Abstract Detrended Fluctuation Analysis has been used in several fields of science to study the statistical properties of trend stationary and nonstationary time-series. Its application to financial data has produced important results concerning long-range correlations and long-memory. However, these results may be contaminated if the researcher attributes to nonstationary trends the effect of stationary trends with endogenous structural breaks. Our paper proposes a modified DFA model where boxes to determine local trends are replaced by endogenous structural break windows. We also allow local trends fitted by quadratic functions and use squared residuals in place of patchy standard deviations to study the magnitude of the power-law exponent. The results show that our modified DFA model performs better than the fixed length alternatives originally proposed, and is, therefore, a suitable model to fit with financial data. Consistently with previous findings, our results show positive long-range correlation in all indices with the higher value for emerging markets.