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.
Highlights
The financial time series is continually brought to our attention
For both FF17 and FF25, HRP using the generalized orthogonal (GO)-GJR with skewness and kurtosis (GJRSK) model gives the best results in terms of annualized return (AR), deviation of negative return (DR), and return/downside risk (R/R) in RP and HRP portfolios
We proposed the GO-GJRSK model which extends the GO-generalized autoregressive conditional heteroscedasticity (GARCH) and incorporates the three features of a financial time series: (1) non-normality, (2) serial correlation, and (3) leverage effect
Summary
The financial time series is continually brought to our attention. Daily news reports inform us for instance of the latest stock market index values, currency exchange rates, oil prices, and interest rates.It is often desirable to monitor their price behaviors frequently and to try to understand the probable development of the prices in the future. The financial time series is continually brought to our attention. News reports inform us for instance of the latest stock market index values, currency exchange rates, oil prices, and interest rates. It is often desirable to monitor their price behaviors frequently and to try to understand the probable development of the prices in the future. Institutional and individual investors involved in international trades can all benefit from a deeper understanding of price behaviors. Modeling and understanding the financial time series is a very important task. There are three distinguishing features in the financial time series, such as stock prices, as follows:
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