Year: 2024
Type: article
Abstract: The concepts of physical dependence and approximability have been extensively used over the past two decades to quantify nonlinear dependence in time series. We show that most stochastic volatility models satisfy both dependence conditions, even if their realizations take values in abstract Hilbert spaces, thus covering univariate, multi‐variate and functional models. Our results can be used to apply to general stochastic volatility models a multitude of inferential procedures established for Bernoulli shifts.
Source: Journal of Time Series Analysis
DOI: 10.1111/jtsa.12765
Authors: Piotr Kokoszka, Neda Mohammadi, Haonan Wang
Institution: Colorado State University
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,340
Funder: National Science Foundation
OpenAlex ID: W4401505970