尾部风险保护：机器学习与现代计量经济学的结合
Tail-risk protection: Machine Learning meets modern Econometrics
摘要：
尾部风险保护是金融行业的焦点，需要坚实的数学和统计工具，特别是在推导交易策略时。最近由机器学习（ML）机制驱动的炒作提高了显示和理解ML工具功能的必要性。在本文中，我们提出了一个动态的尾部风险保护策略，目标是最大的预定义水平的风险衡量的价值在风险，同时控制参与牛市制度。我们提出了不同的弱分类，参数和非参数，估计的风险水平，我们从中获得交易信号，以对冲尾部事件的发生概率。然后，我们比较了不同的方法与统计和交易策略的性能，最后，我们提出了一个集成分类器，产生一个Meta尾部风险保护策略，提高泛化和交易性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
