英文标题：
《Estimate nothing》
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作者：
M. Duembgen, L. C. G. Rogers
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最新提交年份：
2014
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英文摘要：
  In the econometrics of financial time series, it is customary to take some parametric model for the data, and then estimate the parameters from historical data. This approach suffers from several problems. Firstly, how is estimation error to be quantified, and then taken into account when making statements about the future behaviour of the observed time series? Secondly, decisions may be taken today committing to future actions over some quite long horizon, as in the trading of derivatives; if the model is re-estimated at some intermediate time, our earlier decisions would need to be revised - but the derivative has already been traded at the earlier price. Thirdly, the exact form of the parametric model to be used is generally taken as given at the outset; other competitor models might possibly work better in some circumstances, but the methodology does not allow them to be factored into the inference. What we propose here is a very simple (Bayesian) alternative approach to inference and action in financial econometrics which deals decisively with all these issues. The key feature is that nothing is being estimated.
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中文摘要：
在金融时间序列的计量经济学中，通常对数据采用一些参数模型，然后根据历史数据估计参数。这种方法存在几个问题。首先，如何量化估计误差，然后在陈述观测时间序列的未来行为时将其考虑在内？第二，今天可能会做出决定，承诺在相当长的时间内采取未来行动，比如在衍生品交易中；如果在某个中间时间重新估计模型，我们之前的决定将需要修改——但衍生工具已经以较早的价格进行了交易。第三，要使用的参数模型的确切形式通常是从一开始就给出的；在某些情况下，其他竞争对手的模型可能工作得更好，但该方法不允许将它们考虑到推理中。我们在这里提出的是一种非常简单（贝叶斯）的替代方法，用于金融计量经济学中的推理和行动，它决定性地处理了所有这些问题。关键的特点是没有任何估计。
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分类信息：

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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