Title :
Modeling the persistent volatility of asset returns
Author :
Breidt, F. Jay ; Crato, Nuno ; De Lima, Pedro J F
Author_Institution :
Iowa State Univ., Ames, IA, USA
Abstract :
Empirical evidence suggests that the volatility of financial asset returns displays some type of persistence that cannot be appropriately modeled within the classical GARCH (generalized autoregressive conditional heteroskedastic) setting. Two alternative frameworks have been recently suggested to incorporate this type of persistence: fractionally integrated models, such as the long-memory stochastic volatility (LMSV) model, and regime-switching schemes, such as the `switching ARCH´ (SWARCH). A switching stochastic volatility (SWSV) model is a convenient and flexible alternative which can be directly compared with the LMSV model. Asymptotically, the autocorrelation functions of switching-regime and long-memory models have quite distinct behaviors. This fact can help the researcher to make the appropriate choices in face of empirical data
Keywords :
autoregressive moving average processes; bifurcation; economic cybernetics; finance; modelling; switching; GARCH model; autocorrelation functions; autoregressive integrated moving average; financial asset returns; fractional ARIMA; fractionally integrated models; generalized autoregressive conditional heteroskedastic model; long-memory stochastic volatility model; persistent volatility; regime-switching schemes; stochastic variance; structural breaks; switching ARCH; switching stochastic volatility model; Appropriate technology; Autocorrelation; Displays; Economic forecasting; Portfolios; Predictive models; Pricing; Stochastic processes; Structural engineering;
Conference_Titel :
Computational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-4133-3
DOI :
10.1109/CIFER.1997.618947