Stochastic volatility modelling in continuous time with general marginal distributions: inference, prediction and model selection

DOI10.1016/j.jspi.2006.07.015zbMath1255.60074OpenAlexW2039148481MaRDI QIDQ997294

Matthew P. S. Gander, David A. Stephens

Publication date: 23 July 2007

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2006.07.015

zbMATH Keywords

model selectionstochastic volatilityMarkov chain Monte CarloLévy process

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

Multivariate Wishart stochastic volatility and changes in regime, Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC, Inference for Lévy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo, Characteristic function estimation of non-Gaussian Ornstein-Uhlenbeck processes, Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models, Closed-form approximations with respect to the mixing solution for option pricing under stochastic volatility, Inference procedures for stable-Paretian stochastic volatility models, Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes, Transition law-based simulation of generalized inverse Gaussian Ornstein-Uhlenbeck processes, The Ornstein-Uhlenbeck Dirichlet process and other time-varying processes for Bayesian nonparametric inference, SWITCHING TO NONAFFINE STOCHASTIC VOLATILITY: A CLOSED-FORM EXPANSION FOR THE INVERSE GAMMA MODEL, Exact simulation of Ornstein–Uhlenbeck tempered stable processes, Moment estimators for the parameters of Ornstein-Uhlenbeck processes driven by compound Poisson processes, Particle Markov Chain Monte Carlo Methods, Bayesian parameter inference for partially observed stochastic differential equations driven by fractional Brownian motion

Uses Software

• STABLE
• BUGS
• longmemo

Cites Work

• The Pricing of Options and Corporate Liabilities
• Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility
• Itô's formula with respect to fractional Brownian motion and its application
• Cramér's estimate for Lévy processes
• Arbitrage in fractional Brownian motion models
• Modeling and pricing long memory in stock market volatility
• Long memory vs. structural change in financial time series
• BUGS for a Bayesian analysis of stochastic volatility models
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• Arbitrage with Fractional Brownian Motion
• Transform Analysis and Asset Pricing for Affine Jump-diffusions
• Bayesian Inference for Non-Gaussian Ornstein–Uhlenbeck Stochastic Volatility Processes
• Stochastic Volatility for Lévy Processes
• Option Pricing in Stochastic Volatility Models of the Ornstein‐Uhlenbeck type
• Simulation and inference for stochastic volatility models driven by Levy processes
• A Representation of Independent Increment Processes without Gaussian Components
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item