对于金融资产漂移函数估计的双域组合方法(英文)

Time-domain state-domain methods are common approaches in modern financial analysis.Economic conditions vary time,drift function depends on time and price level for a given state variable.In this paper,to consistently estimate the bivariate drift function,our purpose a new dynamic integrated estimator by combing time-and state-domain methods for estimating drift function.And we establish its asymptotic properties and illustrates it outperforms some old ones by simulations.     

含噪音高频数据动态整合估计波动率的方法

时间域和状态域方法是两种常见的非参数估计方法.前者主要使用的是最近的历史数据,而后者则主要依赖于过去的历史信息.本文在时间域上,通过对含噪音高频数据采用双时间尺度方法获得其波动率,进而获得经动态整合后的波动率.     

基于贝叶斯方法的低违约组合违约率估计

现代信用风险建模的核心是估计违约率,违约率估计是否准确将直接影响信用风险建模的质量。在估计违约率的众多文献中,频率法或logistic回归等统计方法的运用非常广泛,此类统计模型的基础是大样本,它客观上需要最低数量或最优数量的违约数据,而低违约组合(LDP)是指只有很少违约数据甚至没有违约数据的组合,如何估计LDP的违约率、反映违约率的非预期波动是一个值得关注的现实问题。本文针对银行贷款LDP缺乏足够历史违约数据的情况,采用贝叶斯方法估计LDP的违约率,并进一步探讨了根据专家判断或者根据同类银行LDP违约数量的历史数据来确定先验分布的方法。在贝叶斯估计中,通过先验分布的设定,不仅可以实现违约率估计的科学性和合理性,而且可以反映违约的非预期波动,有助于银行实施谨慎稳健的风险管理。     

基于真实波动率和高斯估计的随机波动率模型研究

在随机波动率模型中,由于波动率是不可观测,因此相应的参数估计和统计推断比较困难.将应用真实波动率近似估计积分波动率,进一步基于高斯估计方法给出非线性扩散模型的线性估计,而后再给出随机波动率模型精确的极大似然估计方法.最后,采用上证综合指数和深证成份指数对一系列随机波动率模型进行实证的研究.实证结果表明,均方根模型(Heston模型)较好地描述上证综合指数动态行为,而对于深证成份指数的描述在统计意义上没有显著地解释力.     

基于模糊GJR-GARCH模型的波动率估计

基于模糊数学和模糊时间序列分析理论,在模糊GARCH与GJR-GARCH模型的基础上建立模糊GJR-GARCH模型,并用遗传算法估计了该模型的参数。实证发现沪深两市的收益波动率具有明显的非对称性,相对于普通的GARCH、GJR-GARCH和模糊GARCH模型,模糊GJR-GARCH模型能更好的处理收益率的波动群聚性、时变性和非对称性,具有更好的估计精度。     

基于改进ICA模型的高维波动率估计

本文对IC-GARCH模型进行改进,放宽了ICA关于独立成分是IID的假设,考虑独立成分为ARMA模型的平稳过程,提出了基于自相关结构的IC-GARCH估计方法,并给出了估计量的理论性质和计算效率较高的迭代估计算法。最后,对改进方法进行了模拟和实证分析,结果表明本文提出的模型能够使多元GARCH模型应用于高维金融数据,并大大提高了金融资产收益波动率的估计精度。     

NSD误差下线性模型LAD估计的线性表示

考虑了NSD误差下的线性模型并建立回归参数LAD估计的线性表示.这些结果将独立误差的情形推广和改进到NSD误差的情形.作为一个应用,获得了LAD估计量的收敛率.     

基于实际波动率的组合选择实证研究

本文对证券组合三因素的7种预测方法进行了实证研究和敏感性检验,得出结论:若以周作为组合持有期,则不论何种收益预测方法,基于实际波率的ARFIMA方法在组合持有期上均取得了正的超额收益;基于实际波动率的ARFIMA法在组合选择的各种方法中是最优的.     

扩散模型中扩散系数的非参数组合估计

为了提高扩散系数估计的准确度, 我们利用动态组合时间域与状态域信息提出一个新的组合估计量. 我们发现所提组合估计量能有效估计扩散模型的扩散系数, 正如在本文中模拟所示. 在一定的条件下, 建立了估计量的渐进正态性, 并证明了时间域估计量与状态域估计量是渐进独立的. 大量的模拟展示了所提组合估计量优于单域估计量, 也优于本文所提估计量.     

