Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading

Yi-Ting Chen, Edward Sun and Min-Teh Yu
Additional contact information
Yi-Ting Chen: National Chiao Tung University
Min-Teh Yu: China University of Technology

Computational Economics, 2018, vol. 52, issue 2, No 16, 653-684

Abstract: Abstract Dynamic risk management requires the risk measures to adapt to information at different times, such that this dynamic framework takes into account the time consistency of risk measures interrelated at different times. Therefore, dynamic risk measures for processes can be identified as risk measures for random variables on an appropriate product space. This paper proposes a wavelet feature decomposing algorithm based on the discrete wavelet transform that optimally decomposes the time-consistent features from the product space. This approach allows us to generalize the multiple-stage risk measures of value at risk and conditional value at risk for the feature-decomposed processes, and implement them into portfolio selection using high-frequency data of U.S. DJIA stocks. The overall empirical results confirm that our proposed method significantly improves the performance of dynamic risk assessment and portfolio selection.

Keywords: Big financial data; Dynamic risk measures; Feature engineering; Portfolio optimization; Time consistency; Wavelet (search for similar items in EconPapers)
JEL-codes: C02 C10 C63 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9711-7 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:52:y:2018:i:2:d:10.1007_s10614-017-9711-7

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

DOI: 10.1007/s10614-017-9711-7

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 ().