Daily LGARCH model estimation using high frequency data

Xiaoling Chen; Xingfa Zhang; Yuan Li; Qiang Xiong; Xiaoling Chen; Xingfa Zhang; Yuan Li; Qiang Xiong

doi:10.3934/DSFE.2021009

Data Science in Finance and Economics

2021, Volume 1, Issue 2: 165-179. doi: 10.3934/DSFE.2021009

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Research article

Daily LGARCH model estimation using high frequency data

School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China

Received: 29 July 2021 Accepted: 27 September 2021 Published: 08 October 2021
JEL Codes: C13, C22

In this paper, we introduce the intraday high frequency data to estimate the daily linear generalized autoregressive conditional heteroscedasticity (LGARCH) model. Based on the volatility proxies constructed from the intraday high frequency data, the quasi maximum likelihood estimation (QMLE) of the daily LGARCH model and its asymptotic distribution are studied under some regular assumptions. One criterion is also given to choose the optimal volatility proxy according to the asymptotic results. Simulation studies show that the QMLE of the parameters performs well. It is also found that introducing the intraday high frequency data can significantly improve the estimation precision. The proposed method is applied to analyze the SSE 50 Index, which consists of the 50 largest and most liquid A-share stocks listed on Shanghai Stock Exchange. Empirical results show the method is of potential application value.
- LGARCH model,
- intraday high frequency data,
- quasi maximum likelihood estimation
Citation: Xiaoling Chen, Xingfa Zhang, Yuan Li, Qiang Xiong. Daily LGARCH model estimation using high frequency data[J]. Data Science in Finance and Economics, 2021, 1(2): 165-179. doi: 10.3934/DSFE.2021009

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Abstract

In this paper, we introduce the intraday high frequency data to estimate the daily linear generalized autoregressive conditional heteroscedasticity (LGARCH) model. Based on the volatility proxies constructed from the intraday high frequency data, the quasi maximum likelihood estimation (QMLE) of the daily LGARCH model and its asymptotic distribution are studied under some regular assumptions. One criterion is also given to choose the optimal volatility proxy according to the asymptotic results. Simulation studies show that the QMLE of the parameters performs well. It is also found that introducing the intraday high frequency data can significantly improve the estimation precision. The proposed method is applied to analyze the SSE 50 Index, which consists of the 50 largest and most liquid A-share stocks listed on Shanghai Stock Exchange. Empirical results show the method is of potential application value.

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