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Estimating and forecasting large panels of volatilities with approximate dynamic factor models

(2015) JOURNAL OF FORECASTING. 34(3). p.163-176
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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&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 (c) 2015John Wiley & Sons, Ltd.
Keywords
realized volatilities, vast dimensions, factor models, long memory, forecasting, LONG MEMORY, NUMBER, INFERENCE

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MLA
Luciani, Matteo, and David Veredas. “Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models.” JOURNAL OF FORECASTING, vol. 34, no. 3, 2015, pp. 163–76.
APA
Luciani, M., & Veredas, D. (2015). Estimating and forecasting large panels of volatilities with approximate dynamic factor models. JOURNAL OF FORECASTING, 34(3), 163–176.
Chicago author-date
Luciani, Matteo, and David Veredas. 2015. “Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models.” JOURNAL OF FORECASTING 34 (3): 163–76.
Chicago author-date (all authors)
Luciani, Matteo, and David Veredas. 2015. “Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models.” JOURNAL OF FORECASTING 34 (3): 163–176.
Vancouver
1.
Luciani M, Veredas D. Estimating and forecasting large panels of volatilities with approximate dynamic factor models. JOURNAL OF FORECASTING. 2015;34(3):163–76.
IEEE
[1]
M. Luciani and D. Veredas, “Estimating and forecasting large panels of volatilities with approximate dynamic factor models,” JOURNAL OF FORECASTING, vol. 34, no. 3, pp. 163–176, 2015.
@article{8649379,
  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&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 (c) 2015John Wiley & Sons, Ltd.},
  author       = {Luciani, Matteo and Veredas, David},
  issn         = {0277-6693},
  journal      = {JOURNAL OF FORECASTING},
  keywords     = {realized volatilities,vast dimensions,factor models,long memory,forecasting,LONG MEMORY,NUMBER,INFERENCE},
  language     = {eng},
  number       = {3},
  pages        = {163--176},
  title        = {Estimating and forecasting large panels of volatilities with approximate dynamic factor models},
  url          = {http://dx.doi.org/10.1002/for.2325},
  volume       = {34},
  year         = {2015},
}

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