Forecast evaluation in large cross-sections of realized volatility
Forecast evaluation in large cross-sections of realized volatility
In this paper, we consider the forecast evaluation of realized volatility measures under cross-section dependence using equal predictive accuracy testing procedures. We evaluate the predictive accuracy of the model based on the augmented cross-section when forecasting Realized Volatility. Under the null hypothesis of equal predictive accuracy the benchmark model employed is a standard HAR model while under the alternative of non-equal predictive accuracy the forecast model is an augmented HAR model estimated via the LASSO shrinkage. We study the sensitivity of forecasts to the model specification by incorporating a measurement error correction as well as cross-sectional jump component measures. The out-of-sample forecast evaluation of the models is assessed with numerical implementations.
stat.ML, cs.LG
Katsouris, Christis
c00ef5df-703d-4372-bfd4-a2df0d664f95
9 December 2021
Katsouris, Christis
c00ef5df-703d-4372-bfd4-a2df0d664f95
[Unknown type: UNSPECIFIED]
Abstract
In this paper, we consider the forecast evaluation of realized volatility measures under cross-section dependence using equal predictive accuracy testing procedures. We evaluate the predictive accuracy of the model based on the augmented cross-section when forecasting Realized Volatility. Under the null hypothesis of equal predictive accuracy the benchmark model employed is a standard HAR model while under the alternative of non-equal predictive accuracy the forecast model is an augmented HAR model estimated via the LASSO shrinkage. We study the sensitivity of forecasts to the model specification by incorporating a measurement error correction as well as cross-sectional jump component measures. The out-of-sample forecast evaluation of the models is assessed with numerical implementations.
Text
2112.04887v1
- Author's Original
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Published date: 9 December 2021
Keywords:
stat.ML, cs.LG
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Local EPrints ID: 471734
URI: http://eprints.soton.ac.uk/id/eprint/471734
PURE UUID: 7fcd0b33-ff8c-415b-97a1-5af42464b59f
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Date deposited: 17 Nov 2022 17:38
Last modified: 17 Mar 2024 03:49
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Author:
Christis Katsouris
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