3 papers
q-fin.MF2020
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…
q-fin.PM2019
Hierarchical PCA and Applications to Portfolio Management
Marco Avellaneda
It is widely known that the common risk-factors derived from PCA beyond the first eigenportfolio are generally difficult to interpret and thus to use in practical portfolio managem…