A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp-optimization

The conditional tail expectation (CTE) is an indicator of tail behavior that takes into account both the frequency and magnitude of a tail event. However, the asymptotic normality of its empirical estimator requires that the underlying distribution possess a finite variance; this can be a strong restriction in actuarial and financial applications. A valuable alternative is the median shortfall (MS), although it only gives information about the frequency of a tail event. We construct a class of tail L^p-medians encompassing the MS and CTE. For p in (1,2), a tail L^p-median depends on both the frequency and magnitude of tail events, and its empirical estimator is, within the range of the data, asymptotically normal under a condition weaker than a finite variance. We extrapolate this estimator and another technique to extreme levels using the heavy-tailed framework. The estimators are showcased on a simulation study and on real fire insurance data.     

Kernel Estimation of Rate Function for Recurrent Event Data

Abstract.  Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth will produce a curve with nicks occurring at the censoring times, whereas there is no such problem with the least squares method. Furthermore, the asymptotic variance of the least squares estimator is shown to be smaller under regularity conditions. However, in the implementation of the bootstrap procedures, the moment method is computationally more efficient than the least squares method because the former approach uses condensed bootstrap data. The performance of the proposed procedures is studied through Monte Carlo simulations and an epidemiological example on intravenous drug users.     

或有事项对企业财务能力影响的分析--兼谈会计中的不确定性

阐述了或有事项引起的财务报表变动 ,建立了一套财务指标 ,分析了或有事项对企业财务能力的影响 ,并指出在不确定性会计处理中应注意的几个问题     

蒙古族大德咱雅班智达事迹考述

清初,在祖国统一的大业中,各民族的爱国志士都曾为此做出了贡献,蒙古族高僧大德咱雅班智达.南喀嘉措便是在西藏地方归顺清朝中央,实现祖国统一过程中做出过重要贡献的人物之一。鉴于其在历史发展过程中的重要贡献,当时西藏上层以他为首世,建立了“咱雅班智达“转世系统,从此,它成为藏传佛教在蒙古族地区颇具影响的活佛世系之一。     

社会认知经典研究述评

社会认知是指个人对他人或自己的心理与行为的感知与判断的过程。从 1 93 0年代开始 ,社会心理学家就已经对此问题开展研究。 1 947年 ,J.S .Bruner在以往研究基础上 ,提出了社会知觉概念 ,并且进行了相关实验 ,开启了现当代社会认知研究的大门。 1 970年代后 ,社会认知研究开始广泛借鉴认知心理学的理论和方法 ,尤其受到信息加工理论的深刻影响 ,开展了大量的实证研究。文章在把握社会认知研究的历史脉络基础上 ,评述几个在社会认知研究发展史中占有重要地位的经典研究。     

A bayesian estimator for the dependence function of a bivariate extreme‐value distribution

Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme‐value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set.     

What to do with “I Don't Know:” Elicitation in Ethnographic & Survey Interviews

When a researcher enters an interview, she has already construed it as being a standard type of communicative event. This article considers how a researcher's construal of a communicative event as either an ethnographic or survey interview shapes the production of information. Interview standards entail epistemological assumptions that directly inform the type of information sought and produced. I consider this process through a comparison of the elicitation techniques I employed in survey and ethnographic interviews conducted during research in Mexico. I draw on theory in linguistic anthropology on the nature of meaning in language, examining how dialogicality and interaction are essential to understanding the construal of communicative events.     

Comparative probability for conditional events: A new look through coherence

We study, from the standpoint of coherence, comparative probabilities on an arbitrary familyE of conditional events. Given a binary relation ·, coherence conditions on · are related to de Finetti's coherent betting system: we consider their connections to the usual properties of comparative probability and to the possibility of numerical representations of ·. In this context, the numerical reference frame is that of de Finetti's coherent subjective conditional probability, which is not introduced (as in Kolmogoroff's approach) through a ratio between probability measures.Another relevant feature of our approach is that the family & need not have any particular algebraic structure, so that the ordering can be initially given for a few conditional events of interest and then possibly extended by a step-by-step procedure, preserving coherence.