Calibrating the exponential Ornstein–Uhlenbeck multiscale stochastic volatility model

DOI10.1080/14697688.2012.738929zbMath1294.91194OpenAlexW2064516770MaRDI QIDQ2879040

Cyrille Dubarry, Randal Douc

Publication date: 5 September 2014

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2012.738929

zbMATH Keywords

inferenceexpectation-maximization algorithmparticle smoothingmaximum split data estimatemultiscale stochastic volatility model

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Markov processes: estimation; hidden Markov models (62M05) Stochastic models in economics (91B70) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)

Cites Work

• Sequential Monte Carlo smoothing for general state space hidden Markov models
• Alternative models for stock price dynamics.
• Generalized autoregressive conditional heteroscedasticity
• Consistent and asymptotically normal parameter estimates for hidden Markov models
• Sequential Monte Carlo Methods in Practice
• Multiple time scales and the exponential Ornstein–Uhlenbeck stochastic volatility model
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models