This study examines the role of oil price shocks in U.S. economic activity. Contrary to the existing literature which finds that increases in oil prices are associated with U.S. economic downturns and recessions, our results indicate that the macroeconomic impact of oil price shocks has shifted from being recessionary to expansionary when estimating the model for the pre- and post- U.S. energy autonomy period respectively. Furthermore, the positive effect of oil price shocks on economic activity becomes even more pronounced after the U.S. transitioned from a net crude oil and petroleum importer to a net exporter in October 2019.

Amid escalating geopolitical tensions and increasing market volatility, this paper examines the dynamic interlinkages between global energy markets and key agricultural and metal commodities, using a high-frequency DCC-GARCH connectedness framework. Drawing on a rich dataset spanning January 2015 to November 2024, a period punctuated by the COVID-19 pandemic, the Russia–Ukraine war, Middle East instability, and three U.S. elections, we uncover time-varying volatility spillovers and shifting systemic roles across asset classes. We also identify structural asymmetries in how markets respond to geopolitical acts versus threats, with threats generating a persistent risk premium in energy markets. Our findings carry important implications for investors, policymakers, risk managers and energy market participants seeking to understand the volatility transition mechanism, energy market stability and the diversification opportunities which may rise during periods of heightened geopolitical uncertainty.

How do climate-policy uncertainty and climate shocks affect systemic risk within clean and fossil-fuel energy markets? Could more advanced connectedness models help reduce this risk? This study investigates these concerns through the dynamic interconnections between climate risk and the leading energy commodities: natural gas, crude oil, and clean energy. Employing innovative text-based climate uncertainty indices capturing natural disasters, global warming, international summits, and U.S. climate policy, we apply the frequency-Quantile VAR model to unveil the asymmetric spillovers across time horizons and return quantiles. Our results show that events like the U.S. withdrawal from the Paris Agreement and the 2024 U.S. presidential election, significantly enhance interconnections among both renewable and conventional fossil-fuel energy markets. We also find that climate-policy uncertainty stemming from U.S. climate policy and international summits, consistently transmits risk in the high quantiles, driving both short-term volatility and long-term structural repricing in the energy commodity market. Our findings are useful guidelines for traders, portfolio managers, and policymakers aiming to hedge against tail risks and adapt to a decarbonizing global economy.

Global economic activity is surrounded by increasing uncertainties from various sources. In this paper, we focus on commodity prices and estimate a global commodity uncertainty factor by capturing comovement in volatilities of major agricultural, metals and energy commodity markets through a group-specific Dynamic Factor Model. Then, by computing impulse response functions estimated using a small-scale Structural VAR model, we find that an increase in the common commodity price uncertainty results in a substantial and persistent drop in investment and trade, for a set of emerging and advanced economies. We also show that a global commodity uncertainty shock is more detrimental for shortand long-term economic growth than usual financial and economic policy uncertainty shocks. Last, our methodology turns out to be an efficient way to disentangle "good" and "bad" macroeconomic effects of oil price uncertainty: when an oil price uncertainty shock is common to all commodities, then the macroeconomic effect is likely to be negative, similar to a global demand shock. However, when the uncertainty shock is only specific to the oil market, the short-run effect tends to be positive.

The role of oil price shocks in economic activity and inflation is a controversial but key input to economic policy. To examine these relations, we employ a refined measure of oil shocks based on decomposing realized volatility and estimated using intraday oil futures data. In new results, we find that asymmetric shocks driven by oil price increases (decreases) are actually associated with rising (falling) economic activity, particularly in the US case; while a symmetric volatility channel confirms that increasing oil price volatility negatively affects economic activity, particularly for the EU. Finally, we show that the inflationary effect of rising oil prices holds not only for the US economy, but for the rest of the world.

ESG activities are a forward-looking measure to prevent risks from negative externalities. Linking ESG scores with the crude oil market, we assess their mitigating effect on returns during periods of rising oil price volatility. We examine the interplay between ESG scores and crude oil volatility’s impact on returns. Interestingly, this interaction transforms ESG into an insurance-like hedge, protecting returns as volatility increases. Notably, we identify a pivotal turning point at relatively low volatility levels. Below this threshold, ESG activities lack effectiveness, but when volatility surpasses it, their hedging power becomes pronounced. This effect intensifies as volatility rises, rewarding ESG leaders more significantly. Our sectoral and quantile analyses corroborate these findings. Overall, our findings support the role of ESG activities as a “safe haven” in times of financial turmoil, focusing its contribution to the interplay between ESG and oil volatility on periods of heightened uncertainty in the crude oil market.

