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.

风险溢价的解释，从时间序列属性和交易资产的横截面角度来看，是金融经济学的核心，而基本资产定价方程清楚地说明了风险补偿的经济学。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施具有挑战性，并继续刺激金融领域的理论和计量经济学研究，人们可以区分两种策略。雇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，复杂的模型结构往往阻碍了标准计量经济学方法的使用。第二种策略包括在计量经济学上更容易获得的实证方法，但容易受到对没有理论的测量的批评以及对风险因素的无纪律的追求。法兰克福大学和蒂宾根大学的研究小组研究了两个新的框架来衡量风险更接近的溢价，这可以被视为基于理论和实证策略的极端案例。第一个是前瞻性的，因为它。利用反映在期权价格中的市场预期。它是基于理论的，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的假设的批评。学习方法，因此在某种意义上是向后看的，因为这些方法不是在历史数据中寻找模式，而是在数据科学中寻找模式。感兴趣的对象，即金融资产的有条件预期回报所反映的风险溢价，这一共同目标使得这两种方法上不相交的方法具有可比性，并且原则上可以组合。他们的条款预测绩效 - 回顾条件期望是均方误差最优预测 - 以及结合金融理论和数据科学方法的混合框架的开发和评估，因为缺乏对定量局限性的更深入理解。模型是最近金融危机的主要驱动因素之一，我们强调需要对基于期权和基于数据科学的框架以及将要开发的混合模型的可能性和局限性提供批判性观点。