Monte Carlo experiments on bootstrap DEA

Since the introduction of bootstrap DEA there is a growing literature on applications which use this method, mainly for hypothesis testing. It is therefore important to establish the consistency and evaluate the performance of bootstrap DEA. The few Monte Carlo experiments in the literature perform this exercise on the basis of coverage probabilities, using a certain population assumption and usually they analyze the simple case of 1 input and 1 output. However, it has been argued recently that coverage probabilities are not a good tool of assessment. In our study we evaluate the performance of bootstrap DEA using the standard approach of comparing moments. We use three different data generating processes over three different dimensions while for each case we compare results from both the smooth and naive bootstrap. Our results are not in accordance with previous studies, as we find that the smooth bootstrap performs overall worse while we highlight the cases where the researcher should be cautious when using these techniques

Bootstrap DEA and hypothesis testing

Bootstrapping non-parametric models is a fairly complicated exercise which is associated with implicit assumptions or requirements that are not always obvious to the non-expert user. Bootstrap DEA is a significant development of the past decade; however, some of its assumptions and properties are still quite unclear, which may lead to mistakes in implementation and hypothesis testing. This paper clarifies these issues and proposes a hypothesis testing procedure, along with its limitations, which could be extended to test almost any hypothesis in bootstrap DEA. Moreover, it enhances the intuition behind bootstrap DEA and highlights logical and theoretical pitfalls that should be avoided

Multidirectional conditional convergence in European banking

18 month embargo require

Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries

The paper examines the return and volatility spillovers between crude oil, gold and equities, and investigates the usefulness of the two commodities in hedging equity portfolios. Using daily data from January 20043 to May 2016 for the Gulf Cooperation Council countries, a DCC-GARCH model is used to estimate dynamic correlations and hedge ratios. We find significant spillovers from oil to equities, highlighting the heavy dependence of the local economies on oil. Moreover, the spillovers of gold on the stock markets are insignificant, suggesting that gold price fluctuations do not necessarily influence equity investment decisions. In the opposite direction, we find that equities do not exert significant influence on the two commodities, which we attribute to the relatively small capitalisation of the exchanges. Our results reveal low dynamic correlations and hedge ratios, with a few spikes during crises, indicating that oil and gold are cheap hedges for stocks, albeit not good ones, while they could be considered as weak safe havens, but at a considerable cost

Essays on efficiency and productivity: the Greek banking case.

Bootstrap DEA is a valuable tool for gauging the sensitivity of DEA scores towards sampling variations, hence allowing for statistical inference. However, it is associated with generous assumptions while evidence on its performance is limited. This thesis begins with the evaluation of the performance of bootstrap DEA in small samples through a variety of Monte Carlo simulations. The results indicate cases under which bootstrap DEA may underperform and it shown how the violation of the fundamental assumption of equal bootstrap and DEA biases may affect confidence intervals and cause the evidenced underperformance. An alternative approach, which utilises the Pearson system random number generator, seems to perform well towards this respect. In particular, coverage probabilities converge to the nominal ones for samples as small as 120 observations and the bootstrap biases are very close to the DEA ones. In the presence of technological heterogeneity, though, poor performance is observed in all cases, which is not surprising as even the applicability of simple DEA is questionable. Using an illustrative example from the deregulation of the Greek banking sector during late 80s, potential differences arising from the various approaches are discussed. In particular, the theoretical explorations are extended to the case of the Global Malmquist productivity index, which is used to examine the productivity change of Greek banks during (de)regulation. Some differences are observed on the magnitudes of the estimated quantities of interest and on the probability masses at the tails of the relevant bootstrap distributions. Qualitatively, though, the overall conclusions are very similar; the provision of commercial freedoms enhanced the productivity of commercial banks whereas the imposition of prudential controls had the opposite effect. This result is of topical interest as the European Supervisory Mechanism, which recently assumed duties, will closely supervise “significant institutions” which includes the 4 biggest Greek banks and their banking subsidiaries