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We estimate Boosted Regression Trees (BRT) on a sample of monthly data that extends back to 1889 to recover the predictive value of disaggregated news-based uncertainty indexes for U.S recessions. We control for widely-studied standard predictors and use out-of-sample metrics to assess forecast performance. We find that war-related uncertainty is among the top five predictors of recessions at three different forecast horizons (3, 6, and 12 months). The predictive value of war-related uncertainty has fallen in the second half of the 20th century. Uncertainty regarding the state of securities markets has gained in relative importance. The probability of a recession is a nonlinear function of war-related and securities-markets uncertainty. Receiver-operating-characteristic curves show that uncertainty improves out-of-sample forecast performance at the longer forecast horizons. A dynamic version of the BRT approach sheds light on the importance of various lags of government-related uncertainty for recession forecasting at the long forecast horizon.

This paper derives the theoretical skewness in a five-factor affine jump-diffusion model with stochastic variance and jump intensity, and jumps in prices and variances. Numerical analysis shows that all of the uncertainties in this model affect skewness. The information regarding jumps in prices is mainly reflected in the short-term skewness. The skewness for other maturities carries the information that is highly correlated with variance. Furthermore, the theoretical VIX and skewness under a simplified five-factor model are used to fit the market risk-neutral volatility and skewness sequentially. The fitting performances are better than traditional double-jump models.

Several studies have established the predictive power of the yield curve i.e. the difference between long and short-term bond rates and the role of asymmetries in the term structure of bond yields with respect to real economic activity. Using an extensive dataset, comprising 3-month, 1-year, 5-year and 10-year constant maturity Treasury bonds for the Eurozone southern periphery countries – the so-called “PIIGS” – from January 1999 to April 2019, we investigate the links between bond yields of different maturities for the Eurozone southern peripheral countries and we find they co-evolve in line with the predictions of the Expectations Hypothesis theory. We demonstrate the presence of nonlinearities in the term structure, and utilize a multivariate asymmetric two-regime Markov-switching VAR methodology to model them properly. Moreover, we address the economic reasoning behind the introduction of an equilibrium-correction regime-switching approach, hence providing potentially important insights on the behaviour of the entire yield curve. We reveal that the regime shifts are related to the state of the business cycle, particularly in economies in which monetary policy decisions are implemented via changes in short-term rates as a response to deviations of output from equilibrium levels. Our results may have important statistical and economic implications on the behaviour of the term structure of bond yields.

We specify unconventional monetary policy reaction functions for the Fed using linear and nonlinear econometric frameworks. We find that nonstandard policy measures are largely driven by the dynamics of inflation and the output gap, with the effect being particularly strong during QE rounds. Moreover, we uncover the presence of asymmetry and regime dependence in central bank’s actions since the global financial crisis, especially concerning the response of the term spread and the shadow short rate to the growth rate of central bank reserves. From a policy perspective and given the lack of a systematic response of monetary policy to asset price growth in nonstandard times, our findings seem to corroborate the view that concerns about asset price bubbles, financial sector pro-cyclicality and systemic risk should be part of the macro-prudential policy toolkit.

We consider the effects of uncertainty shocks in a nonlinear VAR that allows uncertainty to have amplification effects. When uncertainty is relatively low, fluctuations in uncertainty have small, linear effects. In periods of high uncertainty, the effect of a further increase in uncertainty is magnified. We find that uncertainty shocks in this environment have a more pronounced effect on real economic variables. We also conduct counterfactual experiments to determine the channels through which uncertainty acts. Uncertainty propagates through both the household consumption channel and through businesses delaying investment, providing substantial contributions to the decline in GDP observed after uncertainty shocks. Finally, we find evidence of the ability of systematic monetary policy to mitigate the adverse effects of uncertainty shocks.

In this paper, we set up a generalized periodic asymmetric power GARCH ( PAP - GARCH ) model whose coefficients, power, and innovation distribution are periodic over time. We first study its properties, such as periodic ergodicity, finiteness of moments and tail behavior of the marginal distributions. Then, we develop an MCMC algorithm, based on the Griddy-Gibbs sampler, under various distributions of the innovation term (Gaussian, Student-t, mixed Gaussian-Student-t). To assess our estimation method we conduct volatility and Value-at-Risk forecasting. Our model is compared against other competing models via the Deviance Information Criterion ( DIC ). The proposed methodology is applied to simulated and real data.