Georgios P. Kouretas

In this paper, we investigate the extent to which uncertainty anticipates episodes of high inflation in 30 economies worldwide. Our analysis accounts for several factors, including inflation expectations, real global economic activity, and supply chain disruptions. Using an instrumental variable (IV) approach, we identify exogenous variations in country‐level uncertainty by tracking uncertainty in large global economies, which is particularly relevant for small open economies. The results indicate that periods of high uncertainty are generally associated with low inflation. Importantly, our research reveals two distinct mechanisms through which uncertainty affects inflation. Uncertainty has a negative effect on demand‐driven inflation, while its effect on supply‐driven inflation is positive.

In this paper, we propose a novel methodology to quantify firms' climate‐change transition risk (CCTR) and its implications for credit markets. We utilize the regulatory framework of the European Green Deal's 2050 carbon neutrality roadmap, focusing on large Eurozone firms and their banking relationships. First, we assume a decarbonization pathway aligned with carbon neutrality targets and estimate the impact of CCTR on firm asset values and default probabilities by embedding a revenue–emissions elasticity into a Merton‐style PD model. Second, we link these CCTR‐adjusted PDs to firm‐level syndicated‐loan data to measure the resulting loan‐pricing premium. We find that incorporating CCTR raises average PDs by 0.65 percentage points and lifts loan spreads by 142 bps on average. Firms that deviate from decarbonization paths face an additional interest rate premium equal to 0.3%‐2% in interest costs. We also document significant bank‐level heterogeneity since institutions with stronger E(SG) profiles charge higher premiums, and jurisdictions with stricter climate regulation amplify pricing differences. Our results demonstrate that banks must systematically embed CCTR into credit terms‐failure to do so imply persistent underpricing of transition risk.

We compare the inflation forecasting performance of large language models (LLMs) to traditional models and the Kalman filter approach developed by Hall et al. (2026). Using monthly data for inflation in both the United States and the euro area, we forecast over the period 2020:M1 to 2025:M9; our findings show that all the LLMs significantly improved forecasting performance against the traditional models.

Under the context of the climate change we assess the impact of EU's legislative initiative on European stock markets. Specifically, we focus on its impact on energy and Environmental Social Governance (ESG) sectors for equity returns and volatility for a representative basket of EU countries (participating also in Eurozone) as well as countries outside Eurozone/EU. We derive a cross‐country event analysis for the European green deal (willingness to implement and legal adoption on EU level as long‐term strategy) and national climate laws. Moreover, we assess the efficient market hypothesis (EMH). Second, we expect the announcement effect of the law implementation to have an impact on industry sectoral stock markets' volatility. The EMH is initially exhibited and announcement‐dependent; stock market is pricing announcements inefficiently. We find negative cumulative abnormal returns (CARs) both in the ESG, energy industry sector indexes. Abnormal return (AR) volatility is found per se at the announcement day. The magnitude and sign for abnormal returns as well as cumulative abnormal returns are announcement‐dependent. Heterogeneity is present since ESG (green index) appears to exhibit more statistically significant cumulative abnormal returns than the energy index.

Early studies that used rolling windows found it to be a useful forecasting technique. These studies were, by‐and‐large, based on pre‐2000 data, which were nonstationary. Subsequent work, based on stationary data from the mid‐1990s to 2020, has not been able to confirm that finding. However, this latter result may reflect the fact that there was relatively little structural instability between the mid‐1990s and 2020: The data had become stationary. Following the series of shocks of the early 2020s, this is no longer the case because the shocks produced nonstationarity in the macroeconomic data, such as inflation. Consequently, rolling windows may again be a sensible way forward. The present study assesses this conjecture.

Most studies have found that the Covid‐19 pandemic did not negatively impact the euro area banking industry's performance at an aggregate level. This study explores whether this finding still holds using the return on assets of 16 banking groups operating in the euro area and considering bank‐specific factors, idiosyncrasies related to different exposures of their portfolios to the business cycle and weaknesses stemming from underlying structural vulnerabilities. The banking groups are classified into clusters using unsupervised learning techniques. This research contributes to the empirical literature on the determinants of banks’ performance by highlighting the importance of banks’ heterogeneity, notably controlling for differential performance due to asset quality, solvency and business model. In addition, this paper shows that the magnitude of return on assets’ exposure to the business cycle varies across banks and that inflation in the euro area matters only for a subset of them. Importantly, the study sheds some light on the possible reasons for the mixed results in the literature regarding the role of non‐interest income, the T1 capital ratio and inflation. Finally, no significant effects of the Covid‐19 variables on banks’ return on assets are found during the first seven quarters of the pandemic.

Since the beginning of this century, the global economy has been hit by a series of unforeseen shocks, including the Global Financial Crisis, the euro area's sovereign debt crisis, and most recently, the global inflation surge. To motivate this special issue, we provide a brief overview of recent methods that have been proposed to improve the ability of forecast models to predict shocks and to capture their effects once they have occurred. We also propose a method that may allow central banks to respond more quickly to the kind of inflationary surge that occurred from 2020 to 2022 so that those banks would not have misdiagnosed the surge as a temporary phenomenon.

This paper tests the accuracy and predictability of two term structure models using both yields‐only and factor‐augmented specifications focusing on the recent COVID‐19 crisis. In addition, we test the predictive ability of the yield curve on economic activity for the United States and other advanced countries. We provide evidence that models with an enhanced information set, including COVID‐19 factors, improve interest rate forecasts for this period. Also, we point out that term structure models can determine future variations in economic activity but are time‐ and country‐sensitive. Finally, out‐of‐sample analysis reveals that the use of factor‐augmented term structure models, to reflect the current economic and market conditions, improves their forecasting accuracy.

We conduct an international analysis of the cross-sectional risk premiums of uncertainty risk factors in addition to traditional risk factors. We consider international stock markets in five regions separately. We measure uncertainty by the local and US economic policy uncertainty indices. Economic policy uncertainty risk has negative risk premiums. This implies that investors get lower returns for assets with high uncertainty betas. We further analyze a nonlinear relationship between excess returns and uncertainty risk by adding the downside economic policy uncertainty risk factor which captures high levels of uncertainty, similar to downside market risk. The downside uncertainty risk factor has negative risk premiums.