Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

In situ preparation of intrinsic flame-retardant urea formaldehyde/carbon nanotubes nanocomposite foam: structure and reinforcing mechanism

ABSTRACT

Urea-formaldehyde (UF)/carboxylated carbon nanotubes (CNTs-COOH) nanocomposite foams were prepared via in-situ polymerization. Chemical bonding and hydrogen bonding interactions formed between CNTs-COOH and UF matrix. UF resin adhered onto the ektexine of CNTs-COOH and grafting ratio of 496.41% was achieved. UF/CNTs-COOH foam showed smaller cell size, narrower cell size distribution and lower water absorption compared with UF foam. Introduction of 0.25 wt% CNTs-COOH resulted in 58.43% increase in compressive strength. CNTs-COOH were pulled out with surface covered with UF resin under stress and the failure mode was the destruction of matrix around interfacial layer. UL-94V-0 rating was achieved for the composite foams.     

组合式精馏塔在甲烷氯化物装置精馏系统中的应用

通过对规整填料塔与板式塔的特点进行分析,运用规整填料的低压降、大通量、高效率等特点,以及板式塔的操作稳定性结合在甲烷氯化物精馏塔的改造,通过流程模拟结果及创新性的采用填料与塔板复合的型式,结合填料塔与浮阀塔各自优点形成组合塔,优化了精馏塔运行。     

Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.     

基于AHP的高校基建项目管理后评价研究

针对高校基建项目的特点，界定了高校基建项目管理后评价的内涵。运用层次分析法，构建了完整的项目管理后评价指标体系。以某高校综合实验楼为例，在全面收集项目相关资料和对相关专家广泛咨询的基础上，通过对各评价指标的单排序和总排序计算分析及其一致性检验，得到各影响因素的量化综合评价结果。在此基础上，对项目管理全过程进行后评价，以反馈形式为同类公益性基建项目管理提供参考。     

Iterative equalization combining algorithm in intersymbol interference channel

To realize joint optimization of spatial diversity and equalization combining in the intersymbol interference (ISI) channel, an iterative equalization combining algorithm is proposed. The proposed algorithm uses the coefficients of Turbo equalization to calculate the combination weights without estimating the signal to noise ratio in each diversity branch. The equalized symbols from different diversity branches are combined, and the extrinsic information output from the decoder is fed back to the equalizers, so as to exchange soft information between the equalizers and the decoder. The performance of the proposed algorithm is analyzed using the extrinsic information transfer (EXIT) chart and verified by simulations. Results show that our approach fully exploits time domain information from the multipath channel and spatial domain information from multi receiving antennas, which efficiently improve the performance of the receiver in the severe ISI channel.     

Low complexity quantum private queries protocol

Private information retrieval(PIR) is an important privacy protection issue of secure multi-party computation, but the PIR protocols based on classical cryptography are vulnerable because of new technologies,such as quantum computing and cloud computing. The quantum private queries(QPQ) protocols available, however, has a high complexity and is inefficient in the face of large database. This paper, based on the QKD technology which is mature now, proposes a novel QPQ protocol utilizing the key dilution and auxiliary parameter. Only N quits are required to be sent in the quantum channel to generate the raw key, then the straight k bits in the raw key are added bitwise to dilute the raw key, and a final key is consequently obtained to encrypt the database. By flexible adjusting of auxiliary parameters θ and k, privacy is secured and the query success ratio is improved. Feasibility and performance analyses indicate that the protocol has a high success ratio in first-trial query and is easy to implement, and that the communication complexity of O(N) is achieved.     

Comparison between voxel and consistent meso-scale models of woven composites

The applicability of voxel meshes to model the mechanical behavior of woven composites at the mesoscopic scale is studied and compared to consistent Finite Element (FE) meshes. The methods are illustrated by mechanically modeling a Representative Unit Cell (RUC) of a composite made of four layers of glass fiber plain weave fabric embedded in an epoxy matrix. Mesh convergence is studied to determine the minimum element size necessary to obtain a correct yarn volume fraction. The comparison between both methods is based on (i) homogenized macroscopic elastic properties, (ii) local stress fields, and (iii) first damage prediction. Even if a good agreement is obtained for the elastic properties, the stress concentrations due to the steplike shape of voxels induce significant differences between both methods in terms of first damage prediction.     

Homogenization with uncertainty in Poisson ratio for polymers with rubber particles

The main aim of this work is dual computer analysis of probabilistic coefficients for the homogenized tensor of the polymer filled with the rubber particles having randomized Poisson ratios of both constituents. The major issue is to verify an influence of a randomness in rubber Poisson ratio close to the compressibility limit on the uncertainty of the effective tensor probabilistic characteristics. Probabilistic analysis presented here is carried out using mainly the stochastic perturbation technique provided by the common application of the traditional FEM commercial code ABAQUS and the symbolic computations package MAPLE. This FEM-based technique employs polynomial response function of the optimum order recovered from the weighted least squares method and following a set of deterministic solutions obtained for various values of the randomized input parameter. Optimization procedure is released entirely into a symbolic environment, where maximization of the correlation factor together with minimization of the fitting variance and approximation error are applied. Homogenization technique consists in equating of deformation energies for the real composite and the artificial one characterized by the effective elasticity tensor with uncertainty.