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Quantifying both historic and future volatility is key in portfolio riskmanagement. This note presents and compares estimation strategies forvolatility estimation in an estimation universe consisting on 28 629 uniquecompanies from February 2010 to April 2021, with 858 different portfolios. Theestimation methods are compared in terms of how they rank the volatility of thedifferent subsets of portfolios. The overall best performing approach estimatesvolatility from direct entity returns using a GARCH model for varianceestimation.

Performance evaluation of volatility estimation methods for Exabel