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DTSTART:20200308T070000
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DTSTART;TZID=America/New_York:20200213T150000
DTEND;TZID=America/New_York:20200213T160000
DTSTAMP:20210613T165511
CREATED:20200210T034753Z
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SUMMARY:Andrew Papanicolaou\, NYU\, Principal component analysis for implied volatility surfaces
DESCRIPTION:Principal component analysis (PCA) is a useful tool when trying to uncover factor models from historical asset returns. For the implied volatilities of U.S. equities there is a PCA-based model with a principal eigenportfolio whose return time series lies close to that of an overarching market factor. Specifically\, this market factor is the index resulting from the daily compounding of a weighted average of implied-volatility returns\, with weights based on the optionsâ€™ open interest (OI). We analyze the singular values derived from the tensor structure of the implied volatilities of S&P500 constituents\, and find evidence indicating that the OI-weighted index is one of at least two significant factors in this market.
URL:https://math.sciences.ncsu.edu/event/andrew-papanicolaou-nyu-principal-component-analysis-for-implied-volatility-surfaces/
LOCATION:SAS 4201
CATEGORIES:Special Seminar
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