论文标题
危险的编码价值：财务风险管理的预测机
Encoded Value-at-Risk: A Predictive Machine for Financial Risk Management
论文作者
论文摘要
衡量风险是现代金融风险管理的中心。随着世界经济变得越来越复杂，违反了标准建模假设，先进的人工智能解决方案可能会为分析全球市场提供正确的工具。在本文中，我们提供了一种新颖的方法，用于测量称为编码价值（编码VAR）的市场风险，该方法基于一种人工神经网络，称为人工神经网络，称为变异自动编码器（VAE）。编码的VAR是一种生成模型，可用于从一系列历史横断面股票收益中重现市场情况，同时增加财务数据中存在的信噪比，并学习市场的依赖关系结构，而没有任何关于股票回报共同分配的假设。我们将编码的VAR除外结果与其他11种方法进行了比较，并表明它与文献中介绍的许多其他众所周知的VAR算法具有竞争力。
Measuring risk is at the center of modern financial risk management. As the world economy is becoming more complex and standard modeling assumptions are violated, the advanced artificial intelligence solutions may provide the right tools to analyze the global market. In this paper, we provide a novel approach for measuring market risk called Encoded Value-at-Risk (Encoded VaR), which is based on a type of artificial neural network, called Variational Auto-encoders (VAEs). Encoded VaR is a generative model which can be used to reproduce market scenarios from a range of historical cross-sectional stock returns, while increasing the signal-to-noise ratio present in the financial data, and learning the dependency structure of the market without any assumptions about the joint distribution of stock returns. We compare Encoded VaR out-of-sample results with eleven other methods and show that it is competitive to many other well-known VaR algorithms presented in the literature.
