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A noise-robust estimator of volatility based on interquantile ranges
Authors:Jin-Huei Yeh  Jying-Nan Wang  Chung-Ming Kuan
Institution:1. Department of Finance, National Central University, Taoyuan, Taiwan
2. Department of Finance, Minghsin University of Science and Technology, Hsinchu, Taiwan
3. Department of Finance, National Taiwan University, Taipei, Taiwan
Abstract:This paper proposes a new class of estimators based on the interquantile range of intraday returns, referred to as interquantile range based volatility (IQRBV), to estimate the integrated daily volatility. More importantly and intuitively, it is shown that a properly chosen IQRBV is jump-free for its trimming of the intraday extreme two tails that utilize the range between symmetric quantiles. We exploit its approximation optimality by examining a general class of distributions from the Pearson type IV family and recommend using IQRBV.04 as the integrated variance estimate. Both our simulation and the empirical results highlight interesting features of the easy-to-implement and model-free IQRBV over the other competing estimators that are seen in the literature.
Keywords:
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