Estimation of spot volatility with superposed noisy data
Zhi Liu and
The North American Journal of Economics and Finance, 2018, vol. 44, issue C, 62-79
By using high frequency financial data, we nonparametrically estimate the spot volatility at any given time point, while the simultaneous presence of multiple transactions and market microstructure noise in the observation procedure are considered. Our estimator is based on the summation of the locally ranged increments, while kernel smoothing give us spot volatility. Besides, the microstructure noise can be estimated and removed, if it is modeled as bid-ask spread, which is a frequently used assumption. The consistency and asymptotic normality of the estimator are established. We do some simulation studies to assess the finite sample performance of our estimator. The estimator is also applied to some real data sets, further, the relationship between multiple records and spot volatility is also explored.
Keywords: High frequency financial data; Spot volatility; Range-based estimation; Kernel estimate; Multiple records; Microstructure noise; Central limit theorem (search for similar items in EconPapers)
JEL-codes: C13 C14 G10 G12 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:44:y:2018:i:c:p:62-79
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