On Geometric Ergodicity of Skewed - SVCHARME models
Jerzy P. Rydlewski, Ma{\l}gorzata Snarska
TL;DR
This paper establishes conditions under which a broad class of skewed nonparametric stochastic volatility models with hidden Markov switching exhibit geometric ergodicity, enhancing understanding of their long-term behavior.
Contribution
It provides new theoretical conditions for geometric ergodicity in complex skewed stochastic volatility models with hidden Markov chains.
Findings
Derived sufficient conditions for geometric ergodicity.
Extended ergodicity analysis to nonparametric skewed models.
Applicable to models with switching hidden Markov chains.
Abstract
Markov Chain Monte Carlo is repeatedly used to analyze the properties of intractable distributions in a convenient way. In this paper we derive conditions for geometric ergodicity of a general class of nonparametric stochastic volatility models with skewness driven by hidden Markov Chain with switching.
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Taxonomy
TopicsStochastic processes and financial applications · Markov Chains and Monte Carlo Methods · Financial Risk and Volatility Modeling