TESTS FOR CONDITIONAL HETEROSCEDASTICITY WITH FUNCTIONAL DATA AND GOODNESS-OF-FIT TESTS FOR FGARCH MODELS

浙江大学数学科学学院九十周年院庆系列活动之六十五

TESTS FOR CONDITIONAL HETEROSCEDASTICITY WITH FUNCTIONAL DATA AND GOODNESS-OF-FIT TESTS FOR FGARCH MODELS

时间 / Date and Time:           2018/08/06 (Mon), 10:00 - 11:00

地点 / Venue:                     浙江大学玉泉校区逸夫工商楼200-9楼报告厅

报告人 / Speaker:                Professor Tony Wirjanto

                                       University of Waterloo

                                        https://uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto l

摘要 / Abstract:

Functional data objects that are derived from high-frequency financial data often exhibit volatility clustering characteristic of conditionally heteroscedastic time series. Versions of functional generalized autoregressive conditionally heteroscedastic (FGARCH) models have recently been proposed to describe such data, but so far there are no diagnostic tools available for these models. We propose two portmanteau type tests to measure conditional heteroscedasticity in the squares of return curves. A complete asymptotic theory is provided for each test, and we further show how these methods can be applied to model residuals in order to evaluate the adequacy and aid in order selection of FGARCH models. Simulation studies show that both tests have good size and power to detect conditional heteroscedasticity and model mis-specification in finite samples. In an application, the proposed methods suggest that intra-day asset return curves exhibit conditional heteroscedasticity. Moreover, we find that the magnitude of inter-daily returns alone is not sufficient to capture this conditional heteroscedasticity, but it is adequately modeled by an FGARCH(1,1) model.

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联系人 / Enquiries:

张  奕 / Yi Zhang

Tel: (86) 13588118020    Email: zhangyi63@zju.edu.cn