Minimizing Tracking Error under Riskiness Measure

• Main Authors: LIOU, TE-CHI, 劉德騏
• Other Authors: Yan, Jen-Wei
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/d63zy4

碩士 === 東吳大學 === 經濟學系 === 107 === This paper develops the Riskiness-min tracking error framework, which Riskiness measure is proposed by Aumann and Serrano (2008). Since the Riskiness satisfies monotonicity with respect to stochastic dominance, Riskiness-min tracking error weights guarantees that there exists no other tracking error which second-order stochastic dominates the optimal tracking error. The paper provides the conditions of Riskiness-min tracking error weights which allows us to estimate the optimal weights by method-of-moment. The paper also compares our framework with variance-min tracking error framework proposed by Rolls (1992). The empirical results show that This means that Riskiness-min tracking error framework offers higher replication ability than variance-min tracking error framework when that managers ask smaller tracking error.