A Digital Signal Processing Approach to Moving Averages

Valeriy Zakamulin () and Javier Giner ()
Additional contact information
Valeriy Zakamulin: University of Agder, Norway
Javier Giner: University of La Laguna

Chapter Chapter 10 in The Ultimate Moving Average Handbook, 2025, pp 349-402 from Springer

Abstract: Abstract Many exotic moving averages are inspired by Digital Signal Processing (DSP) techniques. This chapter explores the connection between moving averages and digital filters, emphasizing that DSP filters are typically analyzed in the frequency domain rather than the time domain. The chapter applies DSP principles to examine the properties of conventional moving averages and the trend-following rules derived from them. It then introduces several DSP-inspired moving average variants and evaluates whether they exhibit unique characteristics absent in conventional or other exotic moving averages. The analysis reveals that DSP-inspired moving averages closely resemble so-called “zero-lag” moving averages, which aim to reduce lag while maintaining high smoothness. However, as with zero-lag variants, these filters achieve their reduced lag by incorporating negative weights, which ultimately come at the cost of lower accuracy. The findings suggest that while DSP principles offer valuable insights into moving average behavior, they do not necessarily lead to universally superior trend-following rules.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-90907-8_10

Ordering information: This item can be ordered from
http://www.springer.com/9783031909078

DOI: 10.1007/978-3-031-90907-8_10

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().