Threshold stochastic volatility: Properties and forecasting
Esther Ruiz and
Helena Veiga ()
International Journal of Forecasting, 2017, vol. 33, issue 4, 1105-1123
We analyze the ability of Threshold Stochastic Volatility (TSV) models to represent and forecast asymmetric volatilities. First, we derive the statistical properties of TSV models. Second, we demonstrate the good finite sample properties of a MCMC estimator, implemented in the software package WinBUGS, when estimating the parameters of a general specification, denoted CTSV, that nests the TSV and asymmetric autoregressive stochastic volatility (A-ARSV) models. The MCMC estimator also discriminates between the two specifications and allows us to obtain volatility forecasts. Third, we analyze daily S&P 500 and FTSE 100 returns and show that the estimated CTSV model implies plug-in moments that are slightly closer to the observed sample moments than those implied by other nested specifications. Furthermore, different asymmetric specifications generate rather different European options prices. Finally, although none of the models clearly emerge as best out-of-sample, it seems that including both threshold variables and correlated errors may be a good compromise.
Keywords: Conditional heteroscedasticity; Leverage effect; MCMC estimator; Option pricing; Volatility forecasting (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:4:p:1105-1123
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().