基于动态Copula函数的融资融券保证金计算的研究

发布时间：2018-06-06 21:11

本文选题：融资融券 + VaR　；参考：《南京理工大学》2013年硕士论文

【摘要】：本文在选取融资融券的标的证券基础上,讨论了融资融券的保证金设置问题。保证金和担保比例的设定是为了控制金融资产的风险,避免投资交易者的损失。文章首先讨论了保证金设置时常用的风险控制模型,在对比了风险价格系数法、EWMA方法和VaR方法后选择了VaR作为本文保证金设置的方法。大量的实证研究表明VaR计算的核心是波动率,同时资产波动也是风险产生的主要原因。本文考虑了单个资产波动的SV模型、极值模型和GARCH模型,选择了GARCH模型来拟合单个标的资产的波动,其中残差项分布分别满足正态分布、T分布和ST分布。然后文章引入了Copula连接函数来描述多个标的资产的波动率,并且运用了DCC-MVGARCH的方法将Copula函数模型时变化。基于上述理论,在实证模型中首先运用GARCH模型来拟合中国石化和工商银行的波动,并选取了Normal-Copula和T-Copula分别讨论多个资产的波动率。我们运用了失败率检验的方法最终选取T-Copula时变函数模型作为本文最终的实证模型,计算了两个标的资产的VaR并运用到了融资融券的保证金设置问题上
[Abstract]:On the basis of selecting the underlying securities of margin, this paper discusses the issue of margin setting. Margin and guarantee ratio is designed to control the risk of financial assets and avoid the loss of investment traders. This paper first discusses the risk control model commonly used in margin setting. After comparing EWMA method with VaR method, a lot of empirical studies show that volatility is the core of VaR calculation, and asset volatility is the main reason of risk. In this paper, the SV model, extreme value model and GARCH model of single asset volatility are considered, and the GARCH model is selected to fit the volatility of a single underlying asset, where the residual term distribution satisfies the normal distribution T distribution and St distribution respectively. Then the Copula connection function is introduced to describe the volatility of multiple underlying assets, and the method of DCC-MVGARCH is used to change the Copula function model. In the empirical model, the GARCH model is first used to fit the volatility of Sinopec and ICBC, and Normal-Copula and T-Copula are selected to discuss the volatility of multiple assets respectively. We use the method of failure rate test to select T-Copula time-varying function model as the final empirical model of this paper, calculate the VaR of the two underlying assets and apply it to the margin setting problem of margin.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F224;F830.91

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