Discrete-time stochastic volatility models and MCMC-based statistical inference - Nikolaus Hautsch; Yangguoyi Ou (Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, 2008)
In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates.
Keywords: C15; C22; G12; 330; Stochastic volatility; Markov chain Monte Carlo; Metropolis-Hastings algorithm Jump Processes; Kapitalertrag; Volatilität; Stochastischer Prozess; Markovscher Prozess; Monte-Carlo-Methode; Bayes-Statistik; Theorie; Schätzung; Aktienindex; Wechselkurs; Deutschland; USA