论文标题
尾风保护：机器学习符合现代计量经济学
Tail-risk protection: Machine Learning meets modern Econometrics
论文作者
论文摘要
尾部风险保护是金融业的重点，需要坚实的数学和统计工具，尤其是在得出交易策略时。机器学习（ML）机制驱动的最新炒作提出了显示和理解ML工具功能的必要性。在本文中，我们提出了一种动态的尾巴风险保护策略，该策略针对通过风险的价值衡量的最大预定义风险，同时控制参与牛市的参与。我们提出了不同的弱分类器（参数和非参数），以估计我们得出交易信号以对冲尾巴事件的风险水平的超出概率。然后，我们将不同的方法与统计和交易策略绩效进行比较，最后我们提出了一个合奏分类器，该合奏分类器会产生元尾巴风险保护策略，以改善概括和交易绩效。
Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms has raised the necessity to display and understand the functionality of ML tools. In this paper, we present a dynamic tail risk protection strategy that targets a maximum predefined level of risk measured by Value-At-Risk while controlling for participation in bull market regimes. We propose different weak classifiers, parametric and non-parametric, that estimate the exceedance probability of the risk level from which we derive trading signals in order to hedge tail events. We then compare the different approaches both with statistical and trading strategy performance, finally we propose an ensemble classifier that produces a meta tail risk protection strategy improving both generalization and trading performance.
