| 金融时间序列指标判别框架：以特质波动率为例 |
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| 引用本文： | 汤胤,毛景慧.金融时间序列指标判别框架：以特质波动率为例[J].财经理论与实践,2016(3):35-39. |
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| 作者姓名： | 汤胤 毛景慧 |
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| 作者单位： | 暨南大学 管理学院,广东 广州,510632 |
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| 基金项目： | 中央高校基本科研业务费专项资金资助项目(15JNLH005)，广东省自然科学基金重点项目(2014A030311022) |
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| 摘 要： | 基于拐点集合判别的TBUD方法主要思路是分析拐点集合间的关系，并在高维空间进行划分，从而搭建判别模型，并将分析框架应用在特质波动率等若干指标上，利用实证数据得到结论。应用 TBUD判别框架可以发现，特质波动率等指标无法对拐点集合进行清晰划分，因而并不具有预测能力。
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| 关 键 词： | 特质波动率 支持向量机 贝叶斯判别 趋势预测 |
A Discrimination framework for Financial Time Series Indices based on Inflection Points Set: A Idiosyncratic Volatility Case |
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| TANG Yin,MAO Jinghui.A Discrimination framework for Financial Time Series Indices based on Inflection Points Set: A Idiosyncratic Volatility Case[J].The Theory and Practice of Finance and Economics,2016(3):35-39. |
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| Authors: | TANG Yin MAO Jinghui |
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| Institution: | (School of Management, Jinan University, Guangzhou, Guangdong510632, China) |
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| Abstract: | This paper presents a new method--TBUD to partition the inflection points into col-lections for time series of the stock price.Analyzing the relation among the collections,this paper builds a discrimination framework which is applied to Idiosyncratic Volatility,as a case.The re-sult suggests that Idiosyncratic Volatility can not be divided the inflection points set and is there-fore unable to make an accurate prediction on the future trends of the stock price. |
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| Keywords: | Idiosyncratic Volatility Support Vector Machine Bayesian Discrimination Trend forecasting |
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