Realized Variances vs. Correlations: Unlocking the Gains in Multivariate Volatility Forecasting

Laura Capera Romero and Anne Opschoor
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Laura Capera Romero: Vrije Universiteit Amsterdam and Tinbergen Institute
Anne Opschoor: Vrije Universiteit Amsterdam and Tinbergen Institute

No 24-059/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This paper disentangles the added value of using high-frequency-based (realized) covariance measures on multivariate volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding economic gains using a broad set of portfolio performance metrics. Using state-of-the-art models based on daily returns and realized (co)variances, we predict the conditional covariance matrix on a daily, weekly, biweekly, and monthly frequency, both for dimensions 30 and 50. We evaluate the forecasts statistically using various loss functions and economically by constructing Global Minimum Variance (GMV) portfolios. Using a data set of 50 liquid U.S. stocks from 2001 to 2019, we find that the inclusion of realized variances largely accounts for the improvement in statistical forecast performance (between 65% and at least 78%). The results on the GMV portfolios show that realized covariance models exhibit lower ex-post volatility but tend to produce substantially lower ex-post mean returns compared to models with realized variances and daily returns. Consequently, Sharpe Ratios increase roughly by 35%, leading to significant utility gains, equivalent to up to 500 basis points per year. Combined, our results indicate that there is no economic gain by modeling correlations dynamically, either using daily returns or realized correlations.

Keywords: multivariate volatility; high-frequency data; realized variances; realized correlations (search for similar items in EconPapers)
JEL-codes: C32 C58 G17 (search for similar items in EconPapers)
Date: 2024-11-03
New Economics Papers: this item is included in nep-for and nep-rmg
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