Matching a Distribution by Matching Quantiles Estimation

作者：Sgouropoulos Nikolaos^*; Yao Qiwei; Yastremiz Claudia

来源：Journal of the American Statistical Association, 2015, 110(510): 742-759.

DOI：10.1080/01621459.2014.929522

• Goodness-of-match
• LASSO
• Ordinary least-squares estimation
• Portfolio tracking
• Representative portfolio
• Sample quantile

Motivated by the problem of selecting representative portfolios for backtesting counterparty credit risks, we propose a matching quantiles estimation (MQE) method for matching a target distribution by that of a linear combination of a set of random variables. An iterative procedure based on the ordinary least-squares estimation (OLS) is proposed to compute MQE. MQE can be easily modified by adding a LASSO penalty term if a sparse representation is desired, or by restricting the matching within certain range of quantiles to match a part of the target distribution. The convergence of the algorithm and the asymptotic properties of the estimation, both with or without LASSO, are established. A measure and an associated statistical test are proposed to assess the goodness-of-match. The finite sample properties are illustrated by simulation. An application in selecting a counterparty representative portfolio with a real dataset is reported. The proposed MQE also finds applications in portfolio tracking, which demonstrates the usefulness of combining MQE with LASSO.

• 出版日期2015-6
• 单位北京大学