Standard equilibrium models are unable to replicate the average return on equity and the risk-free rate during 1889-1978, the well-known asset returns puzzle. The present paper, motivated by the excess of outliers in the data, proposes a normal-scale mixture stochastic process for output that is compatible with leptokurtosis. Using formal likelihood-based methods, it is shown that observed asset returns are compatible with posterior distributions implied by the model.

This paper analyzes the productivity and efficiency of Turkish banks from 2002 to 2010. We obtained estimates of efficiency, productivity growth and efficiency growth using a Bayesian stochastic frontier approach and focused on accounting for Non-Performing Loans (NPLs) for use in our model. Specifically, we introduce NPLs as a bad output in an input distance function, and estimate a system of non-linear equations subject to endogeneity. We confirm that the productivity growth of Turkish banks was positive over the period of this study, which was mainly due to the improvement in technology, while efficiency growth continued to be negative over the same period. Methodologically, we also prove that not accounting for NPLs in estimating the frontier model might seriously distort the efficiency and productivity results. The study also provides measures of shadow prices for NPL and discusses the results in terms of several interesting trends in Turkish banking. Finally, the paper provides efficiency and productivity comparisons between domestic and foreign banks.

The aim of this study is to provide a methodology for the joint estimation of efficiency and market power of individual banks. The proposed method utilizes the separate implications of the new empirical industrial organization and the stochastic frontier literatures and suggests identification using the local maximum likelihood (LML) technique. Through LML, estimation of market power of individual banks becomes feasible, while a number of restrictive theoretical and empirical assumptions are relaxed. The empirical analysis is carried out on the basis of EMU bank data. Market power estimates indicate fairly competitive conduct in general; however, heterogeneity in market power estimates is substantial across banks. The latter result suggests that the practice of some banks deviates from the average fairly competitive behavior, a finding that has important policy implications. Finally, efficiency and market power present a negative relationship, which is in line with the so-called "quiet life hypothesis". © 2009 Elsevier B.V. All rights reserved.

This paper examines the performance of European banks during the pre-crisis and post-crisis periods, both in terms of technical and allocative efficiencies. We use an innovative Bayesian dynamic frontier model that: (1) distinguishes between short-run and long-run performance; and (2) provides impulse response functions to examine the dynamic effect of shocks in technical and allocative inefficiencies. Based on a rich sample of European banks, we show that while there was a drop in efficiency for most countries following the crisis, the long-run results suggest improvement both in terms of technical and allocative efficiencies. The impulse response functions also show that in the case of shocks in the system, banks seem to revert back to these long-run allocative efficiency scores. We discuss the results in terms of the current financial crisis and provide interesting implications for the European banking industry. We also discuss the determinants of technical and allocative efficiencies. (We would like to thank Professor Allen N. Berger and Professor Andy Mullineux for their valuable comments on the early version of this paper.)

The modeling process of bubbles, using advanced mathematical and econometric techniques, is a young field of research. In this context, significant model misspecification could result from ignoring potential non-linearities. More precisely, the present paper attempts to detect and date non-linear bubble episodes. To do so, we use Neural Networks to capture the neglected non-linearities. Also, we provide a recursive dating procedure for bubble episodes. When using data on stock price-dividend ratio S&P500 (1871.1–2014.6), employing Bayesian techniques, the proposed approach identifies more episodes than other bubble tests in the literature, while the common episodes are, in general, found to have a longer duration, which is evidence of an early warning mechanism (EWM) that could have important policy implications.

By examining the impact of capital regulation on bank risk-taking using a local estimation technique, this paper attempts to quantify for the first time the heterogeneous response of banks towards this type of regulation in banking sectors of western-type economies. Subsequently, using this information, we examine the sources of heterogeneity. The findings suggest that the impact of capital regulation on bank risk is very heterogeneous across banks and the sources of this heterogeneity can be traced into both bank and industry characteristics, as well as into macroeconomic conditions. An important implication of the findings is that common capital regulatory umbrellas are not sufficient to promote financial stability, especially if they are not accompanied by supervisory effectiveness. On the basis of our findings, we contend that more focus should be placed on the actions needed to restrain excessive risk-taking of banks.

This paper reconsiders the formal estimation of bank risk using the variability of the profit function. In our model, point estimates of the variability of profits are derived from a model where this variability is endogenous to other bank characteristics, such as capital and liquidity. We estimate the new model on the entire panel of US banks, spanning the period 1985q1–2012q4. The findings show that bank risk was fairly stable up to 2001 and accelerated quickly thereafter up to 2007. We also establish that the risk of the relatively large banks and banks that failed in the subprime crisis is higher than the industry’s average. Thus, we provide a new leading indicator, which is able to forecast future solvency problems of banks.