Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?

Fuente: arXiv
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Autore principale: Pollok, Austin
Natura: Preprint
Pubblicazione: 2025
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author Pollok, Austin
author_facet Pollok, Austin
contents The discrepancy between realized volatility and the market's view of volatility has been known to predict individual equity options at the monthly horizon. It is not clear how this predictability depends on a forecast's ability to predict firm-level volatility. We consider this phenomenon at the daily frequency using high-dimensional machine learning models, as well as low-dimensional factor models. We find that marginal improvements to standard forecast error measurements can lead to economically significant gains in portfolio performance. This makes a case for re-imagining the way we train models that are used to construct portfolios.
format Preprint
id arxiv_https___arxiv_org_abs_2506_07928
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?
Pollok, Austin
Statistical Finance
The discrepancy between realized volatility and the market's view of volatility has been known to predict individual equity options at the monthly horizon. It is not clear how this predictability depends on a forecast's ability to predict firm-level volatility. We consider this phenomenon at the daily frequency using high-dimensional machine learning models, as well as low-dimensional factor models. We find that marginal improvements to standard forecast error measurements can lead to economically significant gains in portfolio performance. This makes a case for re-imagining the way we train models that are used to construct portfolios.
title Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?
topic Statistical Finance
url https://arxiv.org/abs/2506.07928