This study evaluates the efficiency of peripheral European domestic banks and examines the effects of bankrisk determinants on their performance over 2007-2014. Data Envelopment Analysis is utilized on a Malmquist Productivity Index in order to calculate the bank efficiency scores. Next, a Double Bootstrapped Truncated Regression is applied to obtain bias-corrected scores and examine whether changes in the financial conditions affect differently banks' efficiency levels. The analysis accounts for the sovereign debt crisis period and for different levels of financial development in the countries under study. Such an application in the respective European banking setting is unique. The proposed method also copes with common misspecification problems observed in regression models based on efficiency scores. The results have important policy implications for the Euro area, as they indicate the existence of a periphery efficiency meta-frontier. Liquidity and credit risk are found to negatively affect banks productivity, whereas capital and profit risk have a positive impact on their performance. The crisis period is found to augment these effects, while bank-risk variables affect more banks' efficiency when lower levels of financial development are observed.

Using a large panel of mainly unquoted euro-area firms over the period 2003-2011, this paper examines the impact of financial pressure on firms' employment. The analysis finds evidence that financial pressure negatively affects firms' employment decisions. This effect is stronger during the euro area-crisis (2010-2011), especially for firms in the periphery compared to their counterparts in non-periphery European economies. When we introduce firm-level heterogeneity, we show that financial pressure appears to be both statistically and quantitatively more important for bank-dependent, small and privately held firms operating in periphery economies during the crisis. JEL Classification numbers: G32; D22; E22; E44 . *We are grateful to Francesco Zanetti (Editor) and three anonymous referees for useful comments and suggestions. We also thank Nikos Giannakopoulos, Sotirios Kokas and participants at the Financial Engineering and Banking

This paper examines the cash holdings behavior of listed and unlisted …rms. We argue that unlisted …rms, which are smaller, face a greater wedge between the cost of external and internal …nance and as a result they need to rely more on the later. Relying on internal funds means that …rms have a precautionary motive to hold cash. We test our theory using an unbalanced panel of mainly small medium enterprises within the euro area over the period 2003-2017 paying special attention to the role of …nancial pressure, …nancial constraints and the recent …nancial crisis. Our …ndings reveal that unlisted …rms hold more cash than their listed counterparts due to precautionary motives. In addition, when considering the e¤ect of …nancial pressure, the results show that the di¤erence in cash holdings between listed and unlisted …rms exhibit a "Ushaped" relationship. Finally, unlisted …rms have a higher sensitivity to save cash out of cash ‡ow than listed …rms. Our results are robust to using di¤erent speci…cations and di¤erent …nancial pressure measures.

This study is investigating the predictability of the five Fama–French factors and explores their optimal portfolio allocation for factor investing during 2000–2017. Firstly, we forecast each factor with a pool of linear and nonlinear models. Next, the individual forecasts are combined through dynamic model averaging, and their performance is benchmarked by the best performing individual predictor and other forecast combination techniques. Finally, we use the generalized autoregressive score model and the skewed t copula method to estimate the correlation of assets. The generalized autoregressive score performance is also compared with other traditional approaches such as dynamic conditional correlation model and asymmetric dynamic conditional correlation. The performance of the constructed portfolios is assessed through traditional metrics and ratios accounting for the conditional value‐at‐risk and the conditional diversification benefits approach. Our results show that combining Bayesian forecast combinations with copulas is leading to significant improvements in the portfolio optimization process, and forecasting covariance accounting for asymmetric dependence between the factors adds diversification benefits to the obtained portfolios.

This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic Support Vector Regression (SVR) hybrid structures. Genetic, krill herd and sine-cosine algorithms are applied to the parameterization process of the SVR and Locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a Random Walk (RW), an Autoregressive Moving Average (ARMA), the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000-2017. The results show that the sine-cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.