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Semiparametric score driven volatility models

Francisco Blasques (), Jiangyu Ji and Andre Lucas ()

Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 58-69

Abstract: 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.

Keywords: Time-varying volatility; Generalized autoregressive score model; Observation driven models; Kernel density estimation (search for similar items in EconPapers)
Date: 2016
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Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:58-69