英文标题：
《A New Class of Discrete-time Stochastic Volatility Model with Correlated
  Errors》
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作者：
Sujay Mukhoti and Pritam Ranjan
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最新提交年份：
2017
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英文摘要：
  In an efficient stock market, the returns and their time-dependent volatility are often jointly modeled by stochastic volatility models (SVMs). Over the last few decades several SVMs have been proposed to adequately capture the defining features of the relationship between the return and its volatility. Among one of the earliest SVM, Taylor (1982) proposed a hierarchical model, where the current return is a function of the current latent volatility, which is further modeled as an auto-regressive process. In an attempt to make the SVMs more appropriate for complex realistic market behavior, a leverage parameter was introduced in the Taylor SVM, which however led to the violation of the efficient market hypothesis (EMH, a necessary mean-zero condition for the return distribution that prevents arbitrage possibilities). Subsequently, a host of alternative SVMs had been developed and are currently in use. In this paper, we propose mean-corrections for several generalizations of Taylor SVM that capture the complex market behavior as well as satisfy EMH. We also establish a few theoretical results to characterize the key desirable features of these models, and present comparison with other popular competitors. Furthermore, four real-life examples (Oil price, CITI bank stock price, Euro-USD rate, and S&P 500 index returns) have been used to demonstrate the performance of this new class of SVMs.
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中文摘要：
在一个有效的股票市场中，收益率及其随时间变化的波动率通常由随机波动率模型（SVM）联合建模。在过去几十年中，有人提出了几种支持向量机，以充分捕捉收益率与其波动率之间关系的定义特征。在最早的支持向量机中，Taylor（1982）提出了一种层次模型，其中当前收益率是当前潜在波动率的函数，进一步将其建模为自回归过程。为了使支持向量机更适合复杂的现实市场行为，在Taylor支持向量机中引入了杠杆参数，但这违反了有效市场假设（EMH，防止套利可能性的收益分布的必要平均零条件）。随后，开发了一系列替代支持向量机，目前正在使用。在本文中，我们对泰勒支持向量机的几种推广提出了均值修正，这些推广既能捕捉复杂的市场行为，又能满足有效市场假说。我们还建立了一些理论结果来描述这些模型的主要理想特征，并与其他流行的竞争对手进行了比较。此外，还使用了四个实际示例（油价、花旗银行股价、欧元兑美元汇率和标准普尔500指数收益率）来演示这类新型支持向量机的性能。
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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