Cross redundancy and sensitivity in DEA models

Data envelopment analysis (DEA) measures the efficiency of each decision making unit (DMU) by maximizing the ratio of virtual output to virtual input with the constraint that the ratio does not exceed one for each DMU. In the case that one output variable has a linear dependence (conic dependence, to be precise) with the other output variables, it can be hypothesized that the addition or deletion of such an output variable would not change the efficiency estimates. This is also the case for input variables. However, in the case that a certain set of input and output variables is linearly dependent, the effect of such a dependency on DEA is not clear. In this paper, we call such a dependency a cross redundancy and examine the effect of a cross redundancy on DEA. We prove that the addition or deletion of a cross-redundant variable does not affect the efficiency estimates yielded by the CCR or BCC models. Furthermore, we present a sensitivity analysis to examine the effect of an imperfect cross redundancy on DEA by using accounting data obtained from United States exchange-listed companies.     

Monday returns and asset pricing

The seasonal patterns observed on Monday stock returns are still unexplained by different asset pricing models. We attempt to fill this gap in the finance literature by using the Fama-French (Journal of Financial Economics 33:3–56, 1993) risk factors to explain the Monday seasonal. The results in the study show that Monday returns are explained by risk factors such as the market return, the size of the firms, and the book-to-market ratios of firms.     

Sequential control of time series by functionals of kernal-weighted empirical processes under local alternatives

Motivated in part by applications in model selection in statistical genetics and sequential monitoring of financial data, we study an empirical process framework for a class of stopping rules which rely on kernel-weighted averages of past data. We are interested in the asymptotic distribution for time series data and an analysis of the joint influence of the smoothing policy and the alternative defining the deviation from the null model (in-control state). We employ a certain type of local alternative which provides meaningful insights. Our results hold true for short memory processes which satisfy a weak mixing condition. By relying on an empirical process framework we obtain both asymptotic laws for the classical fixed sample design and the sequential monitoring design. As a by-product we establish the asymptotic distribution of the Nadaraya-Watson kernel smoother when the regressors do not get dense as the sample size increases.Acknowledgements The author is grateful to two anonymous referees for their constructive comments, which improved the paper. One referee draws my attention to Lifshits paper. The financial support of the Collaborative Research Centre Reduction of Complexity in Multivariate Data Structures (SFB 475) of the German Research Foundation (DFG) is greatly acknowledged.     

Distribution of run statistics in partially exchangeable processes

Let { X_i} _{i 3 1}{{ X_{i}} _{igeq 1}} be an infinite sequence of recurrent partially exchangeable binary random variables. We study the exact distributions of two run statistics (total number of success runs and the longest success run) in { X_i} _{i 3 1}{{ X_{i}} _{igeq1}} . Since a flexible class of models for binary sequences can be obtained using the concept of partial exchangeability, as a special case of our results one can obtain the distribution of runs in ordinary Markov chains, exchangeable and independent sequences. The results also enable us to study the distribution of runs in particular urn models.     

The impact of government intervention on the stabilization of domestic financial markets and on U.S. banks’ asset composition

The 2007–2009 financial crisis that evolved from various factors including the housing boom, aggressive lending activity, financial innovation, and increased access to money and capital markets prompted unprecedented U.S. government intervention in the financial sector. We examine changes in banks’ balance sheet composition associated with U.S. government intervention during the crisis. We find that the initial round of quantitative easing positively impacts bank liquidity across all bank samples. Our results show a positive impact of repurchase agreement market rates on bank liquidity for small and medium banks. We conclude that banks have become more liquid in the post-crisis period, especially the larger banks (large and money center banks). We show that real estate loan portfolio exposures have reverted to pre-crisis levels for money center banks and remained flat for all other bank samples.     

A model of growth with inherited tastes

This research aims at studying a general equilibrium closed economy with overlapping generations and inherited tastes (aspirations), as in de la Croix (Econ Lett 53(1):89–96, 1996). It shows that the interaction between the intensity of aspirations and the elasticity of substitution of effective consumption affects the qualitative and quantitative long-term dynamics from both local and global perspectives. The related literature is extended by showing that (1) the Neimark–Sacker bifurcation found by de la Croix (1996) does not necessarily give rise to fluctuations and (2) endogenous (long-lasting) fluctuations occur through the emergence of period-doubling bifurcations.     

Open interest,volume, and volatility: evidence from Taiwan futures markets

This paper examines the relationships amongst volatility, total trading volume (TVOL) and total open interest (TOI) for three Taiwan stock index futures markets as well as the role of the latter two variables in the dynamics of GARCH modeling and forecasting. From both ex-post and ex-ante perspectives, we study this issue by using the VAR model and augmented GARCH-type models, respectively. For the GARCH-type models, we employ both symmetric and asymmetric models augmented with lagged logs in TOI and/or TVOL. We find that whether addition of these two variables helps the basic GARCH models predict future volatility depends upon the sample period examined for all three sets of futures. Nonetheless, the best three models for out-of-sample volatility forecasting in the MSE sense are generally the augmented models for all sub-intervals and all three futures contracts.