# Forecast Evaluation in Large Cross-Sections of Realized Volatility

arXiv: https://arxiv.org/abs/2112.04887 · HTML: https://mlsanity.com/paper/2112.04887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 69 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 1 of 1)

- [Power Enhancement in High Dimensional Cross-Sectional Tests](https://mlsanity.com/paper/1310.3899/) (2013)

## Built on by (most cited first, 1 of 1)

- [High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods](https://mlsanity.com/paper/2308.16192/) (2023)

## Abstract (arXiv)

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.