Blasques, F., Ji, J. and Lucas, A. (2016). Semiparametric score driven volatility models Computational Statistics and Data Analysis, 100(August):58--69.
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Affiliated authors
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Publication year2016
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JournalComputational Statistics and Data Analysis
A new semiparametric observation-driven volatility model is proposed. In contrast to the standard semiparametric generalized autoregressive conditional heteroskedasticity (GARCH) model, the form of the error density has a direct influence on both the semiparametric likelihood and the volatility dynamics. The estimator is shown to consistently estimate the conditional pseudo true parameters of the model. Simulation-based evidence and an empirical application to stock return data confirm that the new statistical model realizes substantial improvements compared to GARCH type models and quasi-maximum likelihood estimation if errors are fat-tailed and possibly skewed.