Gradient-based simulated maximum likelihood estimation for Lévy-driven Ornstein–Uhlenbeck stochastic volatility models

DOI10.1080/14697688.2013.832864zbMath1402.91809OpenAlexW2059063853MaRDI QIDQ5245899

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Publication date: 16 April 2015

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

Full work available at URL: https://doi.org/10.1080/14697688.2013.832864

zbMATH Keywords

Lévy processesstochastic volatilitymaximum likelihoodsequential Monte Carlosimulation optimizationOU processes

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Diffusion processes (60J60) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (8)

METHOD OF MOMENTS ESTIMATION FOR LÉVY-DRIVEN ORNSTEIN–UHLENBECK STOCHASTIC VOLATILITY MODELS ⋮ Calibration for multivariate Lévy-driven Ornstein-Uhlenbeck processes with applications to weak subordination ⋮ Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions ⋮ Exact simulation of the Ornstein-Uhlenbeck driven stochastic volatility model ⋮ Unnamed Item ⋮ Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling ⋮ Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation ⋮ Moment estimators for the parameters of Ornstein-Uhlenbeck processes driven by compound Poisson processes

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