of macroeconomic uncertainty is higher when predicting the volatility of energy commodities. Our findings also show that higher macroeconomic uncertainty is associated with large volatility episodes subsequently observed in all commodity markets. The predictive power of the unobservable macroeconomic uncertainty factor remains robust to the inclusion of observable economic uncertainty measures, historical commodity price volatility, stock-market realized and news implied volatility, oil price shocks and other macroeconomic variables which are closely related to the production process and the mechanics of commodity markets.

Macroeconomic uncertainty and the distant forward-rate slope

Abstract Over the 1990 to 2014 period, we show that the macroeconomic-uncertainty index of Jurado et al. (2015) is a powerful empirical determinant of the slope in Treasury forward interest rates over the 10- to 30-year term-structure segment. The strong negative partial relation between macroeconomic uncertainty and the distant forward-rate slope remains reliably evident in a multivariate setting that includes expected bond yield volatility (as a control for convexity’s influence), economic growth, expected inflation, and five other well-known uncertainty and risk measures. Beyond the well-known bond convexity channel for promoting a downward sloping distant forward-rate slope, our findings suggest that higher macroeconomic uncertainty can promote a more downward slope in distant forward rates through a hedge channel in the sense of Campbell et al. (2017). Consistent with the hypothesized hedge channel, we also document striking cross-sectional variation in how bond returns are related to macroeconomic uncertainty, both across mid- and long-horizon T-bonds and across high and low credit-risk corporate bonds.

Betting on Gold Using a Futures-Based Gold ETF

In an environment of high macroeconomic uncertainty in the fall of 2012, Tom Michelson was looking at his next trading opportunity. Michelson wanted to build a position in gold with a trading horizon of around a year and was wondering how he should go about investing in gold. Gold futures-based exchange-traded funds (ETFs) seemed like a good alternative, but he needed to understand better how gold futures worked. He was aware that futures-based ETFs for other commodities such as oil and gas had disappointing performances in recent years due to movements in the futures curves. Would a gold futures-based ETF be a good choice to profit from the price appreciation of gold? Excerpt UVA-F-1690 Apr. 22, 2013 BETTING ON GOLD USING A FUTURES-BASED GOLD ETF In an environment of high macroeconomic uncertainty in the fall of 2012, Tom Michelson was looking at his next trading opportunity. Michelson's view was that financial markets would remain in turmoil, and his bet was that gold prices would continue their sustained run-up fueled by the 2008 credit crisis, the successive rounds of quantitative easing by the Federal Reserve, and the ongoing euro-area sovereign debt crisis (Exhibit 1). He was willing to build a position of approximately $ 100,000 in gold with a trading horizon of around a year. How should Michelson go about investing in gold? It used to be that, as an individual investor, he would have to buy shares in a gold mining firm. In previous years, however, as gold prices increased, the stock price of gold mining companies had not appreciated because miners struggled with rising costs. Michelson was not a seasoned investor in buying physical gold (e.g., coins or bullion bars), and he wanted to avoid storage and other costs. His financial adviser suggested that an easy way to get exposure to gold was to invest in exchange-traded funds (ETFs) that tracked the performance of gold. ETF shares traded the same way as stocks and made commodities more accessible than ever. In particular, he was curious about PowerShares DB Gold Fund (ticker: DGL), a futures-based ETF. It seemed to be a cheap way to get futures-based exposure to gold without having to trade directly in gold futures. . . .

Corporate Bond Portfolios and Macroeoconomic Conditions

We propose an approach to optimally select corporate bond portfolios based on bond-specific characteristics (maturity, credit rating, coupon, illiquidity, past performance, and issue size) and macroeconomic conditions (recessions and macroeconomic uncertainty measures). The approach relies on a parametric specification of the portfolio weights and allows us to consider a large cross-section of corporate bonds. We find that in periods of low macroeconomic uncertainty, the optimal corporate bond portfolio is tilted toward bonds with longer maturity and higher credit rating (high ex-ante default risk), relative to the benchmark. By contrast, in high macroeconomic uncertainty regimes, the optimal strategy exhibits a flight-to-safety aspect and favors short maturity and relatively low-credit-rating bonds. In all regimes, corporate bonds with high coupons, high past performance, and small size of issuance lead to higher certainty equivalent returns. Overall, we find that the characteristics used in the corporate bond pricing literature to proxy for various sources of risk are also useful in forming corporate bond portfolios. Conditioning on these characteristics and macroeconomic variables leads to a significant improvement in portfolio performance, with the certainty equivalent increasing about 5% per annum after conservative transaction costs. The gain in performance is evenly divided between high and low macroeconomic uncertainty regimes and is not exclusively concentrated in high-yield bonds.

The economic value of predicting bond risk premia

This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states.

Macroeconomic Uncertainty and Oil Price Volatility

AbstractThis paper shows that higher macroeconomic uncertainty causes higher oil price volatility. Regimes of low and high uncertainty are identified in a threshold VAR model in which the effects of structural oil demand and supply shocks are estimated. The results show that higher macroeconomic uncertainty, as measured by global industrial production volatility, significantly increases the sensitivity of oil prices to shocks in oil demand and supply. This occurs as uncertainty lowers the price elasticity of oil demand and supply. The difference in the estimated oil price elasticities is economically meaningful as the price impact of a similar change in oil production might double when it hits the economy in uncertain times. As such, varying uncertainty can explain why oil price volatility is typically higher during periods such as financial crises and recessions, and why oil price volatility changes over time more generally.

Macroeconomic Uncertainty and Management Earnings Forecasts

SYNOPSISTo provide evidence on the role macroeconomic uncertainty plays in managers' decision to issue management earnings forecasts (MFs), this study develops and tests hypotheses about how such uncertainty affects the issuance and characteristics of MFs. Macroeconomic uncertainty is measured using the dispersion in GDP forecasts and the CBOE's Volatility Index (VIX). We find that during periods of high macroeconomic uncertainty there is a decrease in the likelihood of MF issuance, consistent with managers assigning a higher cost to releasing forward-looking information as macroeconomic uncertainty increases. We also find that managers issue fewer good and bad news MFs, but more neutral MFs, during periods of high macroeconomic uncertainty. Macroeconomic uncertainty also affects the characteristics of the MFs that managers do issue; for example, managers shift to more earnings preannouncements and to shorter-horizon, but more precise, MFs. Further analysis indicates that the regulatory changes imbedded in the Sarbanes-Oxley Act increased the costs of providing MFs, thereby increasing the sensitivity of MF issuance to macroeconomic uncertainty. The findings provide insight into the role macroeconomic uncertainty plays in managers' decision to issue MFs and the characteristics of the MFs they choose to issue.

Inflation Fan Charts and Different Dimensions of Uncertainty: What if Macroeconomic Uncertainty is High?

The paper discusses problems associated with communicating uncertainty by means of ‘fan charts’, used in many central banks for presenting density forecasts of inflation and other macroeconomic variables. Limitations of fan charts in the case of high macroeconomic uncertainty are shown. Issues related to definition of uncertainty are addressed, stressing the need to distinguish between statistical model errors and uncertainty due to lack of knowledge. Modifications of the standard methods of constructing fan charts are suggested. The proposed approach is based on two distributions, one of which is subjective and describes possible macroeconomic scenarios, while the other describes model errors. Total uncertainty is represented as a mixture distribution or density convolution. The proposed approach, although it is a mix of judgment and statistics, allows preserving information about scenarios and separating in the analysis different types of uncertainties.