Distributed Estimator of Market Beta under Extreme Conditions

Distributed statistical inference, as a hot topic and an effective method, has been widely discussed in the past ten years, and a lot of research results have been accumulated. Its representative work are: In theory, Chen and Zhou (Chen and Zhou) [1] put forward the distributed Hill estimator and prove its Oracle property; Volgushev et al. (2017) [2] propose distributed inference for quantile regression processes, and propose a method to calculate the efficiency of this inference, which requires almost no additional computational cost. In application, Mohammed et al. (2020) [3] propose a technique to divide a Deep Neural Networks (DNN) in multiple partitions, which reduces the total latency for DNN inference; Smith and Hollinger (2018) [4] propose a distributed inference-based multi-robot exploration technique that uses the observed map structure to infer unobserved map features, resulting in a reduction in the cumulative exploration path length in the trial; Ye (2017) [5] started to study the stability of the beta coefficient of the Chinese stock market and found the best beta estimation time. Mitra (2019) [6] uses a smooth linear transfer function to measure the amplitude and direction of market movement, and the proposed classification can better capture the asymmetric behavior of beta.