Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data

Yi-Ting Chen, Wan-Ni Lai and Edward Sun
Additional contact information
Yi-Ting Chen: InfoTech
Wan-Ni Lai: Université Côte d’Azur

Computational Economics, 2019, vol. 54, issue 2, No 14, 809-844

Abstract: Abstract High-frequency data is a big data in finance in which a large amount of intra-day transactions arriving irregularly in financial markets are recorded. Given the high frequency and irregularity, such data require efficient tools to filter out the noise (i.e. jumps) arising from the anomaly, irregularity, and heterogeneity of financial markets. In this article, we use a recurrently adaptive separation algorithm, which is based on the maximal overlap discrete wavelet transform (MODWT) and that can effectively: (1) identify the time-variant jumps, (2) extract the time-consistent patterns from the noise (jumps), and (3) denoise the marginal perturbations. In addition, the proposed algorithm enables reinforcement learning to optimize a multiple-criteria decision or convex programming when reconstructing the wavelet-denoised data. Using simulated data, we show the proposed approach can perform efficiently in comparison with other conventional methods documented in the literature. We also apply our method in an empirical study by using high-frequency data from the US stock market and confirm that the proposed method can significantly improve the accuracy of predictive analytics models for financial market returns.

Keywords: Convex optimization; Forecasting; Jump detection; High-frequency data; Reinforcement learning; Wavelet (search for similar items in EconPapers)
JEL-codes: C02 C10 C63 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-019-09881-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:54:y:2019:i:2:d:10.1007_s10614-019-09881-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-019-09881-3

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().