Multivariate copula-based conditional quantiles: analytic higher-order moments and ratio estimation approaches

【Author】 Hakim, Arief; Salman, A. N. M.; Syuhada, Khreshna

【Source】JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION

【影响因子】1.225

【Abstract】In this paper, we aim to modify multivariate copula-based conditional quantiles for a targeted random variable, given multiple random variables attaining their respective quantiles. Specifically, we propose two modifications through the Cornish-Fisher expansion, one of which involves its analytic higher-order unconditional moments. The second one accounts for its higher-order conditional moments estimated using a ratio estimation method. By considering multivariate elliptical and Archimedean copulas with Johnson's SU margins, our simulation study shows that this method provides more accurate conditional moment estimators compared to the naive method. They result in expanded conditional quantile estimators, whose efficiency is relatively better than their unexpanded versions, particularly at lower and higher quantile levels under a stronger (tail) dependence assumption. A much higher efficiency is gained when the first modification is performed. These findings are validated using an empirical study based on cryptocurrency return data and a comparative analysis against the existing estimation approaches.

【Keywords】Conditional quantile; conditional coverage probability; conditional expectation; Cornish-Fisher expansion; efficient simulation; relative efficiency; elliptical copula; Archimedean copula; tail dependence

【发表时间】2024 2024 OCT 10

【收录时间】2024-10-19

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-其他