跳到正文
← 返回论文列表与筛选
阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要直接研究随机波动率模型,并应用于股票指数收益序列。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Jean-Marie Dufour
Pascale Valéry

随机波动率模型中可能非正则假设的精确与渐近检验

研究问题
一因子或二因子随机波动率模型存在奇异矩条件、未识别参数时,如何检验同方差、因子数量及其他参数限制?
方法与思路
作者基于简单矩估计构造多类统计量,比较渐近临界值、局部 Monte Carlo 与最大化 Monte Carlo 检验。
材料陈述的贡献
摘要称其提出有限样本水平可控及非正则下有效的检验;模拟中最大化 Monte Carlo 在识别困难时仍能控制水平。
阅读关注点
应核对精确水平控制所需的模拟机制、干扰参数最大化方式,以及不同非正则情形下各统计量的功效表现。
阅读边界
仅据所给出版社摘要;有限样本控制的完整条件和模拟数值未说明,较好功效的陈述不能推广到所有波动率模型。

原文摘要

We study the problem of testing hypotheses on the parameters of one- and two-factor stochastic volatility models (SV), allowing for the possible presence of non-regularities such as singular moment conditions and unidentified parameters, which can lead to non-standard asymptotic distributions. We focus on the development of simulation-based exact procedures-whose level can be controlled in finite samples-as well as on large-sample procedures which remain valid under non-regular conditions. We consider Wald-type, score-type and likelihood-ratio-type tests based on a simple moment estimator, which can be easily simulated. We also propose a C([alpha])-type test which is very easy to implement and exhibits relatively good size and power properties. Besides usual linear restrictions on the SV model coefficients, the problems studied include testing homoskedasticity against a SV alternative (which involves singular moment conditions under the null hypothesis) and testing the null hypothesis of one factor driving the dynamics of the volatility process against two factors (which raises identification difficulties). Three ways of implementing the tests based on alternative statistics are compared: asymptotic critical values (when available), a local Monte Carlo (or parametric bootstrap) test procedure, and a maximized Monte Carlo (MMC) procedure. The size and power properties of the proposed tests are examined in a simulation experiment. The results indicate that the C([alpha])-based tests (built upon the simple moment estimator available in closed form) have good size and power properties for regular hypotheses, while Monte Carlo tests are much more reliable than those based on asymptotic critical values. Further, in cases where the parametric bootstrap appears to fail (for example, in the presence of identification problems), the MMC procedure easily controls the level of the tests. Moreover, MMC-based tests exhibit relatively good power performance despite the conservative feature of the procedure. Finally, we present an application to a time series of returns on the Standard and Poor's Composite Price Index.

笔记与分组