Dynamic Modelling and Forecasting of Realized Covariance Matrices

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Chiriac_opus-105743.pdf
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2007
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Voev, Valeri
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Zusammenfassung

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The model is based on a multivariate, fractionally integrated Autoregressive Moving Average (ARMA) process for the elements of the Cholesky factors of the observed matrix series. This approach allows for joint modelling of the whole covariance matrix and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions on the model. The model is particularly suited to capture the long memory, typically observed in volatility processes of financial assets. We describe the forecasting procedure and provide an empirical application.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
330 Wirtschaft
Schlagwörter
Forecasting, Fractional integration, Long memory processes, Realized covariance
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Zitieren
ISO 690CHIRIAC, Roxana, Valeri VOEV, 2007. Dynamic Modelling and Forecasting of Realized Covariance Matrices
BibTex
@techreport{Chiriac2007Dynam-1894,
  year={2007},
  series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie},
  title={Dynamic Modelling and Forecasting of Realized Covariance Matrices},
  number={2007/},
  author={Chiriac, Roxana and Voev, Valeri},
  note={Link zur Originalveröffentlichung: <br /> http://www.eea-esem.com/files/papers/EEA-ESEM/2008/975/Chiriac_Voev.pdf}
}
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Link zur Originalveröffentlichung:
http://www.eea-esem.com/files/papers/EEA-ESEM/2008/975/Chiriac_Voev.pdf
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