Nonparametric tests for jump detection via false discovery rate control: a Monte Carlo study

Kaiqiao Li, Kan He, Lizhou Nie, Wei Zhu and Pei Fen Kuan

Journal of Risk Model Validation

Abstract: Nonparametric tests are popular and efficient methods of detecting jumps in high- frequency financial data. Each method has its own advantages and disadvantages, and their performances may be affected by underlying noise and dynamic structures. To address this, we proposed a robust p-value pooling method that aims to combine the advantages of each method. We focus on model validation within a Monte Carlo framework to assess the reproducibility and false discovery rate (FDR). Reproducible analyses via a correspondence curve and an irreproducible discovery rate were analyzed with replicates to study local dependency and robustness across replicates. Extensive simulation studies of high-frequency trading data at a minute level were carried out, and the operating characteristics of these methods were compared via the FDR control framework. Our proposed method was robust across all scenarios under reproducibility and FDR analysis. Finally, we applied this method to minute-level data from the limit order book system: the efficient reconstruction system (LOBSTER). An R package JumpTest implementing these methods has been made available on the Comprehensive R Archive Network.

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