Testing for the Long Memory and Multiple Structural Breaks in Consumer ETFs

Malinda, Maya, Jo-Hui and Chen

Journal of Applied Finance & Banking, 2022, vol. 12, issue 6, 6

Abstract: This research examines the consumer exchange-traded funds (ETFs) in several industries based on long memory and multiple structural breaks. The autoregressive fractionally integrated moving average (ARFIMA) model indicates that consumer ETF returns in the media, consumer service, food and beverage, and consumer goods industries can be accurately predicted. The autoregressive fractionally integrated moving average and fractionally integrated generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) model reveals that only the gaming and consumer goods industries have a long memory in volatility. This study establishes that through the iterated cumulative sum square test, multiple structural breaks exist in consumer ETF industries. Results prove that the consumer goods industry has a long memory and multiple structural breaks. Finally, the structural breaks in consumer ETFs have strong asymmetrical effects, indicating that all of the consumer ETF industries are generally unstable. Â

Keywords: The Long Memory; Multiple Structural Breaks; Consumer ETFs; Iterated Cumulative Sums Squares Test. (search for similar items in EconPapers)
Date: 2022
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