Durables’ interest-rate sensitivity and their persistent comovement with nondurable spending are hallmarks of monetary policy transmission. We develop a two-sector HANK model that replicates this pattern—both across spending categories and among households sorted by liquid asset holdings, consistent with empirical evidence. Direct effects of real interest rate changes are quantitatively important in reproducing sectoral expenditure comovement, while infrequent information updating is crucial to match the hump-shaped dynamics of sectoral and aggregate expenditures. Income effects are essential to preventing counterfactual declines in nondurable spending resulting from fiscal interventions specifically aimed at stimulating durable purchases.

Consensus holds that Emerging Markets and Developing Economies (EMDEs) engage in procyclical fiscal behavior. We emphasize that considering conditional responses to macroeconomic shocks is crucial when evaluating fiscal cyclicality, as neglecting this can result in significant biases. This study investigates the effects of exogenous commodity price shocks on fiscal variables in EMDEs by exploiting major narrative episodes and the heterogeneous exposure of countries to these shocks. Our results reveal that, following an expansionary shift in the terms of trade, fiscal authorities raise government spending and moderately increase taxes. The overall fiscal stance mitigates the effect of commodity price booms while leading to an improvement in the primary balance. These findings contrast with conventional wisdom but align with the optimal policy response to export price shocks predicted by a multi-good small open economy model with incomplete financial markets. We also highlight the role of institutional quality in shaping fiscal policy responses.

This paper investigates the information content of oil market forecasts produced by the U.S. Energy Information Administration (EIA). We evaluate the maximum informative forecast horizons for EIA projections of world and U.S. oil demand, supply, inventories, and prices. Our results show that U.S. forecasts are systematically more informative than their global counterparts, with content horizons extending up to six quarters for most U.S. variables. The information content embedded in EIA forecasts reflects both the agency’s ability to track evolving market conditions and, particularly at short horizons, the incorporation of information that goes beyond simple trend extrapolation.

We develop a data-rich measure of expected macroeconomic skewness in the US economy. Expected macroeconomic skewness is strongly procyclical, mainly reflects the cyclicality in the skewness of real variables, is highly correlated with the cross-sectional skewness of firm-level employment growth, and is distinct from financial market skewness. Revisions in expected skewness lead to business cycle fluctuations nearly indistinguishable from those induced by the main business cycle shock of Angeletos et al. (2020). This result is robust to controlling for macroeconomic volatility and uncertainty, and alternative macroeconomic shocks. Our findings suggest an important role of higher-order dynamics for business cycle theories.

A long tradition in macro-finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra restriction that dividend growth is not persistent. We highlight that persistence in dividend growth induces a previously overlooked channel for return predictability, which we label ``dividend momentum.'' Compared to estimation based on OLS, our restricted informative prior leads to a much more moderate, but still significant, degree of return predictability, with forecasts that are helpful out-of-sample and realistic asset allocation prescriptions with Sharpe ratios that out-perform common benchmarks.

We compute a common factor summarising asymmetries in the expected distributions of a large set of survey-based economic data series for the euro area. This expected skewness factor is distinct from lower-moment factors and can help improve forecasts of risks to economic activity and inflation. In addition, within a monthly vector autoregression (VAR), we show that revisions to survey-based expected skewness have macroeconomic and financial implications, even when the average assessment and expected volatility reflected in the surveys remain unchanged. The skewness measure could benefit economic policy institutions by supporting timely quantitative assessments of the balance of risks.

Time-varying asymmetric inflation risks generate persistent stagflationary effects. A quantitative general equilibrium model with time-varying skewness in the distribution of cost-push shocks matches these effects. Central to the analysis is a representation theorem that provides a tractable characterization of a broad class of models with asymmetric shock distributions. The theorem enables a closed-form characterization of optimal monetary policy, according to which the central bank should lean against the balance of inflation risks, while rendering quantitative general-equilibrium models with time-varying risks amenable to counterfactual and scenario analysis.

Using UK consumer price microdata, we report that aggregate price flexibility varies substantially over time and induces significant non-linearity in inflation. In a regime of high flexibility, the half-life of inflation drops by 50% and its volatility rises considerably. Such asymmetry arises naturally from state-dependent pricing, of which we find ample evidence in the data, particularly following the Great Recession. Neglecting this property may lead to a systematic underprediction of inflation, as seen in the post-pandemic inflation surge. Tracking real-time movements in price flexibility is crucial for assessing inflation dynamics and informing monetary policy decisions.

