Non-parametric Estimation of GARCH (2, 2) Volatility model: A new Algorithm

Lucius Cassim ()

MPRA Paper from University Library of Munich, Germany

Abstract: The main objective of this paper is to provide an estimation approach for non-parametric GARCH (2, 2) volatility model. Specifically the paper, by combining the aspects of multivariate adaptive regression splines(MARS) model estimation algorithm proposed by Chung (2012) and an algorithm proposed by Buhlman and McNeil(200), develops an algorithm for non-parametrically estimating GARCH (2,2) volatility model. Just like the MARS algorithm, the algorithm that is developed in this paper takes a logarithmic transformation as a preliminary analysis to examine a nonparametric volatility model. The algorithm however differs from the MARS algorithm by assuming that the innovations are i.d.d. The algorithm developed follows similar steps to that of Buhlman and McNeil (200) but starts by semi parametric estimation of the GARCH model and not parametric while relaxing the dependency assumption of the innovations to avoid exposing the estimation procedure to risk of inconsistency in the event of misspecification errors.

Keywords: GARCH (2; 2); MARS; Algorithm; Parametric; Semi parametric; Nonparametric (search for similar items in EconPapers)
JEL-codes: C1 C14 C4 (search for similar items in EconPapers)
Date: 2018-05-18
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
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