Occupation density estimation for noisy high-frequency data
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Publication:2116333
DOI10.1016/j.jeconom.2020.05.013OpenAlexW3048963732MaRDI QIDQ2116333
Tim Bollerslev, Jia Li, Congshan Zhang
Publication date: 16 March 2022
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jeconom.2020.05.013
Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)
Cites Work
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- Volatility occupation times
- Testing for jumps in noisy high frequency data
- Asymptotic equivalence for inference on the volatility from noisy observations
- Discretization of processes.
- Quasi-maximum likelihood estimation of volatility with high frequency data
- Limit theorems for moving averages of discretized processes plus noise
- Occupation densities
- Estimating the integrated volatility with tick observations
- Spot volatility estimation using delta sequences
- Microstructure noise in the continuous case: the pre-averaging approach
- Estimating spot volatility with high-frequency financial data
- Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach
- Long memory in continuous-time stochastic volatility models
- Robust Estimation and Inference for Jumps in Noisy High Frequency Data: A Local-to-Continuity Theory for the Pre-Averaging Method
- ESTIMATING THE VOLATILITY OCCUPATION TIME VIA REGULARIZED LAPLACE INVERSION
- On the Moments of the Modulus of Continuity of Itô Processes
- Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
- Statistical Properties of Microstructure Noise
- Continuous Record Asymptotics for Rolling Sample Variance Estimators
- The Distribution of Realized Exchange Rate Volatility
- NONPARAMETRIC FILTERING OF THE REALIZED SPOT VOLATILITY: A KERNEL-BASED APPROACH
- Markov Processes, Gaussian Processes, and Local Times
- A Tale of Two Time Scales
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