RT Book, Section
T1 Multivariate Stochastic Variance Models
A1 Harvey, Andrew
A1 Ruiz Ortega, Esther
A1 Shephard, Neil
AB Changes in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.
PB Oxford University Press
SN 9780198774327
SN 019877432X
YR 1995
FD 1995
LK https://hdl.handle.net/10016/4783
UL https://hdl.handle.net/10016/4783
LA eng
NO Publicado además en: The Review of Economic Studies,  1994,  vol. 61,  n. 2,  p. 247-264
NO Publicado además en: Recent developments in Time Series,  2003,  vol. 2, pp. 134-152
NO Publicado además en: Selected Readings for Stochastic Volatility,  2005,  p. 156-176,  ISBN10: 0199257191,  ISBN13: 9780199257195
DS e-Archivo
RD 1 jun. 2024