Forecasting Realized Volatility of Agricultural Commodities

Stavros Degiannakis; George Filis; Tony Klein; T. Walther

doi:10.1016/j.ijforecast.2019.08.011

Forecasting Realized Volatility of Agricultural Commodities

Stavros Degiannakis
, George Filis
, Tony Klein
, T. Walther

UU LEG Research UUSE Multidisciplinary Economics

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

We forecast the realized and median realized volatility of agricultural commodities using variants of the heterogeneous autoregressive (HAR) model. We obtain tick-by-tick data on five widely-traded agricultural commodities (corn, rough rice, soybeans, sugar, and wheat) from the CME/ICE. Real out-of-sample forecasts are produced for between 1 and 66 days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we demonstrate convincingly that such HAR extensions do not offer any superior predictive ability in their out-of-sample results, since none of these extensions produce significantly better forecasts than the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit from including more complexity, related to the volatility decomposition or relative transformations of the volatility, in the forecasting models.

Original language	English
Pages (from-to)	74-96
Number of pages	23
Journal	International Journal of Forecasting
Volume	38
Issue number	1
DOIs	https://doi.org/10.1016/j.ijforecast.2019.08.011
Publication status	Published - 1 Jan 2022

Bibliographical note

Funding Information:
The authors would like to thank the Guest Editor Tao Hong and three anonymous reviewers for their helpful comments on a previous version of this paper. We are thankful for the comments and support of Matthias Fengler and Karl Frauendorfer. George Filis and Stavros Degiannakis acknowledge the support of Bournemouth University , which provided funding for the purchase of the data under the University’s QR funds. Part of the work has been conducted during Thomas Walther’s research time as Assistant Professor at the University of St. Gallen, Institute for Operations Research and Computational Finance.

Funding Information:
The authors would like to thank the Guest Editor Tao Hong and three anonymous reviewers for their helpful comments on a previous version of this paper. We are thankful for the comments and support of Matthias Fengler and Karl Frauendorfer. George Filis and Stavros Degiannakis acknowledge the support of Bournemouth University, which provided funding for the purchase of the data under the University's QR funds. Part of the work has been conducted during Thomas Walther's research time as Assistant Professor at the University of St. Gallen, Institute for Operations Research and Computational Finance.

Publisher Copyright:
© 2019 International Institute of Forecasters

Keywords

Agricultural commodities
Forecast
Heterogeneous autoregressive model
Median realized volatility
Realized volatility

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10.1016/j.ijforecast.2019.08.011

1-s2.0-S0169207019302353-mainFinal published version, 807 KBLicence: Taverne

Cite this

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title = "Forecasting Realized Volatility of Agricultural Commodities",

abstract = "We forecast the realized and median realized volatility of agricultural commodities using variants of the heterogeneous autoregressive (HAR) model. We obtain tick-by-tick data on five widely-traded agricultural commodities (corn, rough rice, soybeans, sugar, and wheat) from the CME/ICE. Real out-of-sample forecasts are produced for between 1 and 66 days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we demonstrate convincingly that such HAR extensions do not offer any superior predictive ability in their out-of-sample results, since none of these extensions produce significantly better forecasts than the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit from including more complexity, related to the volatility decomposition or relative transformations of the volatility, in the forecasting models.",

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author = "Stavros Degiannakis and George Filis and Tony Klein and T. Walther",

note = "Funding Information: The authors would like to thank the Guest Editor Tao Hong and three anonymous reviewers for their helpful comments on a previous version of this paper. We are thankful for the comments and support of Matthias Fengler and Karl Frauendorfer. George Filis and Stavros Degiannakis acknowledge the support of Bournemouth University , which provided funding for the purchase of the data under the University{\textquoteright}s QR funds. Part of the work has been conducted during Thomas Walther{\textquoteright}s research time as Assistant Professor at the University of St. Gallen, Institute for Operations Research and Computational Finance. Funding Information: The authors would like to thank the Guest Editor Tao Hong and three anonymous reviewers for their helpful comments on a previous version of this paper. We are thankful for the comments and support of Matthias Fengler and Karl Frauendorfer. George Filis and Stavros Degiannakis acknowledge the support of Bournemouth University, which provided funding for the purchase of the data under the University's QR funds. Part of the work has been conducted during Thomas Walther's research time as Assistant Professor at the University of St. Gallen, Institute for Operations Research and Computational Finance. Publisher Copyright: {\textcopyright} 2019 International Institute of Forecasters",

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N2 - We forecast the realized and median realized volatility of agricultural commodities using variants of the heterogeneous autoregressive (HAR) model. We obtain tick-by-tick data on five widely-traded agricultural commodities (corn, rough rice, soybeans, sugar, and wheat) from the CME/ICE. Real out-of-sample forecasts are produced for between 1 and 66 days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we demonstrate convincingly that such HAR extensions do not offer any superior predictive ability in their out-of-sample results, since none of these extensions produce significantly better forecasts than the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit from including more complexity, related to the volatility decomposition or relative transformations of the volatility, in the forecasting models.

AB - We forecast the realized and median realized volatility of agricultural commodities using variants of the heterogeneous autoregressive (HAR) model. We obtain tick-by-tick data on five widely-traded agricultural commodities (corn, rough rice, soybeans, sugar, and wheat) from the CME/ICE. Real out-of-sample forecasts are produced for between 1 and 66 days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we demonstrate convincingly that such HAR extensions do not offer any superior predictive ability in their out-of-sample results, since none of these extensions produce significantly better forecasts than the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit from including more complexity, related to the volatility decomposition or relative transformations of the volatility, in the forecasting models.

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KW - Median realized volatility

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UR - https://www.scopus.com/pages/publications/85083303395

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SP - 74

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JO - International Journal of Forecasting

JF - International Journal of Forecasting

IS - 1

ER -

Forecasting Realized Volatility of Agricultural Commodities

Abstract

Bibliographical note

Keywords

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