Semiparametric Efficient Adaptive Estimation of the PTTGARCH model

Nicola Ciccarelli

MPRA Paper from University Library of Munich, Germany

Abstract: Financial data sets exhibit conditional heteroskedasticity and asymmetric volatility. In this paper we derive a semiparametric efficient adaptive estimator of a conditional heteroskedasticity and asymmetric volatility GARCH-type model (i.e., the PTTGARCH(1,1) model). Via kernel density estimation of the unknown density function of the innovation and via the Newton-Raphson technique applied on the root-n-consistent quasi-maximum likelihood estimator, we construct a more efficient estimator than the quasi-maximum likelihood estimator. Through Monte Carlo simulations, we show that the semiparametric estimator is adaptive for parameters in- cluded in the conditional variance of the model with respect to the unknown distribution of the innovation.

Keywords: Semiparametric adaptive estimation; Power-transformed and threshold GARCH. (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2016
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-net and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:72021

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