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

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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.

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Notes

  1. Sun et al. (2009) provide a review of the adoption of VaR for measuring market risk.

  2. For DWT, we down-sample the coefficients, i.e., in each iteration we halve the number of detail coefficients.

  3. A basis point (bp) is one hundredth of a percent.

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Authors and Affiliations

  1. School of Computer Science, National Chiao Tung University, Hsinchu, Taiwan

    Yi-Ting Chen

  2. School of Business Informatics and Mathematics, University of Mannheim, Mannheim, Germany

    Yi-Ting Chen

  3. KEDGE Business School, 680 Cours de la Libèration, 33405, Talance Cedex, France

    Edward W. Sun

  4. Department of Finance, China University of Technology, Taipei, Taiwan

    Min-Teh Yu

Authors
  1. Yi-Ting Chen
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  2. Edward W. Sun
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  3. Min-Teh Yu
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Corresponding author

Correspondence to Edward W. Sun.

Additional information

Chen’s research was supported by the German Academic Exchange Service (DAAD) and CC-Tech Taiwan. The authors thank the participants of The 10th Annual Conference of the Asia-Pacific Association of Derivatives on August 21–22, 2014, Busan, South Korea for their valuable and insightful comments on earlier versions of the paper.

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Chen, YT., Sun, E.W. & Yu, MT. Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading. Comput Econ 52, 653–684 (2018). https://doi.org/10.1007/s10614-017-9711-7

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