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This study examines the value implications of oil price uncertainty for investors in diversified firms using a sample of 922 USA firms from 2001 to 2019.

Our study employs a panel dataset to examine the value implications of oil price uncertainty for diversified firm investors. We consider several alternative specifications to account for unobserved factors and measurement errors that could potentially bias our results. In particular, we use alternative measures of the excess value of diversified firms and oil price uncertainty, additional control variables, fixed-effects models, the Oster test, impact threshold for confounding variable (ITCV) analysis, two-stage least square instrumental variable (2SLS-IV) analysis and the system-GMM model.

We find that the excess value of diversified firms, relative to a benchmark portfolio of single-segment firms, increases with high oil price uncertainty. The impact of oil price uncertainty is asymmetric, as corporate diversification is value-increasing for diversified firm investors only when the volatility is due to positive oil price changes and amidst supply-driven oil price shocks. The excess value increases irrespective of diversified firms’ financial constraints and oil usage. Diversified firms become conservative in their internal capital allocations with high oil price uncertainty. Such conservatism is value-increasing for diversified firm investors, as it supports higher performance in response to oil price uncertainty.

Our study has three important implications: first, they are relevant to investors in understanding the portfolio value implications of oil price uncertainty. Second, they are helpful for firm managers while comprehending the value-relevant implications of internal capital allocations. Finally, our findings are policy relevant in the context of the future of diversified firms in developed markets.

This research unfolds a holistic association between economic policy uncertainty (EPU) and three important markets (oil, stock and gold) in the Indian context. To do same, the current study uses the monthly dataset of each variable spanning from November 2005 to March 2022.

The authors have portrayed the wavelet-based coherence, correlation and covariance plots to explore the interaction between EPU and markets' behavior. Then, a wavelet-based quantile on quantile regression model and wavelet-based Granger causality has been applied to examine the cause-and-effect relation and causality between the EPU and markets.

The authors’ findings report that the Indian crude oil buyers do not need to consider Indian EPU while negotiating the oil deals in the short term and medium term. However, in case of the long-term persistence of uncertainty, it becomes difficult for a buyer to negotiate oil deals at cheap rates. EPU causes unfavorable fluctuation in the stock market because macroeconomic decisions have a substantial impact on it. The authors have also found that gold is a gauge for economic imbalances and an accurate observer of inflation resulting from uncertainty, showing a safe haven attribute.

The authors’ work is original in two aspects. First, their study solely focused on the Indian economy to investigate the impact and causal power of Indian EPU on three major components of the Indian economy: oil, stock and gold. Second, they will provide their findings after analyzing data at a very microlevel using a wavelet-based quantile on quantile and wavelet-based Granger causality.

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 study aims to examine the uncertainty spillover among eight important asset classes (cryptocurrencies, US stocks, US bonds, US dollar, agriculture, metal, oil and gold) using weekly data from 2014 to 2020. This study also examines the US macro uncertainty and US financial stress spillover on these assets.

The authors use time–frequency connectedness method to study the uncertainty spillover among the asset classes.

This study’s findings revealed that the uncertainty spillover is time-varying and peaked during the 2016 oil supply glut and COVID-19 pandemic. US stocks are the highest transmitter of uncertainty to all other assets, followed by the US dollar and oil. US stocks (US dollar and oil) transmit uncertainty in long (short) term. Furthermore, US macro uncertainty is the net transmitter of uncertainty to the US stocks, industrial metals and oil markets. In contrast, US financial stress is the net transmitter of uncertainty to the US bonds, cryptocurrencies, the US dollar and gold markets. US financial stress (US macro uncertainty) has long (short)-term effects on asset price volatility.

This study complements the studies on volatility spillover among the important asset classes. This study also includes recently financialized asset classes such as cryptocurrencies, agricultural and industrial commodities. This study examines the macro uncertainty and financial stress spillover on these assets.

This study aims to examine the multiscale predictability power of COVID-19 deaths and confirmed cases on the S&P 500 index (USA), CAC30 index (France), BSE index (India), two strategic commodity futures (West Texas intermediate [WTI] crude oil and Gold) and five main uncertainty indices Equity Market Volatility Ticker (EMV), CBOE Volatility Index (VIX), US Economic Policy Uncertainty (EPU), CBOE Crude Oil Volatility Index (OVX) and CBOE ETF Gold Volatility Index (GVZ). Furthermore, the authors analyze the impact of uncertainty indices and COVID-19 deaths and confirmed cases on the price returns of stocks (S&P500, CAC300 and BSE), crude oil and gold.

