Assessment of regional productive performance of European health systems under a metatechnology framework

Within last seventy years, healthcare spending in Europe has grown faster than national income. However, this does not always translate into good health indicators, suggesting a problem of efficiency in different European health systems. This paper analyzes the efficiency of such systems for 185 European regions in 17 countries by grouping them into three clusters according to their institutional setting: regulation, funding and service provision. We investigate their productive performance by adopting a metafrontier framework for exploring the role of technological spillovers. Our findings suggest that the three European health systems show similar efficiency performance; the best performers are regions that have adopted social healthcare insurance; kernel analysis indicates that there is convergence toward a single club in each frontier. Finally, we find a dramatic change in the convergence process after the nancial crisis, with European regions converging toward different groups with different levels of efficiency.     

Average skewness in global equity markets

This paper examines the predictive power of average skewness, defined as the average of monthly skewness values across stocks, documented by the prior literature for US market returns in an international setting. First, we confirm the validity of the results in the original study and show that the intertemporal relation between average skewness and aggregate returns becomes weaker in an alternative sample period. Second, when we repeat the analysis in 22 developed non-US markets, we find that average skewness has no robust predictive power for future market returns. The loss of forecasting power in the international sample does not depend on the method used to calculate average skewness or the regression specification and is supported by additional out-of-sample tests and subsample analysis.     

European business cycles and stock return predictability

This paper finds that the European leading economic indicator, a prime business cycle indicator for the European economies published by the OECD, can strongly predict European stock returns and generate utility gains. Importantly, the predictive power of the European indicator is above and beyond that contained in the country-specific leading indicator. Furthermore, we find that the predictive power of the European indicator is stable.     

Directional predictability from central bank digital currency to cryptocurrencies and stablecoins

This paper investigates the interaction and the directional predictability between the central bank digital currencies (CBDCs) and the major cryptocurrencies and stablecoins during the period between 17 May, 2019–31 December, 2021. To this aim, we employ the "Cross-Quantilogram” model, to examine how and whether the traditional digital currencies react to the CBDC uncertainty and attention shocks. Our findings suggest that CBDC uncertainty index is negatively related to cryptocurrency and stablecoin returns. Furthermore, the CBDC attention index is negatively associated with Bitcoin, Ethereum, XPR and Terra USD, however, it is positively related to Tether, Binance, USD Coin and Dai. Our results are useful for regulators, investors and policy makers, to understand and assess the potential effect of CBDC adoption news on the volatility of the stablecoins and traditional cryptos.     

Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study

In finance, the use of newspaper-based uncertainty measures has grown exponentially in recent years. For instance, a growing number of researchers have used the newspaper-based U.S. economic policy uncertainty (EPU) index suggested in Baker et al. (2016) as a predictor in their model to forecast the variable of interest out-of-sample. Likewise, inspired by the approach suggested in Baker et al. (2016), several other newspaper-based uncertainty measures have been introduced, such as indices measuring geopolitical risk (GPR) and monetary policy uncertainty (MPU). This study evaluates the relative out-of-sample predictive power afforded by more than fifty different newspaper-based uncertainty measures with regards to predicting excess returns on the S&P 500 index one-month ahead using data from 1985$m$1 through 2020$m$12. Our predictive model accounts for salient data features, namely, predictor endogeneity and persistence. Furthermore, we evaluate the evidence of conditional as well unconditional predictive ability as outlined in Giacomini and White (2006), and also explore whether any identified level of gains from a statistical viewpoint lead to gains from an economic viewpoint. We find that newspaper-based uncertainty measures linked with certain components of the equity market volatility (EMV) tracker suggested in Baker et al. (2019) help improve the accuracy of one month ahead point predictions relative to the benchmark the most. In contrast, EPU, GPR and MPU indices, which are more frequently used by researchers are much less successful.