金融工程中资产收益的连续时间模型评述

总结了在过去30年中金融资产收益连续时间模型的发展及主要成果,讨论了迄今连续时间模型参数估计的主要方法,其中特别讨论了MCMC方法;最后指出了现在和未来该领域研究所面临的主要课题。     

基于MCMC稳态模拟的贝叶斯经验费率厘定信用模型

B黨lmann-Straub model is one of the most famous applications of the Bayesian method for the experience rate making.However,by the traditional B黨lmann-Straub model one cannot get the unbiased posterior estimation of the parameters when there is not sufficient prior information for the structural parameters;What's more,the difficult of computing high dimension numeration limits the application of Bayesian method.This paper introduces the Markov chain Monte Carlo simulaton method based on the Gibbs sampling after analyzing the structure of the B黨lmann-Straub model and sets up the Bayesian credibility model for estimating the predictive risk premium.Also by using the results of the numeration analysis,this paper proves that from this model one can get the posterior distributions of the parameters dynamically and the posterior estimation of the censoring parameters in the situation that exists unknown parameters,as well as improve the precision of the numeration,which can be helpful to find the heterogeneity of the premium.     

现代信用风险度量模型的实证比较与适用性分析

本文通过实证比较分析发现,现代信用风险度量模型对银行贷款的违约率、贷款损失和损失率的预测结果的差异性较大;但信用监测模型和信用风险附加法所预测的经济资本配置比例不仅符合巴塞尔协议对银行贷款经济资本的要求,也略大于实际应该配置的比例,实证表明了它们对度量我国商业银行贷款组合的信用风险具有较好的适用性.此外,本文也充分验证了借款人信用等级的不同,银行贷款经济资本配置的比例会有显著性的差异.     

Toward Improved Communication about Wildland Fire: Mental Models Research to Identify Information Needs for Natural Resource Management

The management of natural- and human-induced wildland fires is an intricate process that must balance two considerations: that of fire as a necessary natural disturbance and that of the risks that fire poses. Reconciling tradeoffs between these risks and benefits is contingent upon informed, directed, and two-way communication between wildland fire managers and stakeholders. In an effort to aid with the design of such a communication effort, this study used mental models research to determine the unique wildland fire information and decision-making needs of stakeholders living at the wildland-urban interface of a fire-prone area. While the analysis revealed many similarities in how stakeholders conceptualized the risks and benefits of wildland fire, many misconceptions and important gaps in understanding on the part of both expert and nonexpert respondents were identified.     

An algorithm for maximum likelihood estimation in incomplete block variance component models

In the recovery of interblock information to improve the treatment differences estimates in incomplete block designs, the parameter p is usually unknown. Many authors have worked on the problem of estimating it and of studying its properties together with the properties of the treatment differences estimates. In this paper a numerically efficient algorithm is developed which yields the maximum likelihood estimates (MLE) of all the parameters in the mixed incomplete block design model (treatment effects, ρ and variance)     

An Extended Single‐index Model with Missing Response at Random

An extended single‐index model is considered when responses are missing at random. A three‐step estimation procedure is developed to define an estimator for the single‐index parameter vector by a joint estimating equation. The proposed estimator is shown to be asymptotically normal. An algorithm for computing this estimator is proposed. This algorithm only involves one‐dimensional nonparametric smoothers, thereby avoiding the data sparsity problem caused by high model dimensionality. Some simulation studies are conducted to investigate the finite sample performances of the proposed estimators.     

Estimation of nonseparable models with censored dependent variables and endogenous regressors

In this article we develop a nonparametric estimator for the local average response of a censored dependent variable to endogenous regressors in a nonseparable model where the unobservable error term is not restricted to be scalar and where the nonseparable function need not be monotone in the unobservables. We formalize the identification argument put forward in Altonji, Ichimura, and Otsu (2012 Altonji, J. G., Ichimura, H., Otsu, T. (2012). Estimating derivatives in nonseparable models with limited dependent variables. Econometrica 80:1701–1719.[Crossref], [Web of Science ®] , [Google Scholar]), construct a nonparametric estimator, characterize its asymptotic property, and conduct a Monte Carlo investigation to study its small sample properties. Identification is constructive and is achieved through a control function approach. We show that the estimator is consistent and asymptotically normally distributed. The Monte Carlo results are encouraging.     

A goodness of fit test for the multilevel proportional odds model

It is essential to test the goodness of fit of the model before making inferences based on it. Multilevel modeling of ordinal categorical responses is not as developed as for continuous responses. Assessing model adequacy in terms of the goodness of fit with ordinal categorical responses is still being developed and no satisfactory tests are available so far. As a consequence of that, this study concentrates on developing such a goodness of fit test for Multilevel Proportional Odds models and to study the properties of the test.     

Computation of an efficient and robust estimator in a semiparametric mixture model

In this article, we propose an efficient and robust estimation for the semiparametric mixture model that is a mixture of unknown location-shifted symmetric distributions. Our estimation is derived by minimizing the profile Hellinger distance (MPHD) between the model and a nonparametric density estimate. We propose a simple and efficient algorithm to find the proposed MPHD estimation. Monte Carlo simulation study is conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods. Based on our empirical studies, the newly proposed procedure works very competitively compared to the existing methods for normal component cases and much better for non-normal component cases. More importantly, the proposed procedure is robust when the data are contaminated with outlying observations. A real data application is also provided to illustrate the proposed estimation procedure.     

Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.