Forecast Evaluation in Large Cross-Sections of Realized Volatility
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.
Cite
@article{arxiv.2112.04887,
title = {Forecast Evaluation in Large Cross-Sections of Realized Volatility},
author = {Christis Katsouris},
journal= {arXiv preprint arXiv:2112.04887},
year = {2021}
}