Başçı, SıdıkaKhan, Asad ul IslamKhan, Asad ul IslamYönetim Bilimleri Fakültesi, İktisat Bölümü2024-04-192024-04-192023Başçı, S. ve Khan, Asad I. (2023). Detecting unknown change points for heteroskedastic data. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 24(2), 81-98. https://doi.org/10.24889/ifede.13009071303-0027https://doi.org/10.24889/ifede.1300907https://hdl.handle.net/20.500.12154/2810There are several tests to detect structural change at unknown change points. The Andrews Sup F test (1993) is the most powerful, but it requires the assumption of homoskedasticity. Ahmed et al. (2017) introduced the Sup MZ test, which relaxes this assumption and tests for changes in both the coefficients of regression and variance simultaneously. In this study, we propose a model update procedure that uses the Sup MZ test to detect structural changes at unknown change points. We apply this procedure to model the weekly returns of the Istanbul Stock Exchange's common stock index (BIST 100) for a 21-year period (2003-2023). Our model consists simply a mean plus noise, with occasional jumps in the level of mean or variance at unknown times. The goal is to detect these jumps and update the model accordingly. We also suggest a trading rule that uses the forecasts from our procedure and compare it to the buy-and-hold strategy.eninfo:eu-repo/semantics/openAccessStructual ChangeUnknwn Change PointsSup MZ TestIstanbul Stock ExchangeForecastArticle242819810.24889/ifede.13009071215993