A0490
Title: Nonparametric method of changepoint detection in time series data
Authors: Soudeep Deb - Indian Institute of Management Bangalore (India) [presenting]
Abstract: Structural breaks refer to abrupt changes in the underlying characteristics of a stochastic process that generates a time series. These changes can occur in various aspects such as the mean, variance, or dependence structure of the process. The impact of a structural break can potentially alter the behavior of the entire series, hence detecting these breaks is extremely crucial in econometric modeling. We propose a non-parametric algorithm to detect structural breaks in the conditional mean and/or variance of a time series. Our method does not assume any specific parametric form for the dependence structure of the regressor, the time series model, or the distribution of the model noise. This flexibility allows our algorithm to be applicable to a wide range of time series structures that are commonly encountered in financial econometrics. The effectiveness of the proposed algorithm is validated through an extensive simulation study and a real data application of detecting structural breaks in the mean and volatility of Bitcoin returns. The algorithm's ability to identify structural breaks in the data highlights its practical utility in econometric analysis and financial modeling.
