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

Kei Nakagawa and Yusuke Uchiyama
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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

Mathematics, 2020, vol. 8, issue 11, 1-12

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.

Keywords: GO-GJRSK model; risk-based portfolio; higher order moment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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