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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738481
• 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 naïve 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 S&P 500. Indices like the S&P 500 have performed well and studies have shown that most active managers that use market timing or stock picking do not outperform the indices like the S&P 500. However, market indices like the S&P 500 have some serious limitations due to their market capitalization weightings that have motivated alternative approaches. We will design portfolios to be reasonably close to a market index but limits the risk concentration of the assets by using risk parity. Expected return estimations 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.

拟议的研究将重点介绍用于金融投资设计的数学模型和算法的开发。我们考虑基于风险奇偶校验优化，索引跟踪和涵盖拨号覆盖的投资（资产分配）框架。风险奇偶校验是一种使用数学优化来创建投资决策的相对新方法，在这种决策中，来自单个资产的风险贡献相等，很容易导致多样化的投资组合，这与均值变化优化不同。但是，很少有人注意确定风险奇偶校验模型的鲁棒性。我们发现，幼稚的风险优化方法可以表现出对参数估计的敏感性。强大的风险平价优化公式的发展是我们研究的主要目标。我们将结合预期的收益和短售，以形成一般风险风险平价的一般模型。好处是，投资者将有能力在构建投资策略方面更好地折衷回报和风险均衡程度。投资组合索引是一种被动投资策略，旨在复制广泛的市场指数的风险和回报率，例如标准普尔500指数。诸如标准普尔500指数之类的指数表现良好，研究表明，大多数使用市场时机或股票挑选的活跃经理都不胜过像S​​＆P 500的市场限制，因为他们的市场限制了，而S＆P 500的限制了。替代方法。我们将设计投资组合以合理地接近市场指数，但通过使用风险平价限制了资产的风险集中。然后需要预期的回报估计，我们将通过考虑强大的优化来减轻估计错误。选项叠加层（例如覆盖的呼叫写作）已经出现了RAS，是增强投资组合回报率的有效方法。这是投资者将在其投资组合资产上出售呼叫期权的地方。该策略的一个限制是，与资产的整个位置相对应出售呼叫选项。另外，有盖的电话覆盖层还假定众所周知，投资组合可以出售呼叫选项。我们寻求开发优化模型来捕获广义的封面呼叫问题，在这些问题中，投资分配决策和销售销售都是在资产价格不确定性下完成的。作为一种特殊情况，将开发简单的风险均衡和涵盖的呼叫优化。拟议研究的结果将扩大财务优化领域，并在金融投资行业中具有重大的实际收益，这是加拿大经济的重要部门。 HQP在研究计划中的培训将使他们作为定量金融专业人员为加拿大充满活力的金融业提供实力的培训使他们从事行业。