Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data

DOI10.1016/j.jeconom.2017.09.006zbMath1386.62037OpenAlexW2774632094MaRDI QIDQ1706445

Donggyu Kim, Xin-Bing Kong, Cui-Xia Li, Yazhen Wang

Publication date: 22 March 2018

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2017.09.006

zbMATH Keywords

diffusionregularizationsparsityintegrated volatilitypre-averaging realized volatilityadaptive thresholding

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to actuarial sciences and financial mathematics (62P05) Markov processes: estimation; hidden Markov models (62M05)

Related Items (9)

Optimal sparse eigenspace and low-rank density matrix estimation for quantum systems ⋮ Robust covariance estimation with noisy high-frequency financial data ⋮ Time series models for realized covariance matrices based on the matrix-F distribution ⋮ Adaptive robust large volatility matrix estimation based on high-frequency financial data ⋮ Large volatility matrix analysis using global and national factor models ⋮ Efficient and positive semidefinite pre-averaging realized covariance estimator ⋮ Large volatility matrix estimation with factor-based diffusion model for high-frequency financial data ⋮ Testing against constant factor loading matrix with large panel high-frequency data ⋮ A rank test for the number of factors with high-frequency data

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