Publication: Threshold stochastic volatility: properties and forecasting
Loading...
Identifiers
Publication date
2017-11-01
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Impact
Export
Abstract
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 outof-
sample, it seems that including both threshold variables and correlated errors may be a
good compromise.
Description
Keywords
Conditional heteroscedasticity, Leverage effect, MCMC estimator, Option pricing, Volatility forecasting
Bibliographic citation
Mao, X., Ruiz, E., & Veiga, H. (2017). Threshold stochastic volatility: Properties and forecasting. International Journal of Forecasting, 33 (4), pp. 1105-1123.