Stochastic volatility models as hidden Markov models and statistical applications

By Valentine Genon-Catalot, T. Jeantheau, C. Larédo. 2000

116 citations recorded by the source corpus.

Subfields: Finance, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/4b6db9b1a2b1664e33963deb2f361d839e58aab6

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Citations in the corpus

Econometric analysis of realized volatility and its use in estimating stochastic volatility models (2002, unknown): “…function for the ARMA representation of the process, where the noise in the representation is both white and strong mixing (strong mixing follows from Sørensen (2000) and Genon-Catalot et al. (2000) who showed that if the volatility is strong mixing then squared returns are strong mixing).”
A Test for model specification of diffusion processes (2007, background): “mposes the strict stationarity and α-mixing condition on {Xt}. Under certain conditions, such as Assumption A2 of A¨ıt-Sahalia [1] and Conditions (A4) and (A5) of Genon-Catalot, Jeantheau and Lar´edo [26], Assumption 1(i) holds. Assumption 1(ii) and (iii) are quite standard conditions imposed on the”
Nonparametric inference for Lévy-driven Ornstein-Uhlenbeck processes (2005, background): “This in turn implies that (X n) is ergodic (Genon-Catalot et al. 2000).”
Statistical Methods for Stochastic Differential Equations (2012, methodology): “Estimation based on this type of data was considered by Gloter (2000), Bollerslev & Wooldridge (1992), Ditlevsen & Sørensen (2004), Gloter (2006), and Baltazar-Larios & Sørensen (2009).”
A multivariate Ornstein-Uhlenbeck type stochastic volatility model (2009, methodology): “For µ = β = 0 strong mixing in the univariate OU type stochastic volatility model has been obtained in Genon-Catalot, Jeantheau, and Larédo (2000) and Sørensen (2000).”