The authors used the wavelet coherency method and quantile regression approach to achieve the objectives.

The results show strong multiscale comovements between the variables under investigation. Lead-lag relationships vary across frequencies. Finally, COVID-19 news is a powerful predictor of the uncertainty indices at intermediate (4–16 days) and low (32–64 days) frequencies for EPU and at low frequency for EMV, VIX, OVX and GVZ indices from January to April 2020. The S&P500, CAC30 and BSE indexes and gold prices comove with COVID-19 news at low frequencies during the sample period. By contrast, COVID-19 news and WTI oil moderately correlated at low frequencies. Finally, the returns on equity and commodity assets are influenced by uncertainty indices and are sensitive to market conditions.

This study contributes to the literature by exploring the time and frequency dependence between COVID-19 news (confirmed and death cases) on the returns of financial and commodity markets and uncertainty indexes. The findings can assist market participants and policymakers in considering the predictability of future prices and uncertainty over time and across frequencies when setting up regulations that aim to enhance market efficiency.

This paper aims to investigate the relationship between sustainable investments and a series of uncertainties from January 2014 to December 2021, including many economic and political turbulences and the COVID-19 pandemic.

The authors use Rényi’s transfer entropy method, a nonparametric flexible tool that considers both the center distribution and lower quantiles, capturing extreme rare events that give additional insights to analysis.

The authors’ results indicate significant bidirectional information transmissions between the crude oil volatility and sustainability indices. The authors report information flows between the cryptocurrency uncertainty and sustainability indices considering tail events. The results are essential for market participants making decisions during turbulent times.

This paper is carried out for a variety of uncertainty measures and environmental, social and governance (ESG) portfolios of both developed and developing markets. It adds to literature in terms of methodology used. Rényi’s transfer entropy methodology is first used to measure the relationship between uncertainties and ESG investments.

This study examines the relationship between oil price uncertainty (OPU) and corporate inventory investments using a sample of 6,072 USA manufacturing firms from 1992 to 2019.

The author's study employs a panel dataset to examine the relationship between OPU and corporate inventory investments. The author uses several alternative specifications such as fixed effects models, an instrumental variable analysis, an impact threshold for confounding variable (ITCV) analysis, alternative measures, additional control variables and the percent bias analysis to account for endogeneity issues.

Corporate inventory investments decrease in response to high OPU. This decrease in inventory investments happens regardless of firms' expected stockout costs, information environment and reliance on external financing. As a potential mechanism, an uncertainty-induced increase in cash holdings contributes to this reduction in inventory investments. Also, the effect of OPU is non-linear and asymmetric. In response to the volatility of positive (negative) oil price changes, inventory investments decrease (increase) up to a certain point and increase (decrease) after that. Further, uncertainty-induced adjustments in inventory investments positively influence the operating performance of firms.

The author's study adds to the growing literature that examines the impact of OPU on corporate outcomes. Inventory investments directly affect business operations and could better reflect firms' responses to an uncertain environment.

The authors empirically aim to study the implications of uncertainty generated by oil price volatility on some key macroeconomic variables, including production, exchange rates and interest rates, of both oil-exporting and oil-importing countries. Using a block exogeneity structural Vector Auto Regression (VAR) model that mutes the effects of domestic variables on global factors and that is suitable for small open economies because of significant differences in the responses of domestic production in oil-importing countries will most likely decrease through reducing planning horizons, postponing investment projects and relocating resources more inefficiently.

The authors integrated into the structural vector autoregressive (SVAR) model the block exogeneity feature since all the countries in this study are small open economies that cannot influence the global economic variables. The block exogeneity feature imposes the restriction that the domestic variables have neither a contemporaneous nor a lagged impact on the global variables. This model has eight variables: oil price volatility, world demand and federal funds rate as the global variables; and domestic production, monetary aggregate, inflation rate, exchange rate and interest rate as domestic variables. The authors assemble the data for 12 developing countries for which the necessary data for the analysis are available: six oil exporting countries (Russia, Saudi Arabia, Iran, Kazakhstan, Mexico and Colombia) and six oil importing countries (Turkey, India, Philippines, Poland, South Africa and Indonesia).

