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Tests for conditional heteroscedasticity of functional data

Rice, G., Wirjanto, T., Zhao, Y. (2020) Tests for conditional heteroscedasticity of functional data. Journal of Time Series Analysis, 41 (6). pp. 733-758. ISSN 0143-9782. (doi:10.1111/jtsa.12532) (KAR id:93921)

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Official URL:
http://dx.doi.org/10.1111/jtsa.12532

Abstract

Functional data objects derived from high-frequency financial data often exhibit volatility clustering. Versions of functional generalized autoregressive conditionally heteroscedastic (FGARCH) models have recently been proposed to describe such data, however so far basic diagnostic tests for these models are not available. We propose two portmanteau type tests to measure conditional heteroscedasticity in the squares of asset return curves. A complete asymptotic theory is provided for each test. We also show how such tests can be adapted and applied to model residuals to evaluate adequacy, and inform order selection, of FGARCH models. Simulation results show that both tests have good size and power to detect conditional heteroscedasticity and model mis-specification in finite samples. In an application, the tests show that intra-day asset return curves exhibit conditional heteroscedasticity. This conditional heteroscedasticity cannot be explained by the magnitude of inter-daily returns alone, but it can be adequately modeled by an FGARCH(1,1) model.

Item Type: Article
DOI/Identification number: 10.1111/jtsa.12532
Subjects: H Social Sciences
H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Yuqian Zhao
Date Deposited: 17 May 2022 09:52 UTC
Last Modified: 18 May 2022 10:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/93921 (The current URI for this page, for reference purposes)
Zhao, Y.: https://orcid.org/0000-0002-5396-3316
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