56 citations · 58 across the 4 of their papers we have counts for
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q-fin.MF2020★ 1 cited
Hierarchical PCA and Modeling Asset Correlations
Marco Avellaneda, Juan Andrés Serur
Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierar…
q-fin.ST2020
PCA for Implied Volatility Surfaces
Marco Avellaneda, Brian Healy, Andrew Papanicolaou +1
Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of U.S. equities there is a P…