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Akashi, Fumiya; Liu, Yan; Taniguchi, Masanobu

An empirical likelihood approach for symmetric \(\alpha\)-stable processes. (English) Zbl 1342.60071

Bernoulli 21, No. 4, 2093-2119 (2015).
Summary: The empirical likelihood approach is one of the non-parametric statistical methods, which is applied to the hypothesis testing or construction of confidence regions for pivotal unknown quantities. This method has been applied to the case of independent identically distributed random variables and second order stationary processes. In recent years, we observe heavy-tailed data in many fields. To model such data suitably, we consider symmetric scalar and multivariate \(\alpha\)-stable linear processes generated by an infinite variance innovation sequence. We use a Whittle likelihood type estimating function in the empirical likelihood ratio function and derive the asymptotic distribution of the empirical likelihood ratio statistic for \(\alpha\)-stable linear processes. With the empirical likelihood statistic approach, the theory of estimation and testing for second order stationary processes is nicely extended to heavy-tailed data analyses, not straightforward, and applicable to a lot of financial statistical analyses.

Cited in 7 Documents

MSC:

60G52 Stable stochastic processes
62G05 Nonparametric estimation
62G10 Nonparametric hypothesis testing
62G15 Nonparametric tolerance and confidence regions
62G20 Asymptotic properties of nonparametric inference

Keywords:

\(\alpha\)-stable processes; empirical likelihood ratio; confidence regions; heavy tails; normalized power transfer function; self-normalized periodogram; Whittle likelihood
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\textit{F. Akashi} et al., Bernoulli 21, No. 4, 2093--2119 (2015; Zbl 1342.60071)
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References:

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