This paper examines the validity of the tourism-led economic growth hypothesis for the Euro Area economies. We employ both linear and nonlinear Autoregressive Distributed Lag (ARDL) cointegration approaches to examine the symmetric and asymmetric effects of tourism on economic growth. Furthermore, we control for the presence of structural breaks in the time series, which account for the recent financial and debt crises in the Euro Area. The results support the positive impact of tourism on economic growth for most of the Euro Area economies and are robust to alternative tourism measures. The findings indicate that an asymmetric impact exists both in the long and the short run. Positive and negative shocks in tourism and the real exchange rate result in significantly different effects, both in terms of sign and magnitude, on economic growth.

We examine the impact of the volatility of the US Treasury yield curve slope (term spread volatility) on US economic activity within a VAR framework. Our findings show that a positive shock to term spread volatility leads to a persistent decline in US industrial production. Moreover, our econometric results are the first to demonstrate that term spread volatility absorbs the macroeconomic predictive information contained in the level of the term spread. Finally, the negative effect of term spread volatility remains robust in alternative VAR models, as well when including popular uncertainty proxies such as the VIX and the EPU indexes.

We empirically show the role of supply and demand shocks as drivers of short and long-run price uncertainty in the crude oil market. We first define oil price uncertainty as the purely unforecastable component of oil price fluctuations and show that uncertainty of the short-run oil price fluctuations is driven by oil supply shocks, while the uncertainty for medium and long-run forecast horizons is mainly caused by aggregate demand. While our findings on the impact of oil supply disruptions on oil price uncertainty are in line with the implications of the theory of storage, we do not find similar results for the medium and the long, whereby the global demand shocks are found to be the main driver for the increasing oil price uncertainty. Interestingly, we show that the recessionary effect of short and medium-horizon uncertainty shocks, we find that long-run oil price uncertainty shocks lead to expansions in global economic activity.

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In this paper we empirically examine the impact of oil price uncertainty shocks on US stock market volatility. We define the oil price uncertainty shock as the unanticipated component of oil price fluctuations. We find that our oil price uncertainty factor is the most significant predictor of stock market volatility when compared with various observable oil price and volatility measures commonly used in the literature. Moreover, we find that oil price uncertainty is a common volatility forecasting factor of S&P500 constituents, and it outperforms lagged stock market volatility and the VIX when forecasting volatility for medium and long-term forecasting horizons. Interestingly, when forecasting the volatility of S&P500 constituents, we find that the highest predictive power of oil price uncertainty is for the stocks which belong to the financial sector. Overall, our findings show that financial stability is significantly damaged when the degree of oil price unpredictability rises, while it is relatively immune to observable fluctuations in the oil market.

In this study, we examine whether the changes in the shape of the yield curve are a significant determinant of stock market volatility. Using the foundations of the dividend growth model, we extend the model to incorporate and relate the shape of the yield curve effects with the transmission from bond to equity market volatility. When including the risk premium and hedging premium components in our model, we theoretically show and empirically verify that the shape of the yield curve is a significant determinant of equity market volatility in major European equity markets. Finally, our forecasting models show that the shape of the yield curve contains significant forecasting power when used as predictor of European stock market volatility.

We study the effect of supply and demand-induced oil price uncertainty on the cost of debt for a sample of US loans credited during the 1990 to 2019 period. We estimate oil price uncertainty following Jurado et al.'s study, whereby oil price uncertainty is captured by forecasting the unpredictable fluctuations of oil prices. Interestingly, our findings reveal that oil price uncertainty induced by supply shocks increases cost of credit, while oil price uncertainty driven by demand shocks decreases the cost of bank loans. In further analysis, the positive association between supply induced oil price uncertainty and interest loan spread is more pronounced for major users of oil, while the negative effect of demand driven oil price uncertainty on the cost of bank loans is stronger for firms that belong to industries that produce oil. Overall, our findings feed into the emerging discussion of the differentiating effects of oil price uncertainty on micro-level outcomes and provide useful implications for both bankers and borrowing firms.JEL Classification: G20 - Financial Institutions and Services: General; Q41 - Energy: Demand and Supply; Prices; Q43 - Energy and the Macroeconomy

In this paper, we empirically examine the predictive power of oil price uncertainty on time-varying volatility in the oil futures market. Quantifying oil price uncertainty as the purely unforecastable component of oil price changes, we find this measure has significant predictive power on the return volatility of crude oil futures for horizons up to 9 months ahead. Moreover, our oil price uncertainty factor outperforms the realized oil price volatility. In addition, our structural vector autoregression model shows that the effect of oil price uncertainty shock on oil-market volatility is higher in magnitude and persistence when compared with the effect of aggregate demand, oil demand, supply, and oil price volatility shocks.

