Early online detection of high volatility clusters using Particle Filters

作者：

Highlights：

• High volatility cluster detectors based on particle filters and hypothesis testing.

• Detection uses prior-posterior probability estimates in asymmetric hypothesis tests.

• Risk-sensitive particle filters used to track and detect greater financial risk.

• Scheme tested and validated using both simulated and actual IBM’s stock market data.

摘要

•High volatility cluster detectors based on particle filters and hypothesis testing.•Detection uses prior-posterior probability estimates in asymmetric hypothesis tests.•Risk-sensitive particle filters used to track and detect greater financial risk.•Scheme tested and validated using both simulated and actual IBM’s stock market data.

论文关键词：Bayesian inference,Risk-sensitive particle filters,Stochastic volatility estimation,Event detection

论文评审过程：Received 25 November 2014, Revised 27 January 2016, Accepted 28 January 2016, Available online 5 February 2016, Version of Record 21 February 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.01.052