Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information
AbstractMost asset returns exhibit high volatility and its persistence. Heuristically, this paper focuses on the role of surprising information in high volatility processes and indicates that dismissing surprising information may lead to considerable loss in forecast accuracy. In response, this paper considers the corresponding extension of the modified MDH to surprising information, and proposes a bivariate stochastic volatility model incorporating surprising information in the volatility equations (BSV-SI), which is also designed to capture the dynamics of returns and trading volume. Using the South Korea stock index and trading volume series, it turns out that performance of the onestep- ahead forecasts of the BSV-SI model is apparently superior to those of other competitive models.
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Bibliographic InfoArticle provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.
Volume (Year): (2011)
Issue (Month): 3 (September)
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Volatility forecasting; Bivariate stochastic volatility model with surprising information; Modified mixture of distribution hypothesis; Realized volatility models; Markov Chain Monte Carlo (MCMC);
Find related papers by JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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