Empirical likelihood for high frequency data-Reference-Cited by-同舟云学术

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Empirical likelihood for high frequency data

Published:2019-02-27 Issue:3 Volume:38 Page:621-632
ISSN:0735-0015
Container-title:Journal of Business & Economic Statistics
language:en
Short-container-title:Journal of Business & Economic Statistics

Author:

Camponovo Lorenzo¹,Matsushita Yukitoshi²,Otsu Taisuke³

Affiliation:

1. University of Applied Sciences and Arts of Italian Switzerland, Galleria 2 - 100 Via Cantonale 6928, Manno, CH Email addresses:

2. Graduate School of Economics, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japan. Email addresses:

3. Department of Economics, London School of Economics, Houghton Addr-Line, London, WC2A 2AE, UK. Email addresses:

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Economics and Econometrics,Social Sciences (miscellaneous),Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/07350015.2018.1549051

Reference46 articles.

1. Aït-Sahalia, Y. and J. Jacod (2009a) Testing for jumps in a discretely observed process, Annals of Statistics, 37, 184-222.

2. Aït-Sahalia, Y. and J. Jacod (2009b) Estimating the degree of activity of jumps in high frequency data, Annals of Statistics, 37, 2202-2244.

3. Aït-Sahalia, Y. and J. Jacod (2014) High-Frequency Financial Econometrics, Princeton University Press.

4. Aït-Sahalia, Y., Mykland, P. A. and L. Zhang (2011) Ultra high frequency volatility estimation with dependent microstructure noise, Journal of Econometrics, 160, 160-175.

5. Andersen, T. G., Dobrev, D. and E. Schaumburg (2012) Jump-robust volatility estimation using nearest neighbor truncation, Journal of Econometrics, 169, 75-93.

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3. Computational analysis of the behavior of stochastic volatility models with financial applications;Journal of Computational and Applied Mathematics;2022-09

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