Abstract
This paper builds and implements multifactor stochastic volatility models where the main objective is step ahead volatility prediction and to describe its relevance for the equity markets. The paper outlines stylised facts from the volatility literature showing density tails, persistence, mean reversion, asymmetry and long memory, all contributing to systematic data dependencies. As a by-product of the multifactor stochastic volatility model estimation, a long-simulated realization of the state vectors is available. The realization establishes a functional form of the conditional distribution, which is evaluated on observed data convenient for step ahead predictions. The paper uses European equity for relevance arguments and illustrational prediction purposes. Multifactor SV models empower volatility visibility and predictability enriching the amount of information available for equity market participants.
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Notes
- 1.
The methodology is designed for estimation and inference for models where (1) the likelihood is not available, (2) some variables are latent (unobservable), (3) the variables can be simulated and (4) there exist a well-specified and adequate statistical model for the simulations. The methodologies (General Scientific Models (GSM) and Efficient Method of Moments (EMM)) are general-purpose implementation of the Chernozhukov and Hong [9] estimator.
- 2.
For the Cholesky decomposition methodology see [4].
- 3.
See www.econ.duke.edu/webfiles/arg for software and applications of the MCMC Bayesian methodology. All models are coded in C/C++ and executable in both serial and parallel versions (OpenMPI).
- 4.
Filtered volatility is a data-dependent concept and the dynamic system must be sampled at the name frequency as the data to determine the density.
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Solibakke, P.B. (2020). Stochastic Volatility Models Predictive Relevance for Equity Markets. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2019. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-56219-9_9
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