{"title":"Optimal VPPI strategy under Omega ratio with stochastic benchmark","authors":"Guohui Guan, Lin He, Zongxia Liang, Litian Zhang","doi":"arxiv-2403.13388","DOIUrl":null,"url":null,"abstract":"This paper studies a variable proportion portfolio insurance (VPPI) strategy.\nThe objective is to determine the risk multiplier by maximizing the extended\nOmega ratio of the investor's cushion, using a binary stochastic benchmark.\nWhen the stock index declines, investors aim to maintain the minimum guarantee.\nConversely, when the stock index rises, investors seek to track some excess\nreturns. The optimization problem involves the combination of a non-concave\nobjective function with a stochastic benchmark, which is effectively solved\nbased on the stochastic version of concavification technique. We derive\nsemi-analytical solutions for the optimal risk multiplier, and the value\nfunctions are categorized into three distinct cases. Intriguingly, the\nclassification criteria are determined by the relationship between the optimal\nrisky multiplier in Zieling et al. (2014 and the value of 1. Simulation results\nconfirm the effectiveness of the VPPI strategy when applied to real market data\ncalibrations.","PeriodicalId":501487,"journal":{"name":"arXiv - QuantFin - Economics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2403.13388","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper studies a variable proportion portfolio insurance (VPPI) strategy.
The objective is to determine the risk multiplier by maximizing the extended
Omega ratio of the investor's cushion, using a binary stochastic benchmark.
When the stock index declines, investors aim to maintain the minimum guarantee.
Conversely, when the stock index rises, investors seek to track some excess
returns. The optimization problem involves the combination of a non-concave
objective function with a stochastic benchmark, which is effectively solved
based on the stochastic version of concavification technique. We derive
semi-analytical solutions for the optimal risk multiplier, and the value
functions are categorized into three distinct cases. Intriguingly, the
classification criteria are determined by the relationship between the optimal
risky multiplier in Zieling et al. (2014 and the value of 1. Simulation results
confirm the effectiveness of the VPPI strategy when applied to real market data
calibrations.