Normalized least-squares estimation in time-varying ARCH models
时变 ARCH 模型中的归一化最小二乘估计AI 解读 ↓
← 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：摘要明确研究金融收益的时变 ARCH 条件波动模型及平方收益自相关，直接涉及波动率与时间序列方法。
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…
时变 ARCH 模型中的归一化最小二乘估计
- 研究问题
- 如何估计时变 ARCH 参数，在局部小样本下进行带宽选择和区间推断，并描述金融收益平方的缓慢自相关衰减？
- 方法与思路
- 作者提出闭式核归一化最小二乘估计，使用基于预测的交叉验证选择带宽，并以残差自助法构造逐点置信区间。
- 材料陈述的贡献
- 摘要称，估计量与核准极大似然具有相同收敛速率且小样本表现更好，自助分布渐近一致，并展示汇率与股指应用。
- 阅读关注点
- 应核对参数时变方式、矩条件及参数空间限制，查看小样本比较设计、自助有效性和预测评价的具体设置。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；条件、数值优势及预测指标未列出，逐点区间不能直接理解为同时置信带。
原文摘要
We investigate the time-varying ARCH (tvARCH) process. It is shown that it can be used to describe the slow decay of the sample autocorrelations of the squared returns often observed in financial time series, which warrants the further study of parameter estimation methods for the model. Since the parameters are changing over time, a successful estimator needs to perform well for small samples. We propose a kernel normalized-least-squares (kernel-NLS) estimator which has a closed form, and thus outperforms the previously proposed kernel quasi-maximum likelihood (kernel-QML) estimator for small samples. The kernel-NLS estimator is simple, works under mild moment assumptions and avoids some of the parameter space restrictions imposed by the kernel-QML estimator. Theoretical evidence shows that the kernel-NLS estimator has the same rate of convergence as the kernel-QML estimator. Due to the kernel-NLS estimator’s ease of computation, computationally intensive procedures can be used. A prediction-based cross-validation method is proposed for selecting the bandwidth of the kernel-NLS estimator. Also, we use a residual-based bootstrap scheme to bootstrap the tvARCH process. The bootstrap sample is used to obtain pointwise confidence intervals for the kernel-NLS estimator. It is shown that distributions of the estimator using the bootstrap and the “true” tvARCH estimator asymptotically coincide. We illustrate our estimation method on a variety of currency exchange and stock index data for which we obtain both good fits to the data and accurate forecasts.