Motivated by the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both the observed returns and realized measures to the latent conditional variance.

A stochastic volatility framework with analytical filtering

Giacomo Bormetti;Giulia Livieri
2017

Abstract

Motivated by the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both the observed returns and realized measures to the latent conditional variance.
2017
Settore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e Finanziarie
SIS 2017
Firenze
28 - 30 giugno 2017
Proceedings of the Conference of the Italian Statistical Society. Statistics and Data Science: new challenges, new generations
Firenze University Press
978-88-6453-521-0
Bayesian Inference; Monte Carlo Markov Chain; High-frequency; Re- alized volatility; ARG; Stochastic volatility
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/90802
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