英文标题：
《Non-Gaussian Stochastic Volatility Model with Jumps via Gibbs Sampler》
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作者：
Arthur T. Rego and Thiago R. dos Santos
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最新提交年份：
2018
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英文摘要：
In this work, we propose a model for estimating volatility from financial time series, extending the non-Gaussian family of space-state models with exact marginal likelihood proposed by Gamerman, Santos and Franco (2013). On the literature there are models focused on estimating financial assets risk, however, most of them rely on MCMC methods based on Metropolis algorithms, since full conditional posterior distributions are not known. We present an alternative model capable of estimating the volatility, in an automatic way, since all full conditional posterior distributions are known, and it is possible to obtain an exact sample of parameters via Gibbs Sampler. The incorporation of jumps in returns allows the model to capture speculative movements of the data, so that their influence does not propagate to volatility. We evaluate the performance of the algorithm using synthetic and real data time series. Keywords: Financial time series, Stochastic volatility, Gibbs Sampler, Dynamic linear models.
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中文摘要：
在这项工作中，我们提出了一个从金融时间序列估计波动率的模型，用Gamerman、Santos和Franco（2013）提出的精确边际似然扩展了非高斯空间状态模型族。文献中有一些模型侧重于估计金融资产风险，然而，由于不知道完全条件后验分布，大多数模型依赖于基于Metropolis算法的MCMC方法。我们提出了一种能够自动估计波动率的替代模型，因为所有的条件后验分布都是已知的，并且可以通过Gibbs采样器获得精确的参数样本。收益跳跃的合并允许模型捕捉数据的投机运动，从而使其影响不会传播到波动性。我们使用合成和实时数据时间序列评估了算法的性能。关键词：金融时间序列，随机波动率，吉布斯采样器，动态线性模型。
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分类信息：

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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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Statistics 统计学
二级分类：Other Statistics 其他统计数字
分类描述：Work in statistics that does not fit into the other stat classifications
从事不适合其他统计分类的统计工作
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