【作者】 胡莹；

【导师】 王安民；

【作者基本信息】 西安电子科技大学 ， 金融学， 2010， 硕士

【摘要】 风险是影响一切金融活动的基本要素。我国金融市场作为一个发展中的新兴市场,不仅仅是信用风险,市场风险等其他风险也必将随着金融市场的发展而逐渐加大。因此,金融风险管理方法研究对当前及未来我国金融创新以及投资机构进行投资决策均具有重要的意义。/支持向量机(Support Vector Machine,SVM)是在统计学习理论的基础上发展起来的一种新的机器学习方法,由于其完备的理论基础、出色的学习性能及预测性能而得到了广泛的应用。本文研究基于支持向量机的证券风险管理方法,主要的工作和取得的成果有:系统总结与回顾了证券市场投资风险度量方法;介绍了基于结构风险最小化原则的支持向量机理论与方法及SVM在经济学中的应用情况并研究了基于SVM的证券价格预测方法。以上海证券交易所综合指数为例的实证研究表明SVM模型能够很好的对股市波动进行建模。以华夏大盘精选基金为例的实证研究表明基于SVM的混沌时间序列预测可以较好捕捉市场运行趋势和识别市场异常波动,是一种优秀的风险预测与管理工具。针对统计学框架下传统VaR计算方法的不足,发展了基于加权支持向量机(W-SVM)的VaR计算新方法。对2001-2009年上证综指的实证研究表明,基于W-SVM的VaR模型优于传统的VaR方法,在小样本、厚尾、非线性及有异常波动的市场条件下,各种置信度下的W-SVM方法均能取得较好的性能。适合于各种风险偏好投资者采用。更多还原

【Abstract】 Risk is the basic factor that affects all financial activities. With the development of China’s financial markets, not only credit risk, but also market risk and other risks will gradually increase. Thus, financial risk management methods are very important to the present and future innovation and financial institutions investment decisions.Support Vector Machine (Support Vector Machine, SVM) is based on statistical learning theory, which is a new way for machine learning. Because of its sound theoretical foundation, excellent learning performance and projected performance, SVM has been widely used. In this paper, risk management method based on support vector machine is investigated. The main work and the results achieved are:Systematically review of stock market investment risk measurement methods; introduced support vector machine theory and methods based on the principle of structural risk minimization; introduced SVM application in the economics. SVM-based prediction method of securities price is studied. Empirical studies for the Shanghai Composite Index have shown that SVM model can model the stock market volatility very well. Empirical studies for the Huaxia foundation have shown that SVM-based prediction of chaotic time series can capture the market trends and identify market fluctuations well and it is an excellent risk prediction and management tools. According to the defects of the traditional VaR computation methods in the statistics framework, a new VaR model based on weighted support vector machine (W-SVM) was investigated. The Shanghai composite index from the year 2001 to 2009 was modeled and the simulation results indicated that the new VAR method based W-SVM is better than traditional methods. Even for small sample, abnormal fluctuations and heavy tails in nonlinear market, W-SVM model can obtain good performance at different confidence intervals. And it is suitable for different investor.更多还原