In the process of discussing the relationship between volume and price in the stock market, the purpose of this paper is to consider how to take the flow of foreign capital into consideration, to determine whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price.,By comparing the relative advantages and disadvantages of the two main non-parametric methods mainstream, and taking the characteristics of the time series of the volume into consideration, the stochastic volatility with Volume (SV-VOL) model based on the APF-LW simulation method is used in the end, to explore and implement a more efficient estimation algorithm. And the volume is incorporated into the model for submersible quantization, by which the problem of insufficient use of volume information in previous research has been solved, which means that the development of the SV model is realized.,Through the Sequential Monte Carlo (SMC) algorithm, the effective estimation of the SV-VOL model is realized by programming. It is found that the stock market volume information is helpful to the prediction of the volatility of the stock price. The exchange market volume information affects the stock returns and the price-volume relationship, which is achieved indirectly through the net capital into stock market. The current exchange devaluation and fluctuation are not conducive to the restoration and recovery of the stock market.,It is still in the exploratory stage that whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price, and how to incorporate the exchange market volume information. This paper tries to determine the information weight of the exchange market volume according to the direct and indirect channels from the perspective of causality. The relevant practices and conclusions need to be tested and perfected.,Previous studies have neglected the influence of the information contained in the exchange market volume on the volatility of stock prices. To a certain extent, this research makes a useful supplement to the existing research, especially in the aspects of research problems, research paradigms, research methods and research conclusion.,SV model with volume information can not only effectively solve the inefficiency of information use problem contained in volume in traditional practice, but also further improve the estimation accuracy of the model by introducing the exchange market volume information into the model through weighted processing, which is a useful supplement to the existing literature. The SMC algorithm realized by programming is helpful to the further advancement and development of non-parametric algorithms. And this paper has made a useful attempt to determine the weight of the exchange market volume information, and some useful conclusions are drawn.

中文翻译：

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基于股票交易量信息视角的股价波动预测

在讨论股票交易量与价格之间的关系的过程中，本文的目的是考虑如何考虑外国资本的流动，确定是否包含交易量信息是否确实有助于预测股票交易量。通过比较两种主要的非参数方法主流的相对优缺点，并考虑到交易量的时间序列特征，基于交易量的随机波动率（SV-VOL）模型最后采用APF-LW仿真方法，探索并实现了一种更高效的估计算法。然后将体积合并到模型中进行潜水量化，从而解决了以往研究中体积信息利用不充分的问题，实现了SV模型的开发。通过顺序蒙特卡洛算法，对SV-VOL模型进行了有效的估计。通过编程实现。发现股票市场数量信息有助于预测股票价格的波动性。交易所市场数量信息会影响股票收益和价格-数量关系，这是通过将净资本间接流入股票市场来实现的。当前的汇率贬值和波动不利于股票市场的恢复和恢复。数量信息的包含是否真的有助于预测股票价格的波动以及如何纳入交易所市场数量信息仍处于探索阶段。本文尝试从因果关系的角度根据直接和间接渠道确定交易所市场量的信息权重。相关的实践和结论需要检验和完善。先前的研究忽略了交易所市场数量所包含的信息对股票价格波动的影响。该研究在一定程度上对现有研究进行了有益的补充，特别是在研究问题，研究范式，研究方法和研究结论等方面。具有交易量信息的SV模型不仅可以有效地解决传统实践中交易量所包含的信息使用效率低下的问题，而且可以通过加权处理将交易所市场交易量信息引入模型中，从而进一步提高了模型的估计精度。对现有文献的有用补充。通过编程实现的SMC算法有助于非参数算法的进一步发展。本文为确定交易所市场交易量信息的权重做出了有益的尝试，并得出了一些有益的结论。通过加权处理将市场交易量信息引入模型，进一步提高了模型的估计精度，这是对现有文献的有益补充。通过编程实现的SMC算法有助于非参数算法的进一步发展。本文为确定交易所市场交易量信息的权重做出了有益的尝试，并得出了一些有益的结论。通过加权处理将市场交易量信息引入模型，进一步提高了模型的估计精度，这是对现有文献的有益补充。通过编程实现的SMC算法有助于非参数算法的进一步发展。本文为确定交易所市场交易量信息的权重做出了有益的尝试，并得出了一些有益的结论。