A0399
Title: Sequential monitoring for change points of M-estimators in risk models
Authors: Xiaohan Xue - University of Bath (United Kingdom) [presenting]
Abstract: A new real-time detection method is proposed for change points of M-estimators in a risk model, based on a previous study. The proposed test efficiently captures change points in several risk measure series: volatility, expectile, Value-at-Risk (VaR), and Expected Shortfall (ES). We derive the asymptotic distribution of the proposed statistic. Monte Carlo simulation results show that our proposed test has better size control and higher power under various change point scenarios. The empirical studies of risk measures based on the S\&P 500 index and GBP/EUR exchange rate illustrate that our proposed test is able to detect change points that are consistent with well-known market events.
