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This paper examines the impact of macroeconomic and risk factors on the profitability and volatility of professional momentum portfolios for the US, the UK, Japan and Germany, for the period 1998–2018. Many of the factors employed, such as energy price changes and economic policy uncertainty, have been largely neglected in the relevant literature.

Regression analysis, VECTOR AUTOREGRESSION (VAR), Panel-VAR, Variance Decomposition Analysis

The results indicate that, since the financial crises in the US and the EU, energy prices and economic-policy uncertainty have become important return determinants, along with market-related uncertainty that seems to have a stable impact over time, especially for the U.S. and U.K. portfolios.

Economic policy uncertainty significantly affects contemporaneous momentum returns in the US, UK and Japan, mainly between 2007 and 2018, while market-related uncertainty affects all markets during all subperiods. In addition, the variance of market-related uncertainty (VIX) explains a large percentage of the variance in the momentum returns for the US, UK and Germany.

The main implication of the findings for portfolio managers is that a manager may increase (decrease) exposure to the momentum factor during optimistic (pessimistic) periods and during periods of rising energy prices (high economic policy and market-related uncertainty).

The paper examines the impact of factors, such as energy prices and economic policy uncertainty, which have been largely neglected in the relevant literature on the possible drivers of the momentum strategies. It employs professional portfolios that are often used in practice as benchmark indexes.

This paper examines the effect of oil price uncertainty on corporate social responsibility (CSR) for 507 US firms over the period 1985–2019.

To investigate the nexus between oil price uncertainty and CSR, we have proceeded with a fixed-effects panel regression model over the period 1985–2019.

Using a dataset of 507 US firms, different specifications of CSR and two alternatives measures of oil price uncertainty, we show that oil price uncertainty negatively influences the CSR in the global US panel and firm's characterized panel. This negative effect is dependent on firms' size, firm's age and value of book share of firms.

US firms are exposed to more risk when carrying high levels of debt, resulting in reduced spending to improve social and environmental conditions. While the negative effect of oil price uncertainty on CSR is exacerbated in economic crisis periods.

US firms are influenced by energy price volatility especially by oil price fluctuations which are the main factor of American economic growth. The rise of oil price uncertainty reduces sustainable corporate development and investment in the green economy.

Rethinking renewable energies as an alternative solution in order to guarantee the performance and sustainability of social, environmental and cultural activities.

Young and small firms, lower-share outstanding firms and high book value per share firms are the most negatively affected by oil price uncertainty and therefore their social responsibilities are reduced. However, by introducing interaction variables in the main model, we find that the most indebted firms on one hand and big firms and high-number shares outstanding firms, on the other hand, are the most influenced by oil price uncertainty which consequently limits their social and environmental responsibility.

This study investigates the nexus between the returns on oil prices (OP) and unemployment (UR) while taking into account the influences of two of the most representative measures of uncertainty, the Baker et al. (2016) and Caldara and Iacovello (2021) indexes of economic policy uncertainty (EP) and geopolitical risks (GP), in the relationship.

The authors use data on the US, Canada, France, Italy, Germany and Japan from January 2000 to February 2022 and the UK from January 2000 to December 2021. The authors then apply the continuous wavelet transform (CWT), wavelet coherence (WC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) to examine the returns within a time and frequency framework.

The CWT tracks the movement and evolution of individual return series with evidence of high variances and heterogenous tendencies across frequencies that also align with critical events such as the GFC and COVID-19 pandemic. The WC reveals the presence of a bidirectional relationship between OP and UR across economies, showing that the two variables affect each other. The authors’ findings establish the predictive influence of oil price on unemployment in line with theory and also show that the variation in UR can impact the economy and alter the dynamics of OP. The authors employ the PWC and MWC to capture the impact of uncertainty indexes in the co-movement of oil price and unemployment in line with the theory of “investment under uncertainty”. Taking into account the common effects of EP and GP, PWC finds that uncertainty measures significantly drive the co-movement of oil prices and unemployment. This result is robust when the authors control for the influence of economic activity (proxied by the GDP) in the co-movement. Furthermore, the MWC reveals the combined intensity, strength and significance of both oil prices and the uncertainty measures in predicting unemployment across countries.

This study investigates the relationship between oil prices, uncertainty measures and unemployment under a time and frequency approach.

1. Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

2. We account for uncertainty measures in the dynamics of oil prices and unemployment.

3. We observe a bidirectional relationship between oil prices and unemployment.

4. Uncertainty measures significantly drive oil prices and unemployment co-movement.

5. Both oil prices and uncertainty measures significantly drive unemployment.

Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

We account for uncertainty measures in the dynamics of oil prices and unemployment.

We observe a bidirectional relationship between oil prices and unemployment.

Uncertainty measures significantly drive oil prices and unemployment co-movement.

Both oil prices and uncertainty measures significantly drive unemployment.

This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central and Eastern European countries (CEEC).

In the research process, the authors use the Bayesian method of inference for the two applied methodologies – Markov switching generalized autoregressive conditional heteroscedasticity (GARCH) model and quantile regression.

