A New Parameter Estimator for the Generalized Pareto Distribution under the Peaks over Threshold Framework

Xu Zhao, Zhongxian Zhang, Weihu Cheng and Pengyue Zhang
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Xu Zhao: College of Applied Sciences, Beijing University of Technology, Beijing 100124, China
Zhongxian Zhang: College of Applied Sciences, Beijing University of Technology, Beijing 100124, China
Weihu Cheng: College of Applied Sciences, Beijing University of Technology, Beijing 100124, China
Pengyue Zhang: Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA

Mathematics, 2019, vol. 7, issue 5, 1-18

Abstract: Techniques used to analyze exceedances over a high threshold are in great demand for research in economics, environmental science, and other fields. The generalized Pareto distribution (GPD) has been widely used to fit observations exceeding the tail threshold in the peaks over threshold (POT) framework. Parameter estimation and threshold selection are two critical issues for threshold-based GPD inference. In this work, we propose a new GPD-based estimation approach by combining the method of moments and likelihood moment techniques based on the least squares concept, in which the shape and scale parameters of the GPD can be simultaneously estimated. To analyze extreme data, the proposed approach estimates the parameters by minimizing the sum of squared deviations between the theoretical GPD function and its expectation. Additionally, we introduce a recently developed stopping rule to choose the suitable threshold above which the GPD asymptotically fits the exceedances. Simulation studies show that the proposed approach performs better or similar to existing approaches, in terms of bias and the mean square error, in estimating the shape parameter. In addition, the performance of three threshold selection procedures is assessed by estimating the value-at-risk (VaR) of the GPD. Finally, we illustrate the utilization of the proposed method by analyzing air pollution data. In this analysis, we also provide a detailed guide regarding threshold selection.

Keywords: generalized Pareto distribution; threshold; peaks over threshold; parameter estimation; extreme values (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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