Estimating and forecasting generalized fractional Long memory stochastic volatility models

In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.

Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation guarantees the positivedefiniteness of the dynamic covariance matrix. The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988, 39(1-2), 69–104), especially for developing tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the likelihood function of RMESV-ALM, and the finite sample properties of the quasi-maximum likelihood estimator of the parameters are analysed. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions from returns to volatility and co-volatility.

Descriptive analysis of the test of everyday attention for children in a Spanish normative sample

Introduction. Test of Everyday Attention for Children (TEA-Ch) has been validated in different countries demonstrating that it is an instrument with a correct balance between reliability and duration. Given the shortage of trustworthy instruments of evaluation in our language for infantile population we decide to explore the Spanish version of the TEA-Ch. Methods. We administered TEA-Ch (version A) to a sample control of 133 Spanish children from 6 to 11 years enrolled in school in the Community of Madrid. Four children were selected at random by course of Primary Education, distributing the sex of equivalent form. Descriptive analysis and comparison by ages and sex in each of the TEA-Ch's subtests were conducted to establish a profile of the sample. In order to analyze the effect of the age, subjects were grouped in six sub-samples: 6, 7, 8, 9, 10 and 11 years-old. Results. This first descriptive analysis demonstrates age exerted a significant effect on each measure, due to an important "jump" in children's performance between 6 and 7 years-old. The effect of sex was significant only in two visual attention measures (Sky Search & Map) and interaction age and sex exerted a significant effect only in the dual task (Score DT). Conclusions. The results suggest that the Spanish version of the TEA-Ch (A) might be a useful instrument to evaluate attentional processes in Spanish child population.

Sample-to-sample fluctuations of the overlap distributions in the three-dimensional Edwards-Anderson spin glass

We study the sample-to-sample fluctuations of the overlap probability densities from large-scale equilibrium simulations of the three-dimensional Edwards-Anderson spin glass below the critical temperature. Ultrametricity, stochastic stability, and overlap equivalence impose constraints on the moments of the overlap probability densities that can be tested against numerical data. We found small deviations from the Ghirlanda Guerra predictions, which get smaller as system size increases. We also focus on the shape of the overlap distribution, comparing the numerical data to a mean-field-like prediction in which finite-size effects are taken into account by substituting delta functions with broad peaks.