We report a significant downward trend in the convenience yield for holding physical inventory in agricultural commodity futures markets, attributing this negative trend to speculative demand shocks, which in turn, leads in decreasing agricultural convenience yields. Moreover, agricultural convenience yields appear negative on average during the recent financialization (of commodities) period. We additionally show that the response of agricultural convenience yields to commodity price uncertainty and supply shocks is much less pronounced in magnitude and persistence compared to that of hedging demand shocks. Overall, our analysis verifies the Keynesian theory of normal backwardation by showing a long-lasting positive response of agricultural convenience yields to a hedging demand shock, thereby leaving the hedging demand as the most significant factor explaining the less frequently observed backwardations in agricultural futures markets.

Exchange rates of commodity exporting countries, generally known as commodity currencies, are often considered to be driven by some specific commodity prices. In this paper, we show that the uncertainty common to a basket of commodity prices is also a significant driver of exchange rate dynamics for a panel of commodity exporting countries. In particular, an increase in global commodity price uncertainty leads to a short-run depreciation of the effective exchange rate in commodity currency countries, followed by a medium-term rebound. We document that this pattern is specific to commodity currencies and is not visible on benchmark currencies like the euro or the U.S. dollar, the latter acting as a typical safe haven currency. We refer to this pattern as the “commodity uncertainty currency” property.

In this paper, we look at the role of various oil jump tail risk measures as drivers of both U.S. headline and core inflation. Those measures are first computed from high-frequency oil future prices and are then introduced into standard regression models in order to (i) assess in-sample determinants of inflation, (ii) assess overtime the evolution of inflation drivers, (iii) estimate impulse response functions and (iv) forecast inflation out-of-sample for various horizons. Empirical results suggest that oil jump tail risk measures contain useful information to describe inflation dynamics, generally leading to upward inflationary pressures. Even after controlling from standard variables involved in a Phillips curve, goodness-of-fit measures show evidence of a gain, in particular for headline inflation. Overall, we observe that oil jump tail risk measures are contributing more to inflation dynamics since the Covid-19 crisis.

Recent evidence has shown that hybrid models for credit ratings are important when assessing the risk of firms. Within this stream of literature, we aim to provide novel evidence on how hard (quantitative), soft (qualitative), and market information predict corporate defaults for unlisted firms by implementing the Cox proportional hazard model. We address this research question by exploiting a unique proprietary dataset comprising of detailed information on internal credit ratings of European unlisted mid-sized firms and compute their Merton's distance-to-default (DD) measure of credit risk with market data collected on comparable publicly listed companies. Our results show that the bank's use of hard, soft, and market information when assessing the credit ratings of borrowers has a significant influence on the prediction of their defaults. Further, we investigate the significant influence of soft information in predicting corporate defaults by drawing on two separate processes through which loan officers can inject soft information in credit scoring, that is, 'codified' and 'uncodified' discretion. Finally, when we distinguish between the loan officer's discretion to upgrade or downgrade an applicant's credit score, we find that it is the upgrade that is likely to predict a lower probability of a firm defaulting. This study contributes to the policy debate on safeguarding the banking sector's continuity by positing that integrating market information into banks' hybrid methods of credit rating helps to improve the accuracy in predicting unlisted firms' credit risk that is useful to policy makers for the design of future forward-looking financial risk management frameworks.

We examine the forecasting power of the volatility of the slope of the US Treasury yield curve on US stock market volatility. Consistent with theoretical asset pricing models, we find that the volatility of the slope of the term structure of interest rates has significant forecasting power on stock market volatility for forecasting horizon ranging from 1 up to 12 months. Moreover, the term structure volatility has significant forecasting power when used for volatility predictions of the intra-day returns of S&P500 constituents, with the predictive power being higher for stocks belonging to the telecommunications and financial sector. Our forecasting models show that the forecasting power of yield curve volatility is higher to and absorbs that of Economic Policy Uncertainty and Monetary Policy Uncertainty, showing that the main channel through which the yield curve volatility affects the stock market is not only related with uncertainty about monetary policy actions or policy rates, but also with uncertainty regarding the future cash flows and dividend payments of US equities. Lastly, we show that the forecasting power of term structure volatility significantly increases during the post-2007 Great recession period which coincides with the Fed adopting unconventional monetary policies to stimulate the economy.