股票市场快速发展,股票价格波动性研究备受关注,准确预测股价走势对投资者决策和市场稳定意义重大。鉴于股票价格波动的不确定性与非线性特征,单一模型预测效果欠佳。为此,本文提出将GARCH与BP神经网络相结合的组合预测方法,以中国农业...股票市场快速发展,股票价格波动性研究备受关注,准确预测股价走势对投资者决策和市场稳定意义重大。鉴于股票价格波动的不确定性与非线性特征,单一模型预测效果欠佳。为此,本文提出将GARCH与BP神经网络相结合的组合预测方法,以中国农业银行股票日收盘价数据为例,基于误差修正思想构建组合模型,运用BP神经网络对GARCH模型的残差数据进行预测校正。研究结果表明组合模型预测效果优于单一模型,验证了该组合模型在提高股票价格预测准确度方面的有效性。With the rapid development of the stock market, the study of stock price volatility has attracted much attention, and accurate prediction of stock price movements is of great significance to investors’ decision-making and market stability. In view of the uncertainty and nonlinear characteristics of stock price volatility, the prediction effect of a single model is not good. For this reason, this paper proposes a combined prediction method combining GARCH and BP neural network, taking the daily closing price data of Agricultural Bank of China as an example, constructing a combined model based on the idea of error correction, and utilizing BP neural network to correct the residual data of the GARCH model for prediction. The results show that the combination model predicts better than a single model, which verifies the effectiveness of the combination model in improving the accuracy of stock price prediction.展开更多
本文以绿色债券和传统债券市场收益率的波动性为研究主题,首先通过构建GARCH模型来度量债券收益率的波动性,其次通过DCC-GARCH模型来研究分析传统债券市场收益率波动性与绿色债券市场收益率波动性之间的联动关系,结果发现两者之间存在...本文以绿色债券和传统债券市场收益率的波动性为研究主题,首先通过构建GARCH模型来度量债券收益率的波动性,其次通过DCC-GARCH模型来研究分析传统债券市场收益率波动性与绿色债券市场收益率波动性之间的联动关系,结果发现两者之间存在正向相关关系。政府等决策部门在制定政策时应关注传统债券市场与绿色债券市场间的联动关系,以促进市场的稳定和健康发展。推动绿色债券市场的信息披露透明化,有助于投资者更好地理解两类市场的风险和收益波动性。投资者应密切关注影响两类市场的宏观经济和市场因素,以便在波动性增加时迅速调整投资组合。This paper takes the volatility of green bond and traditional bond market as the research theme. Firstly, GARCH model is constructed to measure the volatility of bond yield. Secondly, DCC-GARCH model is used to study and analyze the linkage relationship between the volatility of traditional bond market and the volatility of green bond market. The results show that there is a positive correlation between the two. The government and other decision-making departments should pay attention to the linkage between the traditional bond market and the green bond market when formulating policies, so as to promote the stable and healthy development of the market. Promoting transparency in the green bond market will help investors better understand the risk and return volatility of both markets. Investors should pay close attention to macroeconomic and market factors affecting both types of markets so that they can quickly adjust their portfolios when volatility increases.展开更多
随着金融市场的迅速发展,基金行业市场规模不断增长,截至2024年底,公募基金管理总规模已突破32万亿元。准确有效测量基金的投资风险,对于基金市场的稳健发展以及基金投资者进行合理的资产配置具有重要作用。本文选取了安信价值精选股票(...随着金融市场的迅速发展,基金行业市场规模不断增长,截至2024年底,公募基金管理总规模已突破32万亿元。准确有效测量基金的投资风险,对于基金市场的稳健发展以及基金投资者进行合理的资产配置具有重要作用。本文选取了安信价值精选股票(000577.OF)、华夏中证500ETF联接A (001052.OF)、国泰安康定期支付混合A (000367.OF)等不同投资类型和规模的9支样本基金自2017年11月至2022年2月的单位净值数据,利用VaR值来衡量基金风险。根据基金收益率序列“尖峰后尾”、“波动聚集”的特性,建立了GARCH_VaR模型,在正态分布和可以调整尾部参数的t分布、GED分布三种假设条件下,分别计算出各只基金的日VaR值,并应用Kupiec失败率检法对计算出的VaR值的进行检验,据此对不同分布假设条件下的模型进行评价,结果显示不同类型的基金风险差异较大,GED分布假设条件下的模型VaR估计更为准确,更能准确反映基金风险。