Download PDFOpen PDF in browserStochastic volatility model’s predictive relevance for Equity MarketsEasyChair Preprint 233917 pages•Date: January 8, 2020AbstractThis paper builds and implements multifactor stochastic volatility models. The main objective is volatility prediction and its relevance for equity markets. The paper outlines stylised facts from volatility literature showing density tails, persistence, mean reversion, asymmetry and long memory, all contributing to systematic dependencies. Applying long simulations from stochastic volatility (SV) models and filter volatility using a form of nonlinear Kalman filtering, the unobservables of the nonlinear latent variables can be forecasted with associated fit characteristics. The paper uses European equity data from United Kingdom (Ftse100) and Norway (Equinor) for relevance arguments and illustrational prediction purposes. Multifactor SV models seem to enrich volatility predictions empowering equity market relevance. Keyphrases: Bayesian estimators, Kalman filter, M-H algorithm, MCMC Simulations, stochastic volatility
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