英文标题：
《Efficient Computation of the Quasi Likelihood function for Discretely
Observed Diffusion Processes》
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作者：
Lars Josef H\\\"o\\\"ok and Erik Lindstr\\\"om
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最新提交年份：
2015
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英文摘要：
We introduce a simple method for nearly simultaneous computation of all moments needed for quasi maximum likelihood estimation of parameters in discretely observed stochastic differential equations commonly seen in finance. The method proposed in this papers is not restricted to any particular dynamics of the differential equation and is virtually insensitive to the sampling interval. The key contribution of the paper is that computational complexity is sublinear in the number of observations as we compute all moments through a single operation. Furthermore, that operation can be done offline. The simulations show that the method is unbiased for all practical purposes for any sampling design, including random sampling, and that the computational cost is comparable (actually faster for moderate and large data sets) to the simple, often severely biased, Euler-Maruyama approximation.
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中文摘要：
我们介绍了一种几乎同时计算金融中常见的离散观测随机微分方程参数拟极大似然估计所需的所有矩的简单方法。本文提出的方法不局限于微分方程的任何特定动力学，并且对采样间隔几乎不敏感。本文的主要贡献在于，当我们通过一次操作计算所有矩时，计算复杂度在观测数上是次线性的。此外，该操作可以脱机完成。模拟结果表明，该方法在任何抽样设计（包括随机抽样）的所有实际用途中都是无偏的，并且计算成本与简单的、通常带有严重偏差的Euler-Maruyama近似相当（对于中大型数据集，实际上更快）。
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分类信息：

一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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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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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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