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GO-GJRSK Model with Application to Higher Order Risk-Based Portfolio

Author

Listed:
  • Kei Nakagawa

    (NOMURA Asset Management Co. Ltd., 2-2-1, Toyosu, Koto-ku, Tokyo 135-0061, Japan)

  • Yusuke Uchiyama

    (MAZIN Inc., 3-29-14 Nishi-Asakusa, Tito city, Tokyo 111-0035, Japan)

Abstract

There are three distinguishing features in the financial time series, such as stock prices, are as follows: (1) Non-normality, (2) serial correlation, and (3) leverage effect. All three points need to be taken into account to model the financial time series. However, multivariate financial time series modeling involves a large number of stocks, with many parameters to be estimated. Therefore, there are few examples of multivariate financial time series modeling that explicitly deal with higher-order moments. Furthermore, there is no multivariate financial time series model that takes all three characteristics above into account. In this study, we propose the generalized orthogonal (GO)-Glosten, Jagannathan, and Runkle GARCH (GJR) model which extends the GO-generalized autoregressive conditional heteroscedasticity (GARCH) model and incorporates the three features of the financial time series. We confirm the effectiveness of the proposed model by comparing the performance of risk-based portfolios with higher-order moments. The results show that the performance with our proposed model is superior to that with baseline methods, and indicate that estimation methods are important in risk-based portfolios with higher moments.

Suggested Citation

  • Kei Nakagawa & Yusuke Uchiyama, 2020. "GO-GJRSK Model with Application to Higher Order Risk-Based Portfolio," Mathematics, MDPI, vol. 8(11), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1990-:d:441520
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    References listed on IDEAS

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    Cited by:

    1. Yusuke Uchiyama & Kei Nakagawa, 2022. "Schr\"{o}dinger Risk Diversification Portfolio," Papers 2202.09939, arXiv.org.

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