Estimating and forecasting generalized fractional long memory stochastic volatility models

Artikel

This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 10 ; Year: 2017 ; Issue: 4 ; Pages: 1-16 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Methodological Issues: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Financial Econometrics
• Subject: stochastic volatility ; GARCH models ; Gegenbauer polynomial ; long memory ; spectral likelihood ; estimation ; forecasting

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Peiris, Shelton ; Asai, Manabu ; McAleer, Michael
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2017

Further information

• DOI: doi:10.3390/jrfm10040023
• Handle: http://hdl.handle.net/10419/238848
• Last update: 10.03.2025, 11:42 AM CET