With the rapid development of the financial market, the market size of the fund industry continues to grow. As of the end of 2024, the total managed size of public funds has exceeded 32 trillion yuan. Accurately and effectively measuring the investment risk of funds plays an important role in the stable development of the fund market and the rational asset allocation of fund investors. This article selects the unit net asset value data of 9 sample funds with different investment types and scales, including Anxin Value Selected Stock (000577.OF), Huaxia CSI 500 ETF Connect A (001052.OF), and Guotai Ankang Regular Payment Hybrid A (000367.OF), from November 2017 to February 2022, and uses VaR value to measure fund risk. Based on the characteristics of “peak after tail” and “volatility aggregation” in fund return sequences, a GARCH_VaR model was established. Under three assumptions: normal distribution, t-distribution with adjustable tail parameters, and GED distribution, the daily VaR values of each fund were calculated, and the Kupec failure rate test was applied to test the calculated VaR values. Based on this, the models under different distribution assumptions were evaluated. The results showed that there were significant differences in risk among different types of funds, and the VaR estimation of the model under the GED distribution assumption was more accurate and could better reflect fund risk.展开更多
本文采用多元GARCH模型对资产配置中的风险管理进行了研究。研究旨在通过多元GARCH模型对不同资产间的波动率及其相关性进行分析,以此优化资产组合,达到分散风险、提高投资收益的目的。本文首先介绍了GARCH模型的基本原理和多元GARCH模...本文采用多元GARCH模型对资产配置中的风险管理进行了研究。研究旨在通过多元GARCH模型对不同资产间的波动率及其相关性进行分析,以此优化资产组合,达到分散风险、提高投资收益的目的。本文首先介绍了GARCH模型的基本原理和多元GARCH模型的扩展形式,接着利用实际市场数据进行实证分析,验证多元GARCH模型在资产配置中的有效性。结果表明,多元GARCH模型能够较好地捕捉资产间的动态相关性,对风险管理具有重要意义。最后,本文讨论了多元GARCH模型在实际应用中的优势和局限性,并提出了未来的研究方向。This paper investigates the application of the multivariate GARCH model in risk management for asset allocation. The study aims to optimize asset portfolios by analyzing the volatility and correlations between different assets using the multivariate GARCH model, thereby achieving risk diversification and improving investment returns. The paper begins with an introduction to the basic principles of the GARCH model and its multivariate extensions, followed by an empirical analysis using real market data to validate the effectiveness of the multivariate GARCH model in asset allocation. The results indicate that the multivariate GARCH model can effectively capture the dynamic correlations between assets, making it a valuable tool for risk management. Finally, the paper discusses the advantages and limitations of the multivariate GARCH model in practical applications and suggests future research directions.展开更多
文摘股票市场快速发展,股票价格波动性研究备受关注,准确预测股价走势对投资者决策和市场稳定意义重大。鉴于股票价格波动的不确定性与非线性特征,单一模型预测效果欠佳。为此,本文提出将GARCH与BP神经网络相结合的组合预测方法,以中国农业银行股票日收盘价数据为例,基于误差修正思想构建组合模型,运用BP神经网络对GARCH模型的残差数据进行预测校正。研究结果表明组合模型预测效果优于单一模型,验证了该组合模型在提高股票价格预测准确度方面的有效性。With the rapid development of the stock market, the study of stock price volatility has attracted much attention, and accurate prediction of stock price movements is of great significance to investors’ decision-making and market stability. In view of the uncertainty and nonlinear characteristics of stock price volatility, the prediction effect of a single model is not good. For this reason, this paper proposes a combined prediction method combining GARCH and BP neural network, taking the daily closing price data of Agricultural Bank of China as an example, constructing a combined model based on the idea of error correction, and utilizing BP neural network to correct the residual data of the GARCH model for prediction. The results show that the combination model predicts better than a single model, which verifies the effectiveness of the combination model in improving the accuracy of stock price prediction.
