Research Article

DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA

Volume: 24 Number: 2 December 31, 2023
TR EN

DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA

Abstract

There 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.

Keywords

References

  1. Andrews, D. W. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica, 61(4), 821-856.
  2. Andrews, D. W., Lee, I & Ploberger W. (1996). Optimal Change Point Tests for Normal Linear Regression. Journal of Econometrics, 70, 9-38.
  3. Ahmed, M., Haider, G., & Zaman, A. (2017). Detecting structural change with heteroskedasticity. Communications in Statistics -Theory and Methods, 46(21), 10446-10455.
  4. Basci, E., Basci, S., & Zaman, A. (2000). A method for detecting structural breaks and an application to the Turkish stock market. METU Studies in Development, 27(1-2), 35-45.
  5. Bai, J. (1994). Least squares estimation of a shift in linear processes. Journal of Time Series Analysis, 15(5), 453-472.
  6. Boutahar, M. (2012). Testing for change in mean of independent multivariate observations with time varying covariance. Journal of Probability and Statistics, 2012, 1-17.
  7. Boutahar, M. (2018). Testing for change in mean of heteroskedastic time series. Cornell University, https://arxiv.org/abs/1102.5431.
  8. Chernoff, H., & Zacks, S. (1964). Estimating the current mean of a normal distribution which is subjected to changes in time. The Annals of Mathematical Statistics, 35(3), 999-1018.

Details

Primary Language

English

Subjects

Economics

Journal Section

Research Article

Publication Date

December 31, 2023

Submission Date

May 23, 2023

Acceptance Date

September 25, 2023

Published in Issue

Year 2023 Volume: 24 Number: 2

APA
Başçı, S., & Khan, A. U. 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.1300907
AMA
1.Başçı S, Khan AUI. DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 2023;24(2):81-98. doi:10.24889/ifede.1300907
Chicago
Başçı, Sıdıka, and Asad Ul Islam Khan. 2023. “DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 24 (2): 81-98. https://doi.org/10.24889/ifede.1300907.
EndNote
Başçı S, Khan AUI (December 1, 2023) DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 24 2 81–98.
IEEE
[1]S. Başçı and A. U. I. Khan, “DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA”, Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, vol. 24, no. 2, pp. 81–98, Dec. 2023, doi: 10.24889/ifede.1300907.
ISNAD
Başçı, Sıdıka - Khan, Asad Ul Islam. “DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 24/2 (December 1, 2023): 81-98. https://doi.org/10.24889/ifede.1300907.
JAMA
1.Başçı S, Khan AUI. DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 2023;24:81–98.
MLA
Başçı, Sıdıka, and Asad Ul Islam Khan. “DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, vol. 24, no. 2, Dec. 2023, pp. 81-98, doi:10.24889/ifede.1300907.
Vancouver
1.Sıdıka Başçı, Asad Ul Islam Khan. DETECTING UNKNOWN CHANGE POINTS FOR HETEROSKEDASTIC DATA. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 2023 Dec. 1;24(2):81-98. doi:10.24889/ifede.1300907

Cited By

Dokuz Eylül University Journal of Faculty of Business
is indexed and abstracted by TR-DİZİN, EBSCO and SOBIAD. 

Dokuz Eylül University Publisher's Web Page

Journal Contact Details