Estimation of risk premia from option data and using machine learning methods: comparison, forecast quality and potential of hybrid strategies

根据期权数据并使用机器学习方法估计风险溢价：混合策略的比较、预测质量和潜力

• 批准号: 440957921
• 负责人: Professor Dr. Joachim Grammig
• 金额: --
• 依托单位: Lehrstuhl für Derivate und Financial Engineering
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/440957921?language=en
• 关键词: Estimation; risk; premia; option; data; Estimation; risk; premia; option; data

The explanation of risk premia, in terms of their time series properties and in the cross-section of traded assets, is at the heart of financial economics. While the fundamental asset pricing equation makes a clear statement about the economics of risk compensation - it is the covariance of an asset's return and the stochastic discount factor that determines the risk premium - empirical implementations of this general concept are challenging and continue to spur theoretical and econometric research in finance. Within a vast and active literature one can distinguish two strategies. The first employs theory-based structural models for their empirical analysis, which has the advantage of being based on principled economic thought. However, the assessment of the empirical performance is often hampered by intricate model structures that preclude the use of standard econometric methods. Moreover, the models employed are sometimes highly stylized and rely on apparently unrealistic assumptions. The second strategy consists of empirical approaches that are econometrically more accessible, but are prone to the critique of measurement without theory and an undisciplined fishing for risk factors. Joining the forces of two finance research groups at the Universities of Frankfurt and Tübingen, this project takes a closer look at two novel frameworks to measure risk premia that can be conceived of as extreme cases of the theory-based and the empirical strategies. The first is forward-looking, because it exploits market expectations that are reflected in option prices. It is theory-based, because it relies on a reformulation of the fundamental asset pricing equation. The non-parametric nature of this strategy counters the critique of employing unrealistic assumptions. The second approach employs machine learning methods and is thus backward-looking in a sense that these methods look for patterns in historical data. One does not draw on economic theory, but concepts from data science. While their philosophies are fundamentally different, the two new frameworks are concerned with the same object of interest, namely the risk premium reflected in the conditional expected return of a financial asset. This common objective makes the two methodologically disjoint approaches comparable, and in principle combinable. Accordingly, our proposal aims at providing a comparative evaluation of the two frameworks in terms of their forecast performance - recalling that the conditional expectation is the mean-squared-error optimal forecast - and the development and assessment of hybrid frameworks that combine the financial theory- and data science-based approaches. Because a lack of deeper understanding of the limits of quantitative models was one main driver of the recent financial crises, we emphasize the need to provide a critical view on the possibilities and limitations of the option-based and the data science-based framework, as well as the hybrid models to be developed.

关于其时间序列属性和翻译资产的横截面，风险溢价的解释是金融经济学的核心。尽管基本资产定价方程式清楚地陈述了风险补偿的经济学，但它是资产回报的协方差，而随机折现因素决定了这一普遍概念的风险溢价 - 经验实施这一普遍概念的挑战，并继续刺激财务中的理论和经济研究。在庞大而活跃的文献中，可以区分两种策略。首批员工基于理论的结构模型，用于其经验分析，其优势是基于主要的经济思想。但是，对经验绩效的评估通常会受到排除使用标准经济方法的复杂模型结构的阻碍。此外，采用的模型有时是高度风格化的，并且依赖于明显不切实际的假设。第二种策略包括经济上更容易获得的经验方法，但易于没有理论和对危险因素的未纪律捕鱼的批判性。该项目加入了法兰克福大学和图宾根大学的两个金融研究小组的力量，仔细研究了两个新颖的框架，以衡量风险溢价，这些框架可以被认为是基于理论和经验策略的极端情况。第一个是前瞻性的，因为它探讨了期权价格反映的市场期望。它是基于理论的，因为它依赖于基本资产定价方程的改革。该策略的非参数性质反驳了采用不切实际的假设的批评。第二种方法采用了机器学习方法，因此从某种意义上说，这些方法在历史数据中寻找模式。一个不是借鉴经济理论，而是借鉴数据科学的概念。尽管他们的哲学在根本上是不同的，但两个新框架与感兴趣的对象有关，即在金融资产的有条件预期回报中反映的风险溢价。这个共同的目标使这两种方法上的两种不相交方法是可比较的，并且原则上结合在一起。彼此之间，我们的提案旨在根据其预测表现对两个框架进行比较评估 - 回顾条件期望是均值 - 错误的最佳预测 - 以及结合了金融理论和基于数据科学方法的混合框架的开发和评估。由于缺乏对定量模型限制的更深入的了解是最近金融危机的主要驱动力，因此我们强调有必要对基于选项的基于选项和基于数据科学的框架的可能性和局限性以及要开发的混合模型提供批判性观点。