Tests for Conditional Heteroscedasticity of Functional Data
26 Pages Posted: 6 Oct 2020
Date Written: November 1, 2020
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.
Keywords: Functional time series, heteroscedasticity testing, model diagnostic checking, high‐frequency volatility models, intra‐day asset price
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