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基于ARMA-GARCH模型创业板综合指数波动分析
Analysis of Volatility in the ChiNext Composite Index Based on ARMA-GARCH Model

DOI: 10.12677/ecl.2024.1341145, PP. 241-249

Keywords: 创业板综合指数,波动率预测,ARMA-GARCH模型
ChiNext Composite Index
, Volatility Prediction, ARMA-GARCH Model

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Abstract:

创业板的推出为高成长企业增加了上市的机会,也为投资者提供了更多的投资渠道,相应地创业板市场股价具有更高的波动性,也为投资者带来了更高的收益与风险。本文选取2014年1月至2023年12月创业板综合指数日收盘价的历史数据,通过对其对数收益率序列建立ARMA-GARCH族模型,比较不同阶数下的模型拟合的优劣选出最优模型,对创业板综合指数收盘价进行预测。研究结果表明,基于ARMA (3, 2)-TGARCH (1, 1)模型的预测误差相对较小,误差绝对值几乎都在0.2%以内。
The launch of the ChiNext board has increased the opportunities for high growth enterprises to go public and provided investors with more investment channels. Correspondingly, the stock price of the ChiNext board market has higher volatility and also brings higher returns and risks to investors. This article selects historical data of the daily closing prices of the ChiNext Composite Index from January 2014 to December 2023. By establishing an ARMA-GARCH family model based on its logarithmic return series, the optimal model is selected by comparing the fit of models at different orders to predict the closing prices of the ChiNext Composite Index. The research results indicate that the prediction error based on the ARMA (3, 2)-TGARCH (1, 1) model is relatively small, and the absolute value of the error is almost within 0.2%.

References

[1]  Engle, R.F., Ng, V.K. and Rothschild, M. (1990) Asset Pricing with a Factor-Arch Covariance Structure: Empirical Estimates for Treasury Bills. Journal of Econometrics, 45, 210-236.
https://doi.org/10.1016/0304-4076(90)90099-f
[2]  Chiang, D. (2001) Empirical Analysis of Stock Returns and Volatility: Evidence from Seven Asian Stock Markets Based on TAR-GARCH Model. Review of Quantitative Finance and Accounting, 17, 301-318.
[3]  唐齐鸣, 陈健. 中国股市的ARCH效应分析[J]. 世界经济, 2001, 24(3): 29-36.
[4]  岳朝龙. 上海股市收益率GARCH模型族的实证研究[J]. 数量经济与技术经济研究, 2001, 18(6): 126-129.
[5]  田华, 曹家和. 中国股票市场报酬与波动的GARCH-M模型[J]. 系统工程理论与实践, 2003, 23(8): 81-86.
[6]  黄海南, 钟伟. GARCH类模型波动率预测评价[J]. 中国管理科学, 2007, 15(6): 13-19.
[7]  仝玉民. 股票市场波动性——来自沪深300指数的证据[J]. 金融经济, 2009(3): 81-83.
[8]  陈艳, 韩立磊. 沪深300指数收益波动性实证研究[J]. 金融经济, 2009(7): 70-72.
[9]  刘任重, 郭雪. 股指期货推出对股票现货市场波动性的影响[J]. 首都经济贸易大学学报, 2016, 18(2): 27-33.
[10]  Mhd Ruslan, S.M. and Mokhtar, K. (2021) Stock Market Volatility on Shipping Stock Prices: GARCH Models Approach. The Journal of Economic Asymmetries, 24, e00232.
https://doi.org/10.1016/j.jeca.2021.e00232
[11]  俞越. 基于ARMA-GARCH模型的沪深300指数回报率波动性研究[J]. 全国流通经济, 2022(28): 153-156.
[12]  王玉洁. 我国创业板市场波动规律研究——基于创业板指数的实证分析[J]. 时代金融, 2012(3): 219-220.
[13]  王红, 陈帅. 我国创业板市场弱式有效性的实证分析——基于Wild Bootstrap方差比检验[J]. 商业时代, 2014(32): 96-99.
[14]  王凯风. 深市创业板指数波动性量化研究——基于ARMA-TARCH模型与VaR方法[J]. 金融与经济, 2014(10): 62-66.
[15]  董佳慧, 张李兰. 我国创业板上市公司高管减持对股票市场影响的实证分析[J]. 市场论坛, 2016(9): 60-63.
[16]  王兆瑞, 方壮志. 人民币汇率波动对创业板指数影响的实证分析[J]. 金融理论与实践, 2016(4): 68-71.

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