Viscosities of the Gay-Berne nematic liquid crystal

We present molecular dynamics simulation measurements of the viscosities of the Gay-Berne phenomenological model of liquid crystals in the nematic and isotropic phases. The temperature dependence of the rotational and shear viscosities, including the nonmonotonic behavior of one shear viscosity are in good agreement with experimental data. The bulk viscosities are significantly larger than the shear viscosities, again in agreement with experiment.Comment: 11 pages, 4 Postscript figures, Revte

Introducing innovative technologies in higher education: An experience in using geographic information systems for the teaching‐learning process

In today's world, new technologies are being used for the teaching‐learning process in the classroom. Their use to support learning can provide significant advantages for the teaching‐learning process and have potential benefits for students, as many of these technologies are a part of the work life of many current professions. The aim of this study is to analyse the use of innovative technologies for engineering and science education after examining the data obtained from students in their learning process and experiences. The study has been focused on computational geographic information systems, which allow access to and management of large volumes of information and data, and on the assessment of this tool as a basis for a suitable methodology to enhance the teaching‐learning process, taking into account the great social impact of big data. The results allow identifying the main advantages, opportunities, and drawbacks of using these technological tools for educational purposes. Finally, a set of initiatives has been proposed to complement the teaching activity and to improve user experience in the educational field.This study was supported by the Spanish Research Agency and the European Regional Development Fund under project CloudDriver4Industry TIN2017‐89266‐R

An Evolutionary Optimization Approach to Risk Parity Portfolio Selection

In this paper we present an evolutionary optimization approach to solve the risk parity portfolio selection problem. While there exist convex optimization approaches to solve this problem when long-only portfolios are considered, the optimization problem becomes non-trivial in the long-short case. To solve this problem, we propose a genetic algorithm as well as a local search heuristic. This algorithmic framework is able to compute solutions successfully. Numerical results using real-world data substantiate the practicability of the approach presented in this paper

The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown

Strategies used as spectroscopy of financial markets reveal new stylized facts

We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This enormous dataset allows us to compare (i) a closed national market (A-shares) with an international market (B-shares), (ii) individuals and institutions and (iii) real investors to random strategies with respect to timing that share otherwise all other characteristics. We find that more trading results in smaller net return due to trading frictions. We unveiled quantitative power laws with non-trivial exponents, that quantify the deterioration of performance with frequency and with holding period of the strategies used by investors. Random strategies are found to perform much better than real ones, both for winners and losers. Surprising large arbitrage opportunities exist, especially when using zero-intelligence strategies. This is a diagnostic of possible inefficiencies of these financial markets.Comment: 13 pages including 5 figures and 1 tabl

Markowitz versus Michaud: Portfolio Optimization Strategies Reconsidered

Several attempts have been made to reduce the impact of estimation errors on the optimal portfolio composition. On the one hand, improved estimators of the necessary moments have been developed and on the other hand, heuristic methods have been generated to enhance the portfolio performance, for instance the resampled efficiency of Michaud (1998). We compare the out-ofsample performance of traditional Mean-Variance optimization by Markowitz (1952) with Michaud's resampled efficiency in a comprehensive simulation study for a large number of relevant estimators appearing in the literature. In this context we consider different estimation periods as well as unconstrained and constrained portfolio optimization problems. The main finding of our simu-lation study concerning the optimization approach is that Markowitz outperforms Michaud on average. Furthermore, the estimation strategy of Frost/Savarino (1988) proves to work excellent in all analyzed situations