CONDITIONALLY HETEROSKEDASTIC FACTOR MODELS WITH SKEWNESS AND LEVERAGE EFFECTS
包含偏度与杠杆效应的条件异方差因子模型AI 解读 ↓
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包含偏度与杠杆效应的条件异方差因子模型
- 研究问题
- 金融收益的条件异方差、动态偏度和杠杆效应如何同时纳入因子模型？收益期限变化又如何影响因子数量及波动持续性判断？
- 方法与思路
- 作者以条件矩限制设定因子模型，构造无条件矩用于 GMM 推断，讨论时间聚合封闭性，并应用于英国行业指数日超额收益。
- 材料陈述的贡献
- 摘要称同时考虑偏度和杠杆效应带来明显效率收益及更低波动持续性估计；较长期限下所需异方差因子更少，杠杆证据更弱。
- 阅读关注点
- 核对矩限制、因子识别和时间聚合结论的条件，并检查效率比较基准、持续性定义及不同收益期限下的估计结果。
- 阅读边界
- 仅据所给 Crossref 摘要，无法确定封闭性的具体条件、效率增益幅度和实证稳健性；英国行业指数结果不构成普遍优势证明。
原文摘要
SUMMARY Conditional heteroskedasticity, skewness and leverage effects are well‐known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects. This model is specified in terms of conditional moment restrictions and unconditional moment conditions are proposed allowing inference by the generalized method of moments (GMM). The model is also shown to be closed under temporal aggregation. An application to daily excess returns on sectorial indices from the UK stock market provides strong evidence for dynamic conditional skewness and leverage with a sharp efficiency gain resulting from accounting for both effects. The estimated volatilitypersistence from the proposed model is lower than that estimated from models that rule out such effects. I also find that the longer the returns' horizon, the fewer conditionally heteroskedastic factors may be required for suitable modeling and the less strong is the evidence for dynamic leverage. Some of these results are in line with the main findings of Harvey and Siddique ( ) and Jondeau and Rockinger ( ), namely that accounting for conditional skewness impacts the persistence in the conditional variance of the return process. Copyright © 2012 John Wiley & Sons, Ltd.