Trend detection under erroneous observations: application to quantitative financial strategies

Guillaume Bernis and Simone Scotti

Journal of Investment Strategies

Abstract: ABSTRACT In this paper, we show how to handle the problem of trend detection, in the context of financial strategies, when the data is potentially erroneous. We focus on the case of a filtering method based on wavelets. This is used, for instance, to build an estimator of a given security at a future time horizon, or to construct trading signals based on extreme variations from the trend. We study how the erroneous observation of past data is incorporated into the filter method and, therefore, into the estimator built with it. The techniques of error calculus with Dirichlet forms are applied to see how the errors affect the estimation: they define an expansion of the estimator in terms of its first- and second-order moments, interpreted as statistical variance/covariance and bias.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-investment-strateg ... financial-strategies (text/html)

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:rsk:journ6:2410452

Access Statistics for this article

More articles in Journal of Investment Strategies from Journal of Investment Strategies
Bibliographic data for series maintained by Thomas Paine (maintainer@infopro-digital.com).