Evidence for hedge fund predictability from a multivariate Student's t full-factor GARCH model

Extending previous work on hedge fund return predictability, this paper introduces the idea of modelling the conditional distribution of hedge fund returns using Student's t full-factor multivariate GARCH models. This class of models takes into account the stylized facts of hedge fund return series, that is, heteroskedasticity, fat tails and deviations from normality. For the proposed class of multivariate predictive regression models, we derive analytic expressions for the score and the Hessian matrix, which can be used within classical and Bayesian inferential procedures to estimate the model parameters, as well as to compare different predictive regression models. We propose a Bayesian approach to model comparison which provides posterior probabilities for various predictive models that can be used for model averaging. Our empirical application indicates that accounting for fat tails and time-varying covariances/correlations provides a more appropriate modelling approach of the underlying dynamics of financial series and improves our ability to predict hedge fund returns.     

THE EFFECTS OF MACROECONOMIC CONDITIONS AT GRADUATION ON OVEREDUCATION

This article shows that unfavorable economic conditions at graduation decrease the likelihood of a good job‐worker match over a worker's subsequent career. Mismatch is quantified in terms of overeducation by both industry and occupation. The German Socio‐Economic Panel and region‐level unemployment rates from 1994 to 2012 are used. Instrumental variables estimates account for endogenous graduation timing. A single percentage point increase in regional unemployment causes an increase in the probability of overeducation of 1.6–1.7 percentage points for university graduates. Effects for technical tertiary education and apprenticeship graduates are smaller. Labor market entry conditions affect workers for up to 9 years after graduation. (JEL J23, J22, E32, I23)     

Asymptotic distribution of regression type estimators of parameters of stable laws

Asymptotic distributions of regression-type estimators for the parameters of stable distributions am obtained. The asymptotic normalized standard deviations of the estimators are computed for various values of the parameters and various choices of the number of points used in getting the regression estimates.     

The densest <Emphasis Type="Italic">k</Emphasis>-subgraph problem on clique graphs

The Densest k-Subgraph (DkS) problem asks for a k-vertex subgraph of a given graph with the maximum number of edges. The problem is strongly NP-hard, as a generalization of the well known Clique problem and we also know that it does not admit a Polynomial Time Approximation Scheme (PTAS). In this paper we focus on special cases of the problem, with respect to the class of the input graph. Especially, towards the elucidation of the open questions concerning the complexity of the problem for interval graphs as well as its approximability for chordal graphs, we consider graphs having special clique graphs. We present a PTAS for stars of cliques and a dynamic programming algorithm for trees of cliques. M.L. is co-financed within Op. Education by the ESF (European Social Fund) and National Resources. V.Z. is partially supported by the Special Research Grants Account of the University of Athens under Grant 70/4/5821.     

A Translation and Validation Study of the Life Orientation Test Revised in the Greek Speaking Population of Nurses among Three Hospitals in Athens and Ioannina

The life orientation test-revised (LOT-R) (Scheier et al. in Journal of Personality and Social Psychology 67:1063–1078, 1994) is a brief measure for assessing dispositional optimism. The aim of this study was to develop a Greek language version of the LOT-R and to assess the instrument’s psychometric properties. The LOT-R was translated and culturally adopted in Greek language, and the final version was administered, along with a questionnaire consisting socioeconomic characteristics and a single item measuring optimism, to 276 Greek speaking, hospital nurses (222 female, 54 male), aged 22–65 years (mean 37.8, SD 8.3). Results showed that the LOT-R has good internal consistency (Cronbach’s α = .71 and item total correlation coefficients from .27 to .73, a unitary structure, and stability over a 3-months period (r = .66). Moreover, the Greek version of the scale exhibited good convergent validity with single-item optimism scale (r = .73). Principal components analysis revealed a two-factor structure representing the constructs of optimism and pessimism. The Greek life orientation test revised appears to be a valid tool in assessing dispositional optimism in Greek speaking people and is expected to facilitate the examination of optimism in Greek speaking populations.     

Extended generalised Pareto models for tail estimation

The most popular approach in extreme value statistics is the modelling of threshold exceedances using the asymptotically motivated generalised Pareto distribution. This approach involves the selection of a high threshold above which the model fits the data well. Sometimes, few observations of a measurement process might be recorded in applications and so selecting a high quantile of the sample as the threshold leads to almost no exceedances. In this paper we propose extensions of the generalised Pareto distribution that incorporate an additional shape parameter while keeping the tail behaviour unaffected. The inclusion of this parameter offers additional structure for the main body of the distribution, improves the stability of the modified scale, tail index and return level estimates to threshold choice and allows a lower threshold to be selected. We illustrate the benefits of the proposed models with a simulation study and two case studies.     

Estimating the parameters of Poisson-exponential models

This paper proposes two methods of estimation for the parameters in a Poisson-exponential model. The proposed methods combine the method of moments with a regression method based on the empirical moment generating function. One of the methods is an adaptation of the mixed-moments procedure of Koutrouvelis & Canavos (1999). The asymptotic distribution of the estimator obtained with this method is derived. Finite-sample comparisons are made with the maximum likelihood estimator and the method of moments. The paper concludes with an exploratory-type analysis of real data based on the empirical moment generating function.     

Bayesian estimation in Kibble's bivariate gamma distribution

The authors describe Bayesian estimation for the parameters of the bivariate gamma distribution due to Kibble (1941). The density of this distribution can be written as a mixture, which allows for a simple data augmentation scheme. The authors propose a Markov chain Monte Carlo algorithm to facilitate estimation. They show that the resulting chain is geometrically ergodic, and thus a regenerative sampling procedure is applicable, which allows for estimation of the standard errors of the ergodic means. They develop Bayesian hypothesis testing procedures to test both the dependence hypothesis of the two variables and the hypothesis of equal means. They also propose a reversible jump Markov chain Monte Carlo algorithm to carry out the model selection problem. Finally, they use sets of real and simulated data to illustrate their methodology.     

On Bayesian lasso variable selection and the specification of the shrinkage parameter

We propose a Bayesian implementation of the lasso regression that accomplishes both shrinkage and variable selection. We focus on the appropriate specification for the shrinkage parameter λ through Bayes factors that evaluate the inclusion of each covariate in the model formulation. We associate this parameter with the values of Pearson and partial correlation at the limits between significance and insignificance as defined by Bayes factors. In this way, a meaningful interpretation of λ is achieved that leads to a simple specification of this parameter. Moreover, we use these values to specify the parameters of a gamma hyperprior for λ. The parameters of the hyperprior are elicited such that appropriate levels of practical significance of the Pearson correlation are achieved and, at the same time, the prior support of λ values that activate the Lindley-Bartlett paradox or lead to over-shrinkage of model coefficients is avoided. The proposed method is illustrated using two simulation studies and a real dataset. For the first simulation study, results for different prior values of λ are presented as well as a detailed robustness analysis concerning the parameters of the hyperprior of λ. In all examples, detailed comparisons with a variety of ordinary and Bayesian lasso methods are presented.     

Revisiting the Effect of Income on Health in Europe: Evidence from the 8th Round of the European Social Survey

Social Indicators Research - This study provides new evidence about the effects of income on population health. To do so, our first research question controls for the absolute income hypothesis:...