Approximating long-memory processes with low-order autoregressions
Implications for modeling realized volatility
Datos Bibliográficos
| ID | 21542086 |
|---|---|
| Autores | Richard T Baillie (0000-0002-4534-0063, King's College London, autor de correspondencia), Dooyeon Cho (0000-0003-2588-9347, Sungkyunkwan University), Seunghwa Rho (Hanyang University) |
| Año | 2023 |
| Volumen | 64 |
| Número | 6 |
| Páginas | 2911-2937 |
| Fecha de publicación | 2023-06-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Empirical Economics (JOURNAL) |
| Identificadores de la revista | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Editorial | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-022-02357-8 |
| OpenAlex | W4327697256 |
| Idioma | EN |
| Referencias citadas | 45 |
Autoregressive fractionally integrated moving average · Autoregressive model · Econometrics · Economics · Impulse response · Indirect Inference · Inference · Long memory · Statistics · Stochastic volatility · Complex Systems and Time Series Analysis · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics
Long memory relationships and the aggregation of dynamic models
An Introduction to Long‐memory Time Series Models and Fractional Differencing
Modeling and Forecasting Realized Volatility
Long-Term Storage Capacity of Reservoirs
Fractional differencing
Estimating the Dimension of a Model
Testing the null hypothesis of stationarity against the alternative of a unit root
Good Volatility, Bad Volatility
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |