Forecasting the realized variance of oil-price returns using machine learning : is there a role for U.S. state-level uncertainty?
Oğuzhan Çepni, Rangan Gupta, Daniel Pienaar, Christian Pierdzioch
| Year of publication: |
2022
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|---|---|
| Authors: | Çepni, Oğuzhan ; Gupta, Rangan ; Pienaar, Daniel ; Pierdzioch, Christian |
| Published in: |
Energy economics. - Amsterdam : Elsevier, ISSN 0140-9883, ZDB-ID 795279-X. - Vol. 114.2022, p. 1-14
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| Subject: | Aggregate and regional uncertainties | Forecasting | Machine learning | Oil price | Realized variance | Künstliche Intelligenz | Artificial intelligence | USA | United States | Prognoseverfahren | Forecasting model | Volatilität | Volatility | Ölpreis | Varianzanalyse | Analysis of variance |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.eneco.2022.106229 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://ebvufind01.dmz1.zbw.eu/10013477404