Sparse vector heterogeneous autoregressive modeling for realized volatility
Author:
Funder
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Subject
Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s42952-020-00090-5.pdf
Reference21 articles.
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2. Baek, C., Davis, R. A., & Pipiras, V. (2017). Sparse seasonal and periodic vector autoregressive modeling. Computational Statistics and Data Analysis, 106, 103–126. https://doi.org/10.1016/j.csda.2016.09.005.
3. Baek, C., Davis, R. A., & Pipiras, V. (2018). Periodic dynamic factor models: Estimation approaches and applications. Electronic Journal of Statistics, 12(2), 4377–4411. https://doi.org/10.1214/18-EJS1518.
4. Baek, C., & Pipiras, V. (2014). On distinguishing multiple changes in mean and long-range dependence using local Whittle estimation. Electronic Journal of Statistics, 8, 931–964. https://doi.org/10.1214/14-EJS916.
5. Basu, S., & Michailidis, G. (2015). Regularized estimation in sparse high-dimensional time series models. The Annals of Statistics, 43(4), 1535–1567.
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