Empirical Characteristic Function Estimation and Its Applications

By Jun Yu. 2004

213 citations recorded by the source corpus.

Abstract This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/357c6d34d8484e4bfe1249a95f8fa23a4c4c18ba

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Citations in the corpus

A Didactic Note on Affine Stochastic Volatility Models (2006, background): “As far as estimation is concerned, the knowledge of the joint characteristic function can be exploited for generalized moment estimators (cf. Jiang & Knight (2002) and Yu (2004) for an overview).”
Bibliography on stable distributions, processes and related topics (2004, background): “…Csorgo (1981), Feuerverger and Mureika (1977), Feuerverger and McDunnough (1981b), Feuerverger and McDunnough (1981a), Kogon and Williams (1998), Yu (2004), Besbeas and Morgan (2008), Bibalan et al. (2017), Kateregga et al. (2017), Zhang (2018) Approximate maximum likelihood estimation Bee and…”
CONSTANT PROPORTION PORTFOLIO INSURANCE IN THE PRESENCE OF JUMPS IN ASSET PRICES (2009, unknown): “Option based portfolio insurance (El Karoui, Jeanblanc, and Lacoste 2005; Leland and Rubinstein 1988) combines a position in the risky asset with a put option on this asset. In many cases options on a given fund or portfolio may not be available in the market: an alternative approach is to use const”
Local explosion modelling by non‐causal process (2017, unknown): “For dependent data, see for instance Yu (2004), Taufer and Leonenko (2009) and the references therein.”
Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function (2014, unknown): “…the Cox-Ingersoll-Ross process (e.g. Zhou, 2000), the Heston process and a mixture of stochastic volatility and jump processes (e.g. Jiang and Knight, 2002, Rockinger and Semenova, 2005 and Chacko and Viceira, 2003) and affine jump diffusion models (e.g. Yu, 2004), yielding interesting results.”
Maximum likelihood estimation of stochastic frontier models by the Fourier transform (2012, methodology): “For an excellent review of this and other techniques in the context of the empirical CF, see Yu (2004).”