半参数两样本密度比率模型估计问题的一点注记

In this paper,a semiparametric two-sample density ratio model is considered and the empirical likelihood method is applied to obtain the parameters estimation.A commonly occurring problem in computing is that the empirical likelihood function may be a concaveconvex function.Here a simple Lagrange saddle point algorithm is presented for computing the saddle point of the empirical likelihood function when the Lagrange multiplier has no explicit solution.So we can obtain the maximum empirical likelihood estimation (MELE) of parameters.Monte Carlo simulations are presented to illustrate the Lagrange saddle point algorithm.     

积分波动率二阶变差估计量分析:鞍点算法

本文利用鞍点逼近方法对Black-Scholes模型的积分波动率的二阶变差估计量的估计误差进行分析,得到了相对于中心极限定理更为精细的结果,并且给出了逼近的鞍点算法。结果表明鞍点逼近是中心极限定理的纠正。模拟结果表明鞍点算法给出的估计误差分布相对于正态逼近更合理。该结果在对积分波动率进行统计假设检验时是有意义的。     

基于广义回归估计的校准加权设计效应模型研究

校准是最常用的加权调整方法,然而传统加权调整设计效应模型只考虑有差异权数导致的精度损失,忽略使用辅助信息后的精度改进,因此应用于设计效应计算时存在一定的缺陷。本文在Spencer模型的基础上进行拓展,引入反映辅助变量和调查变量相关关系的广义回归估计量,构建了校准加权设计效应的一般模型。数值分析结果显示,校准加权设计效应模型的效果优于传统加权调整设计效应模型;尤其在调查变量与辅助变量高度相关的情形下,校准加权设计效应模型能够准确地估计出不等概率抽样设计和校准调整的综合效率。     

A Bootstrap-based Method to Achieve Optimality in Estimating the Extreme-value Index

Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptive method to choose the number of order statistics involved in an optimal way, balancing variance and bias components. Recently this has been achieved for the similar but some what less involved case of regularly varying tails (Drees and Kaufmann, 1997); Danielsson et al., 1996). The present paper follows the line of proof of the last mentioned paper.     

Statistical Analysis of Financial Volatility by Wavelet Shrinkage

We analyze the Nikkei daily stock index and verify how wavelets can help in identifying, estimating and predicting its volatility features. While we study the conditional mean and variance dynamics, by utilizing statistical parametric inference techniques, we also decompose the observed signal with a data de-noising procedure. We thus investigate how wavelets discriminate among information at different resolution levels and we attempt to understand whether the de-noised data lead to a better identification of the underlying volatility process. We find that the wavelet data pre-processing strategy, by reducing the measurement error of the observed data, is useful for improving the volatility prediction power.     

A Comparison of Restricted and Unrestricted Estimators in Estimating Linear Functions of Ordered Scale Parameters of Two Gamma Distributions

The problem of estimating linear functions of ordered scale parameters of two Gamma distributions is considered. A necessary and sufficient condition on the ratio of two coefficients is given for the maximum likelihood estimator (MLE) to dominate the crude unbiased estimator (UE) in terms of mean square error. A modified MLE which satisfies the restriction is also suggested, and a necessary and sufficient condition is also given for it to dominate the admissible estimator based solely on one sample. The estimation of linear functions of variances in two sample problem and also of variance components in a one-way random effect model is mentioned.     

A Semiparametric Model of Estimating Volatility of Diffusion Processes

This article provides a semiparametric model to estimate the diffusion coefficient of a stochastic differential equation from discretely observed data without assuming any functional form of the diffusion coefficient. It is shown that the model has the consistency such that estimated states of the diffusion coefficient converge to the true ones as the number of observations (N) goes to infinity and the sampling time interval (Δt) goes to zero while NΔt going to infinity.     

回归分析中参数估计的一种新方法

本文根据矩阵的广义逆理论,建立了回归分析中进行参数估计的一种新方法,该方法更简单更精确。     

A Subsampling Method for the Computation of Multivariate Estimators with High Breakdown Point

Abstract

All known robust location and scale estimators with high breakdown point for multivariate samples are very expensive to compute. In practice, this computation has to be carried out using an approximate subsampling procedure. In this article we describe an alternative subsampling scheme, applicable to both the Stahel-Donoho estimator and the minimum volume ellipsoid estimator, with the property that the number of subsamples required can be substantially reduced with respect to the standard subsampling procedures used in both cases. We also discuss some bias and variability properties of the estimator obtained from the proposed subsampling process.     

非负矩阵最大特征值的估计法

通过构造一个新的矩阵,从而得到一个非负矩阵最大特征值的估计法,该方法将适用范围推广到一般非负矩阵,并通过实例验证了这种新方法精确度更高.