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摘要翻译：
在本文中，我们使用多种不同的计量经济学模型来预测加密货币的日收益。为了捕捉金融时间序列的显著特征，如条件方差的快速变化、测量误差的非正态性和急剧增加的趋势，我们提出了一种具有t分布测量误差和随机波动性的时变参数VAR。为了控制过参数化，我们依赖于收缩先验的贝叶斯文献，这使得我们能够以灵活的方式收缩与无关预测相关的系数和/或执行模型规范。利用大约一年的每日数据，我们进行实时预测练习，并研究是否任何提出的模型能够优于朴素随机游走基准。为了评估所提出的模型所产生的预测收益的经济相关性，我们还进行了一个简单的交易练习。
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英文标题：
《Predicting crypto-currencies using sparse non-Gaussian state space
models》
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作者：
Christian Hotz-Behofsits, Florian Huber and Thomas O. Z\"orner
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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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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英文摘要：
In this paper we forecast daily returns of crypto-currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non-normality of the measurement errors and sharply increasing trends, we develop a time-varying parameter VAR with t-distributed measurement errors and stochastic volatility. To control for overparameterization, we rely on the Bayesian literature on shrinkage priors that enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner. Using around one year of daily data we perform a real-time forecasting exercise and investigate whether any of the proposed models is able to outperform the naive random walk benchmark. To assess the economic relevance of the forecasting gains produced by the proposed models we moreover run a simple trading exercise.
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PDF链接：
https://arxiv.org/pdf/1801.06373
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