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Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models
Author(s) -
Luciani Matteo,
Veredas David
Publication year - 2015
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2325
Subject(s) - stylized fact , univariate , volatility clustering , econometrics , dynamic factor , curse of dimensionality , volatility (finance) , factor analysis , computer science , skewness , economics , autoregressive conditional heteroskedasticity , machine learning , multivariate statistics , macroeconomics
We introduce an approximate dynamic factor model for modeling and forecasting large panels of realized volatilities. Since the model is estimated by means of principal components and low‐dimensional maximum likelihood, it does not suffer from the curse of dimensionality. We apply the model to a panel of 90 daily realized volatilities pertaining to S&P 100 from January 2001 to December 2008. Results show that our model is able to capture the stylized facts of panels of volatilities (comovements, clustering, long memory, dynamic volatility, skewness and heavy tails), and that it performs fairly well in forecasting, in particular in periods of turmoil, in which it outperforms standard univariate benchmarks. Copyright © 2015 John Wiley & Sons, Ltd.