Estimation and asymptotic covariance matrix for stochastic volatility models
DOI10.1007/S10260-016-0373-8zbMath1384.62319OpenAlexW2549029815MaRDI QIDQ1697869
Publication date: 20 February 2018
Published in: Statistical Methods and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10260-016-0373-8
consistencyasymptotic normalitystochastic volatilityfinancial returnsasymptotically stationary processasymptotic variance-covariance matrix
Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)
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Cites Work
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- Estimation of stochastic volatility models via Monte Carlo maximum likelihood
- Quasi-maximum likelihood estimation of stochastic volatility models
- Markov chain Monte Carlo methods for stochastic volatility models.
- Maximum likelihood estimation of a latent variable time-series model
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Encompassing and indirect inference
- MODELING STOCHASTIC VOLATILITY: A REVIEW AND COMPARATIVE STUDY
- Theory & Methods: Estimation of the Stochastic Volatility Model by the Empirical Characteristic Function Method
- Linear‐representation Based Estimation of Stochastic Volatility Models
- GMM estimation of a realized stochastic volatility model: A Monte Carlo study
- Estimating stochastic volatility models through indirect inference
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