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An Alternative Maximum Entropy Model for Time-Varying Moments with Application to Financial Returns
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Klaus Herrmann
Published/Copyright:
May 11, 2010
With their article on Maximum Entropy (ME) densities for time-varying moments, Rockinger and Jondeau (2002) set a milestone for the application of information theoretic principles to the analysis of financial market data. In this note we briefly discuss the application of their approach to financial data, point out some shortcomings that it encounters and show how these can be overcome. Applying our model to different market indices, we find evidence for time-variability of skewness and kurtosis.
Published Online: 2010-5-11
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Estimation of Parameters in the Presence of Model Misspecification and Measurement Error
- An Alternative Maximum Entropy Model for Time-Varying Moments with Application to Financial Returns
- Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form
- First and Second Order Asymptotic Bias Correction of Nonlinear Estimators in a Non-Parametric Setting and an Application to the Smoothed Maximum Score Estimator
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Articles in the same Issue
- Article
- Estimation of Parameters in the Presence of Model Misspecification and Measurement Error
- An Alternative Maximum Entropy Model for Time-Varying Moments with Application to Financial Returns
- Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form
- First and Second Order Asymptotic Bias Correction of Nonlinear Estimators in a Non-Parametric Setting and an Application to the Smoothed Maximum Score Estimator
- Conditional Skewness, Kurtosis, and Density Specification Testing: Moment-Based versus Nonparametric Tests
