scholarly journals Garch Model Test Using High-Frequency Data

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1922
Author(s):  
Chunliang Deng ◽  
Xingfa Zhang ◽  
Yuan Li ◽  
Qiang Xiong
Keyword(s):  
High Frequency ◽  
Garch Model ◽  
Wald Test ◽  
Asymptotic Distributions ◽  
High Frequency Data ◽  
Ratio Test ◽  
Test Statistics ◽  
Frequency Data ◽  
Test Statistic ◽  
Potential Applications

This work is devoted to the study of the parameter test for the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Based on the daily GARCH model, using the parameter estimator obtained by intraday high-frequency data, the adjusted Likelihood Ratio test statistic and Wald test statistic are provided. Asymptotic distributions of the two adjusted test statistics are deducted and a way to select the optimal sampling frequency is also discussed. Simulation studies show that the proposed test statistics have better size and power than traditional ones (without using intraday high-frequency data). An empirical study is given to illustrate the potential applications of the proposed tests. The results show the idea of this article is of certain superiority and it can be extended to other GARCH type models.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

M-estimation for periodic GARCH model with high-frequency data

2017 ◽  
Vol 33 (3) ◽  
pp. 717-730 ◽  
Author(s):  
Peng-ying Fan ◽  
Si-xin Wu ◽  
Zi-long Zhao ◽  
Min Chen
Keyword(s):  
High Frequency ◽  
Garch Model ◽  
High Frequency Data ◽  
Frequency Data ◽  
M Estimation

Asymptotics for the systematic and idiosyncratic volatility with large dimensional high-frequency data

2019 ◽  
Vol 09 (03) ◽  
pp. 2050007
Author(s):  
Xin-Bing Kong ◽  
Jin-Guan Lin ◽  
Guang-Ying Liu
Keyword(s):  
High Frequency ◽  
Null Hypothesis ◽  
Laplace Transforms ◽  
High Frequency Data ◽  
Idiosyncratic Volatility ◽  
Test Statistics ◽  
Frequency Data ◽  
Independence Test ◽  
Kolmogorov Smirnov ◽  
Functional Central Limit

In this paper, we decompose the volatility of a diffusion process into systematic and idiosyncratic components, which are not identified with observations discretely sampled from univariate process. Using large dimensional high-frequency data and assuming a factor structure, we obtain consistent estimates of the Laplace transforms of the systematic and idiosyncratic volatility processes. Based on the discrepancy between realized bivariate Laplace transform of the pair of systematic and idiosyncratic volatility processes and the product of the two marginal Laplace transforms, we propose a Kolmogorov–Smirnov-type independence test statistics for the two components of the volatility process. A functional central limit theorem for the discrepancy is established under the null hypothesis that the systematic and idiosyncratic volatilities are independent. The limiting Gaussian process is realized by a simulated discrete skeleton process which can be applied to define an approximate critical region for an independence test.

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