Climate shocks and wealth inequality in the UK: evidence from monthly data.

This paper investigates both the linear and nonlinear effects of climate risk shocks on wealth inequality in the UK using the local projections (LPs) method, based on high-frequency, i.e., monthly data. The linear results show that climate risk shocks lead to an increase in wealth inequality in the longer term. The nonlinear results present some evidence of heterogeneous responses of wealth inequality to climate risk variable shocks between high- and low-climate risk regimes. The findings highlight the disproportionate increased burden of climate change on households that are already experiencing poverty, particularly households in high-climate risk areas. As such, measures to mitigate the adverse effects of climate change need to be tailored so as not to overburden the poor.

Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach.

Because the U.S. is a major player in the international oil market, it is interesting to study whether aggregate and state-level economic conditions can predict the subsequent realized volatility of oil price returns. To address this research question, we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility (HAR-RV) model. To estimate the models, we use quantile-regression and quantile machine learning (Lasso) estimators. Our estimation results highlights the differential effects of economic conditions on the quantiles of the conditional distribution of realized volatility. Using weekly data for the period April 1987 to December 2021, we document evidence of predictability at a biweekly and monthly horizon.

Forecasting the realized variance of oil-price returns: a disaggregated analysis of the role of uncertainty and geopolitical risk.

We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical risks for forecasting the future realized volatility of oil-price (WTI) returns over the monthly period from 1985:01 to 2021:08. Using machine-learning techniques, we find that adding the disaggregated metrics to the array of predictors improves the accuracy of forecasts at intermediate and long forecast horizons, and mainly when we use random forests to estimate our forecasting model.

Do German economic research institutes publish efficient growth and inflation forecasts? A Bayesian analysis.

We use Bayesian additive regression trees to reexamine the efficiency of growth and inflation forecasts for Germany. To this end, we use forecasts of four leading German economic research institutes for the sample period from 1970 to 2016. We reject the strong form of forecast efficiency and find evidence against the weak form of forecast efficiency for longer-term growth and longer-term inflation forecasts. We cannot reject weak efficiency of short-term growth and inflation forecasts and of forecasts disaggregated at the institute level. We find that Bayesian additive regression trees perform significantly better than a standard linear efficiency-regression model in terms of forecast accuracy.

The predictive power of oil price shocks on realized volatility of oil: A note.

This paper examines the predictive power of oil supply, demand and risk shocks over the realized volatility of intraday oil returns. Utilizing the heterogeneous autoregressive realized volatility (HAR-RV) framework, we show that all shock terms on their own, and particularly financial market driven risk shocks, significantly improve the forecasting performance of the benchmark HAR-RV model, both in- and out-of-sample. Incorporating all three shocks simultaneously in the HAR-RV model yields the largest forecasting gains compared to all other variants of the HAR-RV model, consistently at short-, medium-, and long forecasting horizons. The findings highlight the predictive information captured by disentangled oil price shocks in accurately forecasting oil market volatility, offering a valuable opening for investors and corporations to monitor oil market volatility using information on traded assets at high frequency.