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

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.

从风险溢价的时间序列属性和交易资产的横截面来解释风险溢价，是金融经济学的核心。 虽然基本的资产定价方程对风险补偿的经济学做出了明确的陈述-它是资产回报的协方差和决定风险溢价的随机贴现因子-这一一般概念的实证实现具有挑战性，并继续刺激金融理论和计量经济学研究。在大量活跃的文献中，可以区分出两种策略。第一种方法采用基于理论的结构模型进行实证分析，其优点是基于原则性的经济思想。然而，评估的经验表现往往是阻碍了复杂的模型结构，排除了使用标准的计量经济学方法。此外，所采用的模型有时是高度程式化的，依赖于显然不切实际的假设。第二种策略包括实证方法，这些方法在计量经济学上更容易获得，但容易受到对没有理论的测量的批评和对风险因素的无纪律捕捞。该项目与法兰克福大学和蒂宾根大学的两个金融研究小组合作，仔细研究了两个衡量风险溢价的新框架，这两个框架可以被视为基于理论和经验策略的极端情况。第一种是前瞻性的，因为它利用了反映在期权价格中的市场预期。它是以理论为基础的，因为它依赖于基本资产定价方程的重新表述。这种策略的非参数性质反驳了对采用不切实际假设的批评。第二种方法采用机器学习方法，因此在某种意义上是向后看的，这些方法在历史数据中寻找模式。人们并不借鉴经济理论，而是借鉴数据科学的概念。虽然这两个新框架的理念根本不同，但它们关注的是相同的利益对象，即金融资产的有条件预期回报所反映的风险溢价。这一共同的目标使得这两种方法论上互不相关的方法具有可比性，而且原则上是可以结合的。因此，我们的建议旨在提供两个框架在其预测性能方面的比较评估-回顾条件期望是均方误差最优预测-以及混合框架的开发和评估，该混合框架结合了联合收割机和基于数据科学的方法。由于对定量模型的局限性缺乏更深入的理解是最近金融危机的主要驱动因素之一，我们强调有必要对基于期权和基于数据科学的框架以及有待开发的混合模型的可能性和局限性提供批判性观点。