Robust Risk Parity and Covered Call Optimization

稳健的风险平价和备兑看涨期权优化

• 批准号: RGPIN-2019-05733
• 负责人: Kwon, Roy
• 金额: $ 2.26万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715772
• 关键词: Robust; Risk; Parity; Covered; Call

The proposed research will focus on the development of mathematical models and algorithms for financial investment design. We consider investment (asset allocation) frameworks based on risk parity optimization, index tracking, and covered call overlays. Risk parity is a relatively new approach that uses mathematical optimization to create investment decisions where risk contributions from individual assets are equal which readily results in diversified portfolios unlike the use of mean-variance optimization. However, little attention has been given to determine how robust risk parity models are. We have found that nave approaches for risk parity optimization can exhibit considerable sensitivity to parameter estimations. The development of a robust formulation of risk parity optimization is a major goal of our research. We will incorporate expected returns and short selling to form general risk-return risk parity-based models. The benefit is that an investor will have the ability to better trade-off return and degree of risk parity in constructing investment strategies. Portfolio indexing is a passive investing strategy that aims to replicate the risk and return profile of a broad market index such as the Sstimations are then required, which we will mitigate estimation error by considering robust optimization. Option overlays such as covered call writing has emerged ras an effective method of enhancing returns of a portfolio. This is where an investor will sell call options on the assets of her portfolio. One limitation of this strategy is that call options are sold in correspondence to the entire position of an asset. Also, covered call overlays also assume that the investment portfolio is known prior to selling the call options. We seek to develop optimization models to capture a generalized cover call problem where both investment allocation decisions and covered call selling are done simultaneously under uncertainty in asset prices. As a special case, simultaneous risk parity and covered call optimization will be developed. The results from the proposed research will extend the field of financial optimization and have significant practical benefits in the financial investment industry which is an important sector of the Canadian economy. The training of HQP in the research program will prepare them to engage in industry as quantitative financial professionals adding strength to Canada's vibrant financial industry.

拟议的研究将侧重于开发金融投资设计的数学模型和算法。我们考虑基于风险平价优化、指数跟踪和备兑看涨期权覆盖的投资（资产配置）框架。风险平价是一种相对较新的方法，它使用数学优化来创建投资决策，其中各个资产的风险贡献相等，这很容易导致投资组合多样化，这与使用均值方差优化不同。然而，很少有人关注确定风险平价模型的稳健程度。我们发现，简单的风险平价优化方法对参数估计表现出相当大的敏感性。开发稳健的风险平价优化公式是我们研究的主要目标。我们将结合预期收益和卖空来形成基于风险平价的一般风险收益模型。这样做的好处是，投资者在制定投资策略时将能够更好地权衡回报和风险平价程度。 投资组合指数是一种被动投资策略，旨在复制广泛市场指数的风险和回报状况，例如需要 Sstimations，我们将通过考虑稳健优化来减轻估计误差。 期权覆盖（例如备兑看涨期权立权）已成为提高投资组合回报的有效方法。这是投资者出售其投资组合资产的看涨期权的地方。该策略的一个限制是，看涨期权的出售与资产的整个头寸相对应。此外，备兑看涨期权覆盖还假设投资组合在出售看涨期权之前是已知的。我们寻求开发优化模型来捕获广义的备兑看涨期权问题，其中投资分配决策和备兑看涨期权销售是在资产价格不确定的情况下同时进行的。作为一种特殊情况，将同时开发风险平价和备兑看涨期权优化。 拟议研究的结果将扩展金融优化的领域，并对作为加拿大经济重要部门的金融投资行业产生重大的实际效益。 HQP 在研究项目中的培训将使他们做好准备，成为量化金融专业人士，为加拿大充满活力的金融业增添力量。