A multivariate HAR-RV model with heteroscedastic errors and its WLS estimation
- Authors
- Hwang, Eunju; Hong, Won-Tak
- Issue Date
- Jun-2021
- Publisher
- ELSEVIER SCIENCE SA
- Keywords
- Asymptotic normality; GARCH(1,1) model; Multivariate HAR-RV model; Weighted least squares estimation
- Citation
- ECONOMICS LETTERS, v.203
- Journal Title
- ECONOMICS LETTERS
- Volume
- 203
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81561
- DOI
- 10.1016/j.econlet.2021.109855
- ISSN
- 0165-1765
- Abstract
- This work considers a multivariate heterogeneous autoregressive-realized volatility (HAR-RV) model in the presence of heteroscedasticity and aims to analyze realized volatilities of multiple assets that possess non-standard features, such as non-Gaussianity, time varying volatility and long-memory dependence. For capturing the long-memory, a HAR-RV model is employed, while for a heavy-tailed distribution, a GARCH process is adopted on the noise term. To estimate coefficients of the HAR-RV-GARCH model, we suggest weighted least squares estimator (WLSE) based on an observed weighting scheme and prove its asymptotic normality. Simulation results show a good performance on the WLSE. The multivariate HAR-RV-GARCH model fitted by the WLSE is illustrated with an application to realized volatilities of multiple financial data. © 2021
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