Terms of trade are an inaccurate empirical proxy for how fluctuations in international prices affect the economy. To capture the relevance of terms of trade fluctuations for the domestic business cycle, the role of export and import prices needs to be analyzed separately. Using a sample of developing economies, we find that the economy’s response to a positive export price shock does not mirror the response to a negative import price shock. (JEL E23, E32, E43, F14, F44, O11)

We document that inflation risk in the U.S. varies significantly over time and is often asymmetric. To analyze the macroeconomic effects of these asymmetric risks within a tractable framework, we construct the beliefs representation of a general equilibrium model with skewed distribution of markup shocks. Optimal policy requires shifting agents’ expectations counter to the direction of inflation risks. We perform counterfactual analyses using a quantitative general equilibrium model to evaluate the implications of incorporating real-time estimates of the balance of inflation risks into monetary policy communications and decisions.

We model permanent and transitory changes of the predictive density of U.S. GDP growth. A substantial increase in downside risk to U.S. economic growth emerges over the last 30 years, associated with the long-run growth slowdown started in the early 2000s. Conditional skewness moves procyclically, implying negatively skewed predictive densities ahead and during recessions, often anticipated by deteriorating financial conditions. Conversely, positively skewed distributions characterize expansions. The modeling framework ensures robustness to tail events, allows for both dense or sparse predictor designs, and delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks.

A key question for households, firms, and policy makers is: how is the economy doing now? This paper develops a Bayesian dynamic factor model that allows for nonlinearities, heterogeneous lead–lag patterns and fat tails in macroeconomic data. Explicitly modeling these features changes the way that different indicators contribute to the real-time assessment of the state of the economy, and substantially improves the out-of-sample performance of this class of models. In a formal evaluation, our nowcasting framework beats benchmark econometric models and professional forecasters at predicting US GDP growth in real time.

We examine the impact of commodity price changes on the business cycles and capital flows in emerging markets and developing economies (EMDEs), distinguishing between their role as a source of shock and as a channel of transmission of global shocks. Our findings reveal that surges in export prices, triggered by commodity price shocks, boost domestic GDP, an effect further amplified by the endogenous decline of country spreads. However, the effects on capital flows appear muted. Shifts in U.S. monetary policy and global risk appetite drive the global financial cycle in EMDEs. Eased global credit conditions, attributed to looser U.S. monetary policy or lower global risk appetite, lead to a rise in export prices, higher output, a decrease in government borrowing costs, and stimulate greater capital flows. The endogenous response of export prices amplifies the output effects of a more accommodative U.S. monetary policy while country spreads magnify the impact of shifts in global risk appetite.

This paper proposes a novel method for the identification of monetary policy shocks. By applying natural language processing techniques to documents that staff economists at the Federal Reserve prepare for FOMC meetings, we capture the information set of the committee at the time of policy decisions. We verify econometrically that the language contains valuable information beyond what is incorporated in the staff’s numerical forecasts. Using machine learning techniques, we then predict changes in the target interest rate conditional on the committee’s information set and obtain a measure of monetary policy shocks as the residual. We find that the dynamic responses of macro variables to our identified shocks are consistent with the theoretical consensus. A real-time application of our method interprets the 2022 rate hikes as almost entirely systematic.

