Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models

19 Pages Posted: 14 Sep 2011 Last revised: 1 Aug 2013

See all articles by David Veredas

David Veredas

Vlerick Business School

Matteo Luciani

Board of Governors of the Federal Reserve System

Date Written: March 28, 2012

Abstract

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&P100 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 period of turmoil in which it outperforms standard univariate benchmarks.

Keywords: Realized volatilities, vast dimensions, factor models, long memory, forecasting

JEL Classification: C32, C51, G01

Suggested Citation

Veredas, David and Luciani, Matteo, Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models (March 28, 2012). Available at SSRN: https://ssrn.com/abstract=1927338 or http://dx.doi.org/10.2139/ssrn.1927338

David Veredas (Contact Author)

Vlerick Business School ( email )

Library
REEP 1
Gent, BE-9000
Belgium

Matteo Luciani

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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