Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions

DOI10.1080/14697688.2016.1185142zbMath1405.62147OpenAlexW2415850724MaRDI QIDQ4554510

No author found.

Publication date: 14 November 2018

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

Full work available at URL: https://doi.org/10.1080/14697688.2016.1185142

zbMATH Keywords

Lévy processesmaximum likelihood estimationstochastic volatilitycharacteristic functiongradient estimationhidden Markov structure

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Applications of statistics to actuarial sciences and financial mathematics (62P05) Markov processes: estimation; hidden Markov models (62M05)

Related Items (5)

METHOD OF MOMENTS ESTIMATION FOR LÉVY-DRIVEN ORNSTEIN–UHLENBECK STOCHASTIC VOLATILITY MODELS ⋮ Exact simulation of the Ornstein-Uhlenbeck driven stochastic volatility model ⋮ 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

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian hypothesis testing in latent variable models
• Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study
• Quasi-maximum likelihood estimation of stochastic volatility models
• Markov chain Monte Carlo methods for stochastic volatility models.
• Stochastic calculus for finance. II: Continuous-time models.
• Indirect inference methods for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes
• Inference in hidden Markov models.
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• A Theory of the Term Structure of Interest Rates
• Inverting Analytic Characteristic Functions and Financial Applications
• Inference for Lévy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo
• A New Stochastic Derivative Estimator for Discontinuous Payoff Functions with Application to Financial Derivatives
• Sensitivity Estimates from Characteristic Functions
• Bayesian Inference for Non-Gaussian Ornstein–Uhlenbeck Stochastic Volatility Processes
• Financial Modelling with Jump Processes
• Gradient-based simulated maximum likelihood estimation for Lévy-driven Ornstein–Uhlenbeck stochastic volatility models
• A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
• Stock Price Distributions with Stochastic Volatility: An Analytic Approach
• Simulation and inference for stochastic volatility models driven by Levy processes

This page was built for publication: Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions