METHOD OF MOMENTS ESTIMATION FOR LÉVY-DRIVEN ORNSTEIN–UHLENBECK STOCHASTIC VOLATILITY MODELS
From MaRDI portal
Publication:5051950
DOI10.1017/S0269964820000315OpenAlexW3033602016MaRDI QIDQ5051950
Yan-Feng Wu, Zeyu Zheng, Jianqiang Hu, Xiangyu Yang
Publication date: 18 November 2022
Published in: Probability in the Engineering and Informational Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1017/s0269964820000315
parameter estimationmethod of momentsstochastic volatility modelconsistency and asymptotic normality
Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)
Cites Work
- Unnamed Item
- Unnamed Item
- Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility
- Bayesian estimation of stochastic volatility models based on OU processes with marginal gamma law
- Time series: theory and methods.
- Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study
- Computational techniques for econometrics and economic analysis
- Quasi-maximum likelihood estimation of stochastic volatility models
- On multidimensional Ornstein-Uhlenbeck processes driven by a general Lévy process
- Moment estimators for the parameters of Ornstein-Uhlenbeck processes driven by compound Poisson processes
- On the system of difference equations \(x_n=c_ny_{n-3}/(a_n+b_ny_{n-1}x_{n-2}y_{n-3})\),\(y_n=\gamma_nx_{n-3}/(\alpha_n+\beta_nx_{n-1}y_{n-2}x_{n-3})\)
- Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes
- Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
- Inference for Lévy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo
- Simulated Moments Estimation of Markov Models of Asset Prices
- Multivariate Stochastic Variance Models
- Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions
- Bayesian Inference for Non-Gaussian Ornstein–Uhlenbeck Stochastic Volatility Processes
- MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
- GARCH Models
- 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
- Method of moment estimation in the COGARCH(1,1) model
- Some Limit Theorems for Stationary Processes