The results clearly indicate that oil price uncertainty has a low effect on output in moderate market conditions in the selected countries. On the other hand, in the phases of contraction and expansion, which are portrayed by the tail quantiles, the authors find negative and positive Bayesian quantile parameters, which are relatively high in magnitude. This implies that in periods of deep economic crises, an increase in the oil price uncertainty reduces output, amplifying in this way recession pressures in the economy. Contrary, when the economy is in expansion, oil price uncertainty has no influence on the output. The probable reason lies in the fact that the negative effect of oil volatility is not strong enough in the expansion phase to overpower all other positive developments which characterize a growing economy. Also, evidence suggests that increased oil uncertainty has a more negative effect on industrial production than on real GDP, whereas industrial share in GDP plays an important role in how strong some CEECs are impacted by oil uncertainty.

This paper is the first one that investigates the spillover effect from oil uncertainty to output in CEEC.

This paper aims to investigate how different uncertainty shocks affect international housing prices.

The 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.

Some 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.

The 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.

The purpose of this paper is to empirically analyze the impact of macroeconomic uncertainty on a large sample of 19 commodity markets.

The authors rely on Jurado et al.’s (2015) measure of macroeconomic uncertainty based on a wide range of monthly macroeconomic and financial indicators and estimate a threshold VAR model to assess whether the impact of macroeconomic uncertainty on commodity prices differs under the high- or low-uncertainty state.

The findings show that positive macroeconomic uncertainty shocks affect commodity prices returns negatively on average and the impact of macroeconomic uncertainty is generally higher in high-uncertainty states compared with low-uncertainty states. Besides, although the safe-haven role of precious metals is confirmed, energy and industrial markets are more sensitive to short-run and long-run macroeconomic uncertainty, respectively.

The findings in this study suggest that commodity prices reflect not only the level of economic fundamental but also the volatility of economic fundamental.

This study empirically analyzes and verifies the influence of macroeconomic uncertainty not only on oil prices but also on four groups of 19 raw materials. As for the methodological issues, the authors rely on a structural threshold vector autoregressive specification for modeling commodity price returns to account for potentially different effects depending on the macroeconomic uncertainty states.

The purpose of this research is to examine the way uncertainty plays a role in built land prices. This paper provides basic real option pricing models of land prices on the demand side in central Tokyo. The model in this research analyzes micro land prices covering individual lot data provided by the Land Price Index. Since land prices are determined by both macro economic environment and micro lot‐specific attributes, this paper utilizes both time‐series economic data and cross‐sectional lot‐specific data. The model incorporates both time‐series (macro) and cross‐sectional (micro) data including uncertainty terms. In addition to the total uncertainty in asset prices over years, this research also gives some ideas of cross‐sectional uncertainty in land price variations by utilizing cross‐sectional amenity variables. These cross‐sectional and time‐series variables including the two uncertainty variables are arithmetically combined and the OLS method is conducted. The data set consists of 4,368 land price data from 1985 through 2000. The results from the option‐based models favor the application of the real option theory in land prices. The total uncertainty with respect to built asset return has a substantial effect on increasing land prices, which implies that an increase in uncertainty leads to an increase in land prices.

In the past few years, numerous economic uncertainty challenges have occurred globally. These uncertainties grasp the attention of the researchers and they examine the role of economic policy uncertainties in several aspects. Therefore, this study contributes to the literature by exploring the house prices volatility and economic policy uncertainty nexus in G7 countries.

The authors applied the newly introduced econometric technique, the GARCH-MIDAS model, to the sample size of January 1998–May 2021.

The result shows a significant relationship between house prices volatility and economic policy uncertainty. Moreover, economic policy uncertainty acts as a significant determinant of house prices volatility. In addition, the out-of-sample also shows that the economic policy uncertainty is an effective predictor and the GARCH-MIDAS has a better predictive ability.

This paper makes a unique contribution to the literature with reference to developed economies, being a pioneering attempt to investigate the GARCH-MIDAS model to analyze the relationship between housing prices volatility and economic policy uncertainty by applying more rigorous and advanced econometric techniques.

The purpose of this paper is to investigate the possible asymmetric response of inflation expectations to oil price and policy uncertainty shocks.

The authors used the test of asymmetric impulse responses proposed by Kilian and Vigfusson (2011) to explore the issue of asymmetry.

Unlike other studies that assume symmetric effects, this study finds asymmetric effects of oil price and policy uncertainty on inflation expectations for positive and negative shocks and for pre- and post-financial-crisis periods. In particular other things being same, a same magnitude oil price shock has greater effect on inflation expectations in post-crisis period than in pre-crisis period. Moreover, in post-crisis period a positive increasing oil price shock has greater effect on inflation expectations than a negative decreasing oil price shock.

The paper concludes that FED’s greater focus on output stabilization since financial crisis has made inflation expectations less anchored and a sudden surge in oil price may quickly trigger inflation through inflation expectations.

Exploring the issue of the possible asymmetric effects of oil price and economic policy uncertainty on inflation expectations is a relatively new topic (as other studies only assumed symmetry and did not investigate the possible asymmetry in this regard).

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.