英文标题：
《Semiparametric stochastic volatility modelling using penalized splines》
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作者：
Roland Langrock, Th\\\'eo Michelot, Alexander Sohn, Thomas Kneib
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最新提交年份：
2014
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英文摘要：
  Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscript, we introduce a novel maximum penalized likelihood approach for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape. The considered framework exploits the strengths both of the powerful hidden Markov model machinery and of penalized B-splines, and constitutes a powerful and flexible alternative to recently developed Bayesian approaches to semiparametric SV modelling. We demonstrate the feasibility of the approach in a simulation study before outlining its potential in applications to three series of returns on stocks and one series of stock index returns.
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中文摘要：
随机波动率（SV）模型模拟了许多归因于资产收益时间序列的程式化事实，同时保持了概念上的简单性。然而，考虑到波动性，通常假设的条件正态分布或Student-t分布回报率受到了质疑。在这篇手稿中，我们介绍了一种新的最大惩罚似然方法，用于以非参数方式估计SV模型中的条件分布，从而避免了对形状的任何潜在关键假设。所考虑的框架利用了强大的隐马尔可夫模型机制和惩罚B样条的优点，构成了一种强大而灵活的半参数SV建模贝叶斯方法的替代方案。我们在模拟研究中证明了该方法的可行性，然后概述了其在三个股票收益率系列和一个股票指数收益率系列中的应用潜力。
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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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