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中国科学技术大学学报 ›› 2020, Vol. 50 ›› Issue (12): 1478-1487.DOI: 10.3969/j.issn.0253-2778.2020.12.005

• 科研论文 • 上一篇    下一篇

基于已实现波动率的Lp分位数回归

  

  1. 中国科学技术大学管理学院统计与金融系,安徽合肥230026
  • 出版日期:2020-12-30 发布日期:2021-03-04

Lp  quantile regression with realized measure

  1. Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
  • Online:2020-12-30 Published:2021-03-04
  • Contact: Chen Yu received her Ph. D. degree in probability and statistics from University of Science and Technology of China in 2006. She is an associate professor of Department of Statistics and Finance, School of Management, University of Science and Technology of China. Her research interests include network risk analysis, extreme value theory, and high-frequency data analysis.
  • About author:Tang Li received his master degree from University of Science and Technology of China in 2020. His research interests focus on risk management.
  • Supported by:

    The work is supported by the National Key Research and Development Plan (2016YFC0800100), the NNSF of China (71771203).

PDF (PC)

146

摘要：

提出了一种基于已实现波动率的Lp分位数回归模型,这是一种新的金融风险模型.基于已实现波动率的Lp分位数回归模型将已实现波动率与Lp分位数回归结合起来,并且将Lp分位数加入模型的度量等式中.该模型是 囊括基于已实现波动率的分位数回归模型和基于已实现波动率的Expectile回归模型的更为一般的模型.通过非 对称幂指数分布(AExpPow)导出模型的对数似然函数,并且通过模拟证实了所提出的对数似然函数的正确性.最 后通过实证研究证实基于已实现波动率的Lp分位数回归模型的有效性,得出如下结论:不同的幂指数p适用于不同的数据,不同的时间频率适用于不同的已实现波动率,而不是时间频率越高越好.

关键词: 已实现波动率, 基于已实现波动率的Lp分位数回归, 非对称幂指数分布

Abstract:

A new financial risk model named Lp quantile regression with a realized measure (realized Lp quantile) was proposed. The realized measure and Lp quantiles were combined and Lp quantile were added to the measurement equation. The realized Lp quantile model is a generic model that includes realized quantile model and expectile model. An asymmetric exponential power distribution (AExpPow) was proposed to derive the formula of log-likelihood. And a simulation was conducted to justify the validity of the log-likelihood. Finally an empirical study was conducted to justify the validity of the realized Lp quantile. And some conclusions were drawn as follows: differfent power indices suit different data and different time-frequencies suit different realized measures, and higher frequency is not always better.

Key words: realized measure, realizedLpquantile regression, asymmetric exponential power distribution

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