The results point out significant differences in the responses of macroeconomic variables to oil price volatility shocks between oil-exporting and oil-importing countries. Furthermore, the local currencies of these countries depreciate due to concerns about possible current account worsening. In response to the shock, domestic interest rates are reduced so as to alleviate the negative exposure of the shock on domestic economic activity. While domestic production in some oil-exporting countries (i.e. Russia, Saudi Arabia and Iran) increases during oil price uncertainty; in some other countries (i.e. Mexico, Kazakhstan and Colombia), domestic production decreases.

Several components of the study contribute to its novelty. One of them is the period under consideration. The time frame that encompasses the most significant geopolitical and financial events, such as the Middle East Spring and the global financial crisis of 2007–2008. The research was conducted using the block-exogeneity SVAR model, which includes 12 oil exporting and importing developing countries. With this model, the global dynamics, particularly the energy market, that these nations may influence and are influenced by, i.e. global and nonglobal factors can be constrained. This makes it easy to determine the various effects prices have on macroeconomic variables.

1. Oil prices and volatility still matter to the global economy

2. Monetary and fiscal policy interventions in response to oil price volatility create uncertainty and impede investment activity

3. The response of macroeconomic variables to volatility shocks in oil prices varies across oil importers and exporters

4. Interest rates help stabilize production in oil-importing economies that have well-functioning financial markets

Oil prices and volatility still matter to the global economy

Monetary and fiscal policy interventions in response to oil price volatility create uncertainty and impede investment activity

The response of macroeconomic variables to volatility shocks in oil prices varies across oil importers and exporters

Interest rates help stabilize production in oil-importing economies that have well-functioning financial markets

This study aims to uncover the main predictors of financial distress in the Gulf Cooperation Council (GCC) countries using a wide range of global factors and asset classes.

This study uses novel approaches that take into account extreme events as well as the nonlinear behavior of time series over various time intervals (i.e. short, medium and long term) and during boom and bust episodes. This study primarily uses the conditional value at risk (CoVaR), the quantile multivariate causality test and the partial wavelet coherence method. The data collection period ranges from March 2014 to September 2022.

US T-bills and gold are the primary factors that can increase financial stability in the GCC region, according to VaRs and CoVaRs. More proof of the predictive value of the oil, gold and wheat markets, as well as geopolitical tensions, uncertainty over US policy and volatility in the oil and US equities markets, is provided by the multivariate causality test. When low extreme quantiles or cross extreme quantiles are taken into account, these results are substantial and sturdy. Lastly, after adjusting for the effect of crude oil prices, this study’s wavelet coherence results indicate diminished long-run connections between the GCC stock market and the chosen global determinants.

Despite the implications of the author’s research for decision makers, there are some limitations mainly related to the selection of Morgan Stanley Capital International (MSCI) GCC ex-Saudi Arabia. Considering the economic importance of the Kingdom of Saudi Arabia (KSA) in the region, the author believes that it would be better to include this country in the data to obtain more robust results. In addition, there is evidence in the literature of the existence of heterogeneous responses to global shocks; some markets are more vulnerable than others. This is another limitation of this study, as this study considers the GCC as a bloc rather than each country individually. These limitations could open up further research opportunities.

These findings are important for investors seeking to manage their portfolios under extreme market conditions. They are also important for government policies aimed at mitigating the impact of external shocks.

This paper aims to understand the effect of a change in Economic Policy Uncertainty (EPU) of India and the USA on investors' sentiment in the Indian context, consisting of Sensex returns and volatility index (Vix).

The authors employ bounds testing approach to cointegration to capture the short-and long-run effects of EPU on investors' sentiment, along with impulse response functions and variance decompositions to check the effect of a shock on Sensex and Vix.

The study concludes the existence of a cointegrating relationship for both models, that is, Vix and Sensex. In the long-run, changes in EPU_India affect Vix and Sensex positively and negatively, respectively. On the other hand, EPU_USA affects Vix and Sensex positively. Furthermore, Gregory and Hansen (1996) cointegration with endogenous structural break reveals a long-run cointegrating relationship for both models.

The effect of EPUs on investors' sentiment reveals that when there is an uncertain event that adversely affects the stock prices, investors should not make haste to take a decision as the impact on stock prices perturbation might be temporary. Therefore, one should persevere for the dip in prices to hit the desired target.

Various studies look at the effect of cross-country EPU on the home country, However, there is no such study in the Indian context. The present study examines the impact of India's EPU on investors' sentiments after controlling the USA's EPU, one of India's largest trading partners and a key determinant of global economic policy.