We evaluate US Energy Information Administration (EIA) forecasts of the world petroleum market, emphasising the importance of taking a multivariate perspective, considering asymmetric loss and allowing for time-variation. Forecasts for total demand, total supply, total stock withdrawals and the oil prices are biased, with biases that change over time and differ across variables. A loss function that takes into account asymmetry and interdependence can rationalise these biases. The implied asymmetric loss gives less weight to under-prediction of both demand and supply, while for oil prices, we document significant regime changes in the implied loss due to asymmetry. The EIA forecasts dominate a simple random walk benchmark when evaluated using symmetric and independent loss in the form of MSE statistical criteria. Yet, when allowing for asymmetry and interdependence that rationalise the EIA forecasts, the performance of the EIA forecasts worsens and is comparable to the random walk benchmark.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Taming Momentum Crashes 63 Pages Posted: 9 Aug 2022 See all articles by Daniele BianchiDaniele BianchiSchool of Economics and Finance, Queen Mary University of LondonAndrea De PolisUniversity of Warwick - Warwick Business SchoolIvan PetrellaUniversity of Warwick - Finance Group; University of Warwick - Warwick Business School; University of Warwick; Centre for Economic Policy Research (CEPR) Date Written: August 4, 2022 Abstract We provide empirical evidence that the returns on US equity momentum exhibit a time-varying skewness which deepens during dramatic losses (crashes). As a result, the dynamics of the strategy expected returns reflects the time variation in both conditional volatility and skewness. This has first order implications for managing risks associated with momentum investing: an adjusted momentum portfolio which hedges in real time for both volatility and skewness risk outperforms benchmark constant and dynamic volatility-managed momentum strategies. This result holds for different levels of transaction costs and risk aversion and cannot be reconciled by the exposure to standard equity risk factors. Keywords: Momentum, time-varying skewness, managed portfolios, asset pricing, score driven models. JEL Classification: G11, G12, G17, C23 Suggested Citation: Suggested Citation Bianchi, Daniele and De Polis, Andrea and Petrella, Ivan and Petrella, Ivan and Petrella, Ivan, Taming Momentum Crashes (August 4, 2022). Available at SSRN: https://ssrn.com/abstract=4182040 Daniele Bianchi (Contact Author) School of Economics and Finance, Queen Mary University of London ( email ) Mile End RoadLondon, London E1 4NSUnited Kingdom HOME PAGE: http://whitesphd.com Andrea De Polis University of Warwick - Warwick Business School ( email ) Coventry CV4 7ALUnited Kingdom Ivan Petrella University of Warwick - Finance Group ( email ) Gibbet Hill RdCoventry, CV4 7ALGreat Britain University of Warwick - Warwick Business School ( email ) Coventry CV4 7ALUnited Kingdom University of Warwick ( email ) Gibbet Hill Rd.Coventry, West Midlands CV4 8UWUnited Kingdom Centre for Economic Policy Research (CEPR) ( email ) LondonUnited Kingdom Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 76 Abstract Views 253 rank 456,103 PlumX Metrics Related eJournals Investments eJournal Follow Investments eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 229 PAPERS 2,218 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo4 in 0.234 seconds

We evaluate US Energy Information Agencies (EIA) forecasts of the world petroleum market, emphasising the importance of taking a multivariate perspective, considering asymmetric loss and allowing for time-variation. Forecasts for total demand, total supply, total stock withdrawals and the oil prices are biased, with biases that change over time and differ across variables. A loss function that takes into account asymmetry and interdependence can rationalise these biases. The implied asymmetric loss gives less weight to under-prediction of both demand and supply, while for oil prices, we document significant regime changes in the implied loss due to asymmetry. The EIA forecasts dominate a simple random walk benchmark when evaluated using symmetric and independent loss in the form of MSE statistical criteria. Yet, when allowing for asymmetry and interdependence that rationalize the EIA forecasts, the performance of the EIA forecasts worsens and is comparable to the random walk benchmark.

When analyzing terms-of-trade shocks, it is implicitly assumed that the economy responds symmetrically to changes in export and import prices. Using a sample of developing countries our paper shows that this is not the case. We construct export and import price indices using commodity and manufacturing price data matched with trade shares and separately identify export price, import price, and global economic activity shocks using sign and narrative restrictions. Taken together, export and import price shocks account for around 40 percent of output fluctuations but export price shocks are, on average, twice as important as import price shocks for domestic business cycles.

Macroeconomists constructing conditional forecasts often face a choice between taking a stand on the details of a fully-specified structural model or relying on correlations from VARs and remaining silent about underlying causal mechanisms. This paper develops tools for constructing economically meaningful scenarios with structural VARs, and proposes a metric to assess and compare their plausibility. We provide a unified treatment of conditional forecasting and structural scenario analysis, relating them to entropic tilting. A careful treatment of uncertainty makes our methods suitable for density forecasting and risk assessment. Two applications illustrate our methods: assessing interest-rate forward guidance and stress-testing bank profitability.