基于指数构建的新能源汽车产业上中下游行业关联性及其影响因素研究

新能源汽车行业近年来因其迅速发展和广阔前景得到了广泛关注，我国也形成了较为完善的新能源产业链。对新能源汽车行业股价进行研究能够从资本市场的角度了解产业链内部的关联性和相互影响情况。本文从新能源汽车产业链上中下游行业中各筛选出10家具有代表性的上市公司，以2018年7月1日至2023年6月30日为研究区间，分别构造了上中下游行业的股价指数。

基于所构建的股指，本文利用时间序列分析方法，通过对上中下游股指进行协整检验并建立误差修正模型，发现上游和中游股价间存在长期均衡关系，下游股价则较为平稳，与上中游之间缺乏显著的长期均衡关系；通过建立BEKK-GARCH模型并进行波动溢出效应检验，发现新能源汽车产业链相邻层级股价间存在相互波动溢出效应，上游对下游也存在单向的波动溢出。

此外，本文还计算了产业链各层级之间的动态条件相关系数，并对影响相关性变动的因素进行了探究。研究发现，油价波动对各层级间的相关性均造成显著正向影响，制造业PMI对相关性造成显著负向影响。相比之下，新能源汽车销量增速仅能影响到中游和下游之间的相关性，而锂矿价格波动则不会对产业链相关性造成显著影响。

本文还根据实证结果对新能源汽车产业链各层级股价相互关联的机制进行了推测，并为公司管理者、投资者以及政策制定者提供了合理建议，以保障我国新能源汽车行业的平稳健康发展。

In recent years, the new energy vehicle industry has received widespread attention due to its rapid development and broad prospects. China has also formed a relatively complete new energy industry chain. Studying the stock prices of the new energy industry can help us understand the internal relationships and mutual influences within the industry chain from the perspective of the capital market. This article selects 10 representative listed companies from each of the upstream, midstream, and downstream industries in the new energy industry vehicle chain, and constructs stock price indices for each of these industries using the research period from July 1, 2018 to June 30, 2023.

Based on the constructed stock indices, this article utilizes time series analysis methods to conduct cointegration tests on the upstream, midstream, and downstream stock indices and establishes error correction models. The results reveal that there exists a long-term equilibrium relationship between upstream and midstream stock prices, while downstream stock prices are relatively stable and lack significant long-term equilibrium relationships with upstream and midstream prices. By establishing a BEKK-GARCH model and conducting a volatility spillover effect test, it is found that there exists mutual volatility spillover effects between adjacent levels of the new energy vehicle industry chain, and there is also a unidirectional volatility spillover from upstream to downstream.

Furthermore, this article calculates the dynamic conditional correlation coefficients between various levels of the industry chain and explores the factors that influence changes in correlation. The study finds that oil price fluctuations have a significant positive impact on the correlation between all levels, while the manufacturing PMI has a significant negative impact on correlation. In contrast, the growth rate of new energy vehicle sales only affects the correlation between the midstream and downstream, while fluctuations in lithium ore prices do not have a significant impact on the correlation within the industry chain.

Based on the empirical results, the article also speculates on the mechanisms of mutual correlation among stock prices at various levels of the new energy industry chain and provides reasonable suggestions for company managers, investors, and policy makers to ensure the stable and healthy development of China's new energy vehicle industry.

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