Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287988 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 42 [Issue:] 4 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2022 [Pages:] 785-801
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
We examine the predictive value of El Niño and La Niña weather episodes for the subsequent realized variance of 16 agricultural commodity prices. To this end, we use high‐frequency data covering the period from 2009 to 2020 to estimate the realized variance along realized skewness, realized kurtosis, realized jumps, and realized upside and downside tail risks as control variables. Accounting for the impact of the control variables as well as spillover effects from the realized variances of the other agricultural commodities in our sample, we estimate an extended heterogeneous autoregressive (HAR) model by means of random forests to capture in a purely data‐driven way potentially nonlinear links between El Niño and La Niña and the subsequent realized variance. We document such nonlinear links, and that El Niño and La Niña increase forecast accuracy, especially at longer forecast horizons, for several of the agricultural commodities that we study in this research.
Subjects: 
agricultural commodities
El Niño and La Niña
forecasting
random forests
realized variance
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.