《Risk management with machine-learning-based algorithms》
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作者:
Simon F\\\'ecamp, Joseph Mikael, Xavier Warin
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最新提交年份:
2020
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英文摘要:
We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets. Sources of incompleteness cover illiquidity, untradable risk factors, discrete hedging dates and transaction costs. The proposed algorithms resulting strategies are compared to classical stochastic control techniques on several payoffs using a variance criterion. One of the proposed algorithm is flexible enough to be used with several existing risk criteria. We furthermore propose a new moment-based risk criteria.
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中文摘要:
我们提出了一些基于机器学习的算法来解决不完全市场中的套期保值问题。不完全性的来源包括流动性不足、不可处理的风险因素、离散对冲日期和交易成本。利用方差准则,将所提出的算法生成的策略与经典的随机控制技术在多个收益上进行了比较。其中一种算法足够灵活,可以与多个现有的风险标准一起使用。我们进一步提出了一种新的基于矩的风险准则。
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分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Risk Management 风险管理
分类描述:Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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