The volatility pattern of financial time series is often characterized by several peaks and abrupt changes, consistent with the time-varying coefficients of the underlying data-generating process. As a consequence, the model-based classification of the volatility of a set of assets could vary over a period of time.We propose a procedure to classify the unconditional volatility obtained from an extended family of Multiplicative Error Models with time-varying coefficients to verify if it changes in correspondence with different regimes or particular dates. The proposed procedure is experimented on 15 stock indices.

Volatility clustering in the presence of time-varying model parameters

OTRANTO, Edoardo
2013-01-01

Abstract

The volatility pattern of financial time series is often characterized by several peaks and abrupt changes, consistent with the time-varying coefficients of the underlying data-generating process. As a consequence, the model-based classification of the volatility of a set of assets could vary over a period of time.We propose a procedure to classify the unconditional volatility obtained from an extended family of Multiplicative Error Models with time-varying coefficients to verify if it changes in correspondence with different regimes or particular dates. The proposed procedure is experimented on 15 stock indices.
2013
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/2500754
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact