Risky Oil: It's All in the Tails
Christiane Baumeister, Florian Huber, Massimiliano Marcellino
NBER Working Paper,
Nr. 32524,
2024
Abstract
The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
Nr. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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Does IFRS Information on Tax Loss Carryforwards and Negative Performance Improve Predictions of Earnings and Cash Flows?
Sandra Dreher, Sebastian Eichfelder, Felix Noth
Journal of Business Economics,
January
2024
Abstract
We analyze the usefulness of accounting information on tax loss carryforwards and negative performance to predict earnings and cash flows. We use hand-collected information on tax loss carryforwards and corresponding deferred taxes from the International Financial Reporting Standards tax footnotes for listed firms from Germany. Our out-of-sample tests show that considering accounting information on tax loss carryforwards does not enhance performance forecasts and typically even worsens predictions. The most likely explanation is model overfitting. Besides, common forecasting approaches that deal with negative performance are prone to prediction errors. We provide a simple empirical specification to account for that problem.
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Conditional Macroeconomic Survey Forecasts: Revisions and Errors
Alexander Glas, Katja Heinisch
Journal of International Money and Finance,
November
2023
Abstract
Using data from the European Central Bank's Survey of Professional Forecasters and ECB/Eurosystem staff projections, we analyze the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the updating and ex-post performance of predictions for inflation, real GDP growth and unemployment are related to beliefs about future oil prices, exchange rates, interest rates and wage growth. While oil price and exchange rate predictions are updated more frequently than macroeconomic forecasts, the opposite is true for interest rate and wage growth expectations. Beliefs about future inflation are closely associated with oil price expectations, whereas expected interest rates are related to predictions of output growth and unemployment. Exchange rate predictions also matter for macroeconomic forecasts, albeit less so than the other variables. With regard to forecast errors, wage growth and GDP growth closely comove, but only during the period when interest rates are at the effective zero lower bound.
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