Rate efficient estimation of realized Laplace transform of volatility with microstructure noise

In this paper, we consider the problem of estimating the Laplace transform of volatility within a fixed time interval [0,T] using high‐frequency sampling, where we assume that the discretized observations of the latent process are contaminated by microstructure noise. We use the pre‐averaging approach to deal with the effect of microstructure noise. Under the high‐frequency scenario, we obtain a consistent estimator whose convergence rate is , which is known as the optimal convergence rate of the estimation of integrated volatility functionals under the presence of microstructure noise. The related central limit theorem is established. The simulation studies justify the finite‐sample performance of the proposed estimator.     

Spectral Estimation of Covolatility from Noisy Observations Using Local Weights

We propose localized spectral estimators for the quadratic covariation and the spot covolatility of diffusion processes, which are observed discretely with additive observation noise. The appropriate estimation for time‐varying volatilities is based on an asymptotic equivalence of the underlying statistical model to a white‐noise model with correlation and volatility processes being constant over small time intervals. The asymptotic equivalence of the continuous‐time and discrete‐time experiments is proved by a construction with linear interpolation in one direction and local means for the other. The new estimator outperforms earlier non‐parametric methods in the literature for the considered model. We investigate its finite sample size characteristics in simulations and draw a comparison between various proposed methods.     

An efficient and fast algorithm for estimating the frequencies of superimposed exponential signals in zero-mean multiplicative and additive noise

In this paper, we consider the superimposed exponential signals in zero-mean multiplicative and additive noise when all the noise are independently and identically distributed. We use a three-step iterative procedure to estimate the frequencies of the considered model. It is observed that the estimators are consistent and work quite well in terms of biases and mean square errors. Moreover, the convergence rate of the estimators attains O _p (N ^?3/2), which is the best convergence rate in the case of only additive noise and constant amplitude.     

Threshold Estimation for Stochastic Processes with Small Noise

Consider a process satisfying a stochastic differential equation with unknown drift parameter, and suppose that discrete observations are given. It is known that a simple least squares estimator (LSE) can be consistent but numerically unstable in the sense of large standard deviations under finite samples when the noise process has jumps. We propose a filter to cut large shocks from data and construct the same LSE from data selected by the filter. The proposed estimator can be asymptotically equivalent to the usual LSE, whose asymptotic distribution strongly depends on the noise process. However, in numerical study, it looked asymptotically normal in an example where filter was chosen suitably, and the noise was a Lévy process. We will try to justify this phenomenon mathematically, under certain restricted assumptions.     

Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes

We consider spatial point processes with a pair correlation function, which depends only on the lag vector between a pair of points. Our interest is in statistical models with a special kind of ‘structured’ anisotropy: the pair correlation function is geometric anisotropic if it is elliptical but not spherical. In particular, we study Cox process models with an elliptical pair correlation function, including shot noise Cox processes and log Gaussian Cox processes, and we develop estimation procedures using summary statistics and Bayesian methods. Our methodology is illustrated on real and synthetic datasets of spatial point patterns.     

Pills,Periods, and Postfeminism

Since the English translation of Elsimar Coutinho's Is Menstruation Obsolete? in 1999 and the introduction of cycle-stopping birth control pills in 2003, US women are increasingly advised that menstruation is not necessary for good health. The most widely distributed source of such messages is direct-to-consumer advertisements for birth control pills. The most recent television ads for Seasonique, the leading brand of cycle-stopping contraceptive in the US, minimize the pill's contraceptive function and focus instead on their effectiveness in reducing the frequency of menstruation. A close reading of these commercials reveals them to be an integral part of an emerging postfeminist media culture that attempts to solidify a new, neoliberal subjectivity for women. This subjectivity imposes an idealized, docile, non-menstruating feminine body, ready for full-time participation in the neoliberal economy.     

Assessing direct and indirect seasonal decomposition in state space

The problem of whether seasonal decomposition should be used prior to or after aggregation of time series is quite old. We tackle the problem by using a state-space representation and the variance/covariance structure of a simplified one-component model. The variances of the estimated components in a two-series system are compared for direct and indirect approaches and also to a multivariate method. The covariance structure between the two time series is important for the relative efficiency. Indirect estimation is always best when the series are independent, but when the series or the measurement errors are negatively correlated, direct estimation may be much better in the above sense. Some covariance structures indicate that direct estimation should be used while others indicate that an indirect approach is more efficient. Signal-to-noise ratios and relative variances are used for inference.     

修辞中的抑制机制综述

抑制作为一种认知压抑,是语言理解中的认知过程和机制。文章综述了抑制机制在修辞语理解中的作用,从隐喻、习语、谚语三个方面来诠释修辞语本体的字面意义在理解过程中会受到抑制作用的阻止,其喻体的比喻意义才是听者或读者所应接收到的目标信息。     

Language,Identity and Empowerment

Abstract

This article addresses the relationship between language and identity and language and power. Language is viewed as an important vehicle through which empowerment can occur in society and through which social workers can contribute to the empowerment of dual language individuals and communities. Language suppression is viewed through the framework of bilinguaphobia, a term coined by Faltis and Coulter in 2004. Because of the long and unique history of Spanish in Southwestern communities, the authors suggest that true empowerment must overcome the prevalent discourse of fear of bilingualism. This article suggests that the present status and identity of Latinos in the Southwestern United States would be enhanced and affirmed by the healthy co-existence of two tongues and two cultural frameworks and that social workers should recognize and advocate for the power of bilingual discourses.     

专利动态特征与专利诉讼:基于智能手机产业的研究

"基于中国金融市场某股票和权证订单成交数据构建交易网络，分析交易网络微观结构蕴含的市场信息和交易行为多样性对即时价格冲击的影响.股票和权证交易网络中包含了近100万的交易者，在给定的显著性水平下，进行交易网络降噪，剔除了大部分噪声交易者，构建“核心”交易网络，发现噪声交易者具有更大的即时价格冲击.本文主要贡献是基于复杂网络模体对交易网络中噪声交易者和交易行为多样性进行了向量化表征，定义交易行为多样性指标，发现交易行为多样性越大的交易者具有更敏感的即时价格冲击影响.基于复杂网络微观结构分析方法解构蕴藏于交易网络中的市场交易行为，为研究交易者行为和价格行为提供了新视角."