文摘本文以绿色债券和传统债券市场收益率的波动性为研究主题,首先通过构建GARCH模型来度量债券收益率的波动性,其次通过DCC-GARCH模型来研究分析传统债券市场收益率波动性与绿色债券市场收益率波动性之间的联动关系,结果发现两者之间存在正向相关关系。政府等决策部门在制定政策时应关注传统债券市场与绿色债券市场间的联动关系,以促进市场的稳定和健康发展。推动绿色债券市场的信息披露透明化,有助于投资者更好地理解两类市场的风险和收益波动性。投资者应密切关注影响两类市场的宏观经济和市场因素,以便在波动性增加时迅速调整投资组合。This paper takes the volatility of green bond and traditional bond market as the research theme. Firstly, GARCH model is constructed to measure the volatility of bond yield. Secondly, DCC-GARCH model is used to study and analyze the linkage relationship between the volatility of traditional bond market and the volatility of green bond market. The results show that there is a positive correlation between the two. The government and other decision-making departments should pay attention to the linkage between the traditional bond market and the green bond market when formulating policies, so as to promote the stable and healthy development of the market. Promoting transparency in the green bond market will help investors better understand the risk and return volatility of both markets. Investors should pay close attention to macroeconomic and market factors affecting both types of markets so that they can quickly adjust their portfolios when volatility increases.
文摘随着金融市场的迅速发展,基金行业市场规模不断增长,截至2024年底,公募基金管理总规模已突破32万亿元。准确有效测量基金的投资风险,对于基金市场的稳健发展以及基金投资者进行合理的资产配置具有重要作用。本文选取了安信价值精选股票(000577.OF)、华夏中证500ETF联接A (001052.OF)、国泰安康定期支付混合A (000367.OF)等不同投资类型和规模的9支样本基金自2017年11月至2022年2月的单位净值数据,利用VaR值来衡量基金风险。根据基金收益率序列“尖峰后尾”、“波动聚集”的特性,建立了GARCH_VaR模型,在正态分布和可以调整尾部参数的t分布、GED分布三种假设条件下,分别计算出各只基金的日VaR值,并应用Kupiec失败率检法对计算出的VaR值的进行检验,据此对不同分布假设条件下的模型进行评价,结果显示不同类型的基金风险差异较大,GED分布假设条件下的模型VaR估计更为准确,更能准确反映基金风险。With the rapid development of the financial market, the market size of the fund industry continues to grow. As of the end of 2024, the total managed size of public funds has exceeded 32 trillion yuan. Accurately and effectively measuring the investment risk of funds plays an important role in the stable development of the fund market and the rational asset allocation of fund investors. This article selects the unit net asset value data of 9 sample funds with different investment types and scales, including Anxin Value Selected Stock (000577.OF), Huaxia CSI 500 ETF Connect A (001052.OF), and Guotai Ankang Regular Payment Hybrid A (000367.OF), from November 2017 to February 2022, and uses VaR value to measure fund risk. Based on the characteristics of “peak after tail” and “volatility aggregation” in fund return sequences, a GARCH_VaR model was established. Under three assumptions: normal distribution, t-distribution with adjustable tail parameters, and GED distribution, the daily VaR values of each fund were calculated, and the Kupec failure rate test was applied to test the calculated VaR values. Based on this, the models under different distribution assumptions were evaluated. The results showed that there were significant differences in risk among different types of funds, and the VaR estimation of the model under the GED distribution assumption was more accurate and could better reflect fund risk.
文摘本文采用多元GARCH模型对资产配置中的风险管理进行了研究。研究旨在通过多元GARCH模型对不同资产间的波动率及其相关性进行分析,以此优化资产组合,达到分散风险、提高投资收益的目的。本文首先介绍了GARCH模型的基本原理和多元GARCH模型的扩展形式,接着利用实际市场数据进行实证分析,验证多元GARCH模型在资产配置中的有效性。结果表明,多元GARCH模型能够较好地捕捉资产间的动态相关性,对风险管理具有重要意义。最后,本文讨论了多元GARCH模型在实际应用中的优势和局限性,并提出了未来的研究方向。This paper investigates the application of the multivariate GARCH model in risk management for asset allocation. The study aims to optimize asset portfolios by analyzing the volatility and correlations between different assets using the multivariate GARCH model, thereby achieving risk diversification and improving investment returns. The paper begins with an introduction to the basic principles of the GARCH model and its multivariate extensions, followed by an empirical analysis using real market data to validate the effectiveness of the multivariate GARCH model in asset allocation. The results indicate that the multivariate GARCH model can effectively capture the dynamic correlations between assets, making it a valuable tool for risk management. Finally, the paper discusses the advantages and limitations of the multivariate GARCH model in practical applications and suggests future research directions.
