SIMULTANEOUSLY MODELING CONDITIONAL HETEROSKEDASTICITY AND SCALE CHANGE
同时建模条件异方差与尺度变化AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究金融收益中的 GARCH 条件异方差与平滑尺度变化。
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同时建模条件异方差与尺度变化
- 研究问题
- 如何在金融收益序列中同时刻画条件异方差和随时间变化的尺度，并估计这两部分，以避免仅由标准 GARCH 承担解释？
- 方法与思路
- 作者在标准 GARCH 中引入平滑尺度函数，将尺度函数的核估计与 GARCH 参数的最大似然估计结合，并给出数据驱动算法。
- 材料陈述的贡献
- 摘要称研究了估计量的渐近性质、进行了模拟，并在每日 S&P 500 和 DAX 100 收益中发现显著尺度变化与条件异方差共存。
- 阅读关注点
- 读全文应核对尺度函数与 GARCH 部分的区分条件、平滑选择、估计步骤及渐近结论，并检查应用中的显著性判断。
- 阅读边界
- 仅据所给 RePEc 出版社摘要，一致性速率的公式有缺失，不能补写具体速率；完整假设、模拟数值与实证细节也未说明。
原文摘要
This paper proposes a semiparametric approach by introducing a smooth scale function into the standard generalized autoregressive conditional heteroskedastic (GARCH) model so that conditional heteroskedasticity (CH) and scale change in financial returns can be modeled simultaneously. An estimation procedure combining kernel estimation of the scale function and maximum likelihood estimation of the GARCH parameters is proposed. Asymptotic properties of the estimators are investigated in detail. It is shown that asymptotically normal, -consistent parameter estimation is available. A data-driven algorithm is developed for practical implementation. Finite sample performance of the proposal is studied through simulation. The proposal is applied to model CH and scale change in the daily S&P 500 and DAX 100 returns. It is shown that both series have simultaneously significant scale change and CH.We are very grateful to the co-editor and two referees for their helpful comments and suggestions, which led to a substantial improvement of this paper. The paper was finished under the advice of Professor Jan Beran, Department of Mathematics and Statistics, University of Konstanz, Germany, and was financially supported by the Center of Finance and Econometrics (CoFE), University of Konstanz. We thank colleagues in CoFE, especially Professor Winfried Pohlmeier, for their interesting questions at a talk of the author. It was these questions that motivated the author to write this paper. Our special thanks go to Dr. Erik Lüders, Department of Finance and Insurance, Laval University, and Stern School of Business, New York University, for his helpful suggestions.