动态高阶矩风险稳健性测度与参数化组合投资决策研究  

Robust Measure of Dynamic Higher Moments Risk and Its Application to Parametric Portfolio Selection

作　　者：刘书婷 许启发[1,2] 蒋翠侠[1] LIU Shuting;XU Qifa;JIANG Cuixia(School of Management,Hefei University of Technology,Hefei 230009,China;Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China)

机构地区：[1]合肥工业大学管理学院,安徽合肥230009 [2]过程优化与智能决策教育部重点实验室,安徽合肥230009

出　　处：《运筹与管理》2023年第8期166-173,共8页Operations Research and Management Science

基　　金：国家社会科学基金一般项目(21BJY255)。

摘　　要：针对已有高阶矩组合投资模型中风险测度与模型求解的不足,本文构建动态高阶矩参数化组合投资决策模型(B-S-K)并给出其求解方案。首先,运用混频数据抽样分位数回归(MIDAS-QR)模型,充分挖掘高频数据信息,提高动态高阶矩风险测度的及时性、准确性和稳健性;其次,采用参数化组合投资策略,将资产特征变量、动态偏度风险和动态峰度风险纳入组合投资权重函数,大幅缩减待估计参数数目,提高模型求解效率。分别对中国股票市场的个股和行业板块指数进行实证,研究结果一致表明:第一,基于MIDAS-QR模型的动态高阶矩风险稳健性测度,不仅充分考虑了金融风险的时变特征,而且测度结果受异常值影响较小,是一个稳健且有效的测度方法;第二,市盈率、账面价值比、动态偏度风险与组合投资权重显著正相关,条件波动率、动态峰度风险与组合投资权重显著负相关,这些为组合投资决策提供了较好的机理性解释;第三,与等权方案、M-V模型、基准(B)模型和B-S模型等相比,本文构建的B-S-K模型,在收益、风险和风险调整收益等三个方面均表现出显著且稳定的优势。Skewness and kurtosis are frequently utilized to describe stylized facts within the financial community.However,their conventional moment-based measures exhibit a high degree of sensitivity to outliers.To improve the deficiency of risk measurement and model optimization in the existing higher order moment portfolio selection model,this paper develops a parametric portfolio model,referred to as the B-S-K model,which incorporates dynamic higher moments risk.Specifically,we first apply the mixed data sampling quantile regression(MIDAS-QR)model to improve the timeliness,accuracy and robustness of dynamic higher moments risk measure by exploiting rich information contained in high-frequency data.Second,we develop a parametric portfolio method with characteristic variables,dynamic skewness risk,and dynamic kurtosis risk for utility-maximizing investors with an exponential utility function.This parametric method with conditional kurtosis not only takes into account the investor attitude towards kurtosis,but also captures implicitly the relation between the stock characteristics and investor expected utility which has been ignored in most of the literature.Furthermore,our approach greatly reduces the number of parameters and improves the solution efficiency.In this case,we only need to estimate the coefficients of variables that enter the portfolio weights instead of the weight coefficients of each stock at each time point.Third,a three-step solution scheme is designed for parametric portfolio with dynamic skewness and kurtosis risks via the Broyden-Fletcher-Goldfarb-Shanno(BFGS)algorithm.In the first two steps,the coefficients of stock-specific characteristics and dynamic skewness risk are estimated separately.In the third step,we keep the coefficients in the first two step fixed at their values obtained,the remaining dynamic kurtosis risk coefficient is estimated by maximizing the investor’s expected utility.By comparing the estimation results of step 2 and step 3,the role of conditional kurtosis in allocating assets

关 键 词：高阶矩风险 组合投资 参数化策略 MIDAS-QR CARA效用函数 

分 类 号：F830.9[经济管理—金融学]