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Rebuild Normality in Chinese Financial Markets with Range-Based Bipower Variance

机译:基于范围的双权方差重建中国金融市场的常态

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Realized range and bipower variance are two important improvements for increasingly popular high-frequency realized estimators in financial markets. This paper verifies a new type of estimator named realized range-based bipower variance and carries out its empirical research with high frequency data from Shanghai Composite Index (SHCI) and Shenzhen Synthesis Index (SZSI). The results show that 1) this estimator combines merits of bothrealized range and realized bipower variance. It is as efficient as realized range estimator and at the same timeremains a consistent estimation of integrated variance of Chinese financial markets’ fluctuation; 2) After standardized by realized range based bipower variance, the distributions of SHCI and SZSI’s daily returns are neither skew nor with high kurtos is anymore. The fact tail and high peak of daily returns are basically eliminated by this high-frequency variance estimator, and the standardized distributions of these returns are nearly normal. 3) Comparative studies show that among four types of realized volatility estimators, the range based bipower variance is the best one to rebuild normality in Chinese financial markets. These findings mean when measuring volatility or fluctuations of financial assets, the usage of this new estimator will increase the performance of many financial practices like pricing or risk management. One feasible way to extend this paper is to consider co-estimators of related assets and detect their impacts to the dynamics of volatilities.
机译:对于金融市场中越来越流行的高频实现估计器,实现范围和双幂方差是两个重要的改进。本文验证了一种新型的估计器,称为已实现的基于范围的双幂方差,并利用来自上海综合指数(SHCI)和深圳综合指数(SZSI)的高频数据进行了实证研究。结果表明:1)该估计器结合了实现范围和实现的双功效方差的优点。它与已实现的范围估算器一样有效,同时仍对中国金融市场波动的整体方差保持一致的估算。 2)通过基于实现的基于范围的双功效方差进行标准化后,SHCI和SZSI的日收益分布既不偏斜也不具有高kurtos。该高频方差估算器基本上消除了日收益率的尾部和高峰,并且这些收益率的标准化分布几乎是正态的。 3)比较研究表明,在四种类型的已实现波动率估计量中,基于范围的双方差是重建中国金融市场正态性的最佳方法。这些发现意味着,在测量金融资产的波动性或波动性时,这种新的估算器的使用将提高许多金融业务的绩效,例如定价或风险管理。扩展本文的一种可行方法是考虑相关资产的共同估计量,并检测它们对波动率动态的影响。

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