Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

An extended likelihood framework for modelling discretely observed credit rating transitions

The estimation of the parameters of a continuous-time Markov chain from discrete-time observations, also known as the embedding problem for Markov chains, plays in particular an important role for the modeling of credit rating transitions. This missing data problem boils down to a latent variable setting and thus, maximum likelihood estimation is usually conducted using the expectation-maximization (EM) algorithm. We illustrate that the EM algorithm is likely to get stuck in local maxima of the likelihood function in this specific problem setting and adapt a stochastic approximation simulated annealing scheme (SASEM) as well as a genetic algorithm (GA) to combat this issue. Above that, our main contribution is to extend our method GA by a rejection sampling scheme, which allows one to derive stochastic monotone maximum likelihood estimates in order to obtain proper (non-crossing) multi-year probabilities of default. We advocate the use of this procedure as direct constrained optimization (of the likelihood function) will not be numerically stable due to the large number of side conditions. Furthermore, the monotonicity constraint enables one to combine structural knowledge of the ordinality of credit ratings with real-life data into a statistical estimator, which has a stabilizing effect on far off-diagonal generator matrix elements. We illustrate our methods by Standard and Poor’s credit rating data as well as a simulation study and benchmark our novel procedure against an already existing smoothing algorithm.     

Limitations of portfolio diversification through fat tails of the return Distributions: Some empirical evidence

This study investigates the level of risk due to fat tails of the return distribution and the changes of tail fatness (TF) through portfolio diversification. TF is not eliminated through portfolio diversification, and, interestingly, the positive tail has declining fatness until a certain level is reached, while the negative tail has rising fatness. This indicates that fat tails are highly relevant to common factors on systematic risk and that the relevance of common factors is higher for the negative tail compared to the positive tail. In the portfolio diversification effect, the declining fatness of the positive tail further reduces risk, but the rising fatness of the negative tail does not contribute to this effect. The asymmetry between the fatness of the positive and negative tails in the return distribution corresponds to the asymmetry of the trade-off relationship between loss avoidance and profit sacrifice that is expected as a consequence of portfolio diversification. Investors use portfolio diversification to reduce their risk of suffering high losses, but following this strategy means sacrificing high-profit potential. Our study provides empirical confirmation for the practical limitation of portfolio diversification and explains why investors with diversified portfolios suffer high losses from market crashes. An examination of the Northeast Asian stock markets of China, Japan, Korea, and Taiwan show identical results.     

空间目标的联合矩阵束极点估计

极点作为谐振区雷达目标的最主要电磁特性，具有很大的空间目标识别前景。但是在使用频域观测数据提取目标极点时，存在虚假极点和估计精度问题。针对上述问题，首先通过加窗和截断改进了后时响应信号的获取流程，抑制了虚假极点影响；然后采用最小描述长度法（MDL）估计极点个数，避免了定阶错误引起的虚假极点；最后采用联合矩阵束法估计极点，有效减少了单角度数据估计极点时存在的遗漏极点和虚假极点问题。FEKO软件验证表明，所提算法具有更高的精度和抗噪能力。     

Investor attention,firm-specific characteristic,and momentum: A case of the Korean stock market

This study examines the sources of negative momentum profits by combining investor attention and the properties of common and firm-specific factors. We choose the Korean stock market as a good case to characterize the negative momentum profits identified in Asia. In both portfolio and stock analyses, a method is devised to generate return data involving the property of each common and firm-specific factor within stock groups by investor attention. This study found significant negative momentum profits within the stock group with high investor attention. This momentum effect is highly dependent on the reversed performance of the past loser portfolio, not the continued performance of the past winner portfolio, and this reversal is strongly attributable to the properties of firm-specific factors, and not those of common factors. These results are robustly consistent regardless of changes in empirical design and the consideration of influence factors, market dynamics, and other stock markets.     

基于风险矩阵的企业采购风险评估

采购是企业经营的一个核心环节,是获取利润的重要来源。文章以企业采购中存在的风险为研究对象,以采购风险评价的一般分析为基础。将风险矩阵引人到企业采购的风险评估中,对企业采购进行风险评估,为企业采购提供一种结构性风险评估方法,并以某电力建设企业采购为例对风险矩阵的应用进行了说明。     

Random matrix application to correlations amongst the volatility of assets

In this paper, we apply tools from random matrix theory (RMT) to estimates of correlations across the volatility of various assets in the S&P 500. The volatility inputs are estimated by modelling price fluctuations as a GARCH(1,1) process. The corresponding volatility correlation matrix is then constructed. It is found that the distribution of a significant number of eigenvalues of the volatility correlation matrix matches with the analytical result from RMT. Furthermore, the empirical estimates of short- and long-range correlations amongst eigenvalues, which are within RMT bounds, match with the analytical results for the Gaussian Orthogonal ensemble of RMT. To understand the information content of the largest eigenvectors, we estimate the contribution of the Global Industry Classification Standard industry groups to each eigenvector. In comparison with eigenvectors of correlation matrix for price fluctuations, only few of the largest eigenvectors of the volatility correlation matrix are dominated by a single industry group. We also study correlations between ‘volatility returns’ and log-volatility to find similar results.     

多重目标决策在企业管理中的应用

本文通过对多重目标综合决策的方法改进,对几家企业的经营管理进行评估.结果表明,这种方法能使评判结果更客观地、合理地、正确地反映事实的本来面目.     

基于椭圆球面波函数的数字带通滤波器设计

为设计频谱性能优良的有限冲激响应(FIR)数字带通滤波器,从窗函数的性质及选择指标出发,分析了椭圆球面波函数(PSWF)作为窗函数的优势;在此基础上根据数字滤波器设计的原理和要求,选择0阶基带椭圆球面波函数作为窗函数设计数字带通滤波器,并利用微分方程状态转移矩阵逼近的PSWF求解算法,给出了基于PSWF的FIR数字带通滤波器设计方法。理论分析和仿真结果表明：PSWF数字带通滤波器具有较低的设计复杂度,与Kaiser滤波器和Blackman滤波器相比,其旁瓣衰减有超过7 dB的优势,且具有与两种滤波器相当的通带波纹波动和过渡带宽。     

基于干扰矩阵的高密WLAN场景下集中式功率控制

针对高密无线局域网（WLAN）场景中系统干扰严重导致吞吐量降低的问题,提出了一种集中式动态发射功率控制算法（CDTPC）。接入控制器（AC）依据接入点（AP）定时上报的信道扫描报告和邻居报告建立干扰矩阵,通过分析干扰矩阵确定干扰源AP集和覆盖漏洞AP集,并对干扰源AP和覆盖漏洞AP进行动态功率控制,在保障覆盖的情况下减小WLAN系统内干扰、提高网络整体吞吐量。仿真表明：在高密WLAN场景中,使用CDTPC算法进行功率控制前后各AP吞吐对量均有所提高,系统整体吞吐量提高了41.5%.