英文标题：
《Forecasting dynamic return distributions based on ordered binary choice》
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作者：
Stanislav Anatolyev and Jozef Barunik
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最新提交年份：
2019
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英文摘要：
We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and past volatility proxy as predictors. Direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications where conditional distribution forecasts are desired.
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中文摘要：
我们提出了一种简单的方法来预测资产回报的条件概率分布。我们使用有序二元选择回归的简约规范，该规范将符号可预测性与不同的分位数联系起来。当使用过去指标和过去波动率代理作为预测因子时，该模型可以非常精确地预测未来的条件概率回报分布。通过对29只流动性最强的美国股票的实证应用，可以看出该模型的直接好处。预测概率分布在简单的交易策略中转化为显著的经济收益。我们的方法在需要条件分布预测的许多其他应用中也很有用。
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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：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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