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

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1990
Author(s):  
Kei Nakagawa ◽  
Yusuke Uchiyama
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Financial Time Series ◽  
Higher Order ◽  
Estimation Methods ◽  
Time Series Modeling ◽  
Higher Order Moments ◽  
Financial Time ◽  
Proposed Model ◽  
Multivariate Financial Time Series

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.

Inference on the tail process with application to financial time series modeling

2018 ◽  
Vol 205 (2) ◽  
pp. 508-525 ◽  
Author(s):  
Richard A. Davis ◽  
Holger Drees ◽  
Johan Segers ◽  
Michał Warchoł
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Time Series Modeling ◽  
Financial Time

Monitoring the mean of multivariate financial time series

2013 ◽  
Vol 30 (3) ◽  
pp. 328-340 ◽  
Author(s):  
Robert Garthoff ◽  
Vasyl Golosnoy ◽  
Wolfgang Schmid
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Financial Time ◽  
The Mean ◽  
Multivariate Financial Time Series

Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction

2009 ◽  
pp. 212-249 ◽  
Author(s):  
David R. Selviah ◽  
Janti Shawash
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Linear Regression ◽  
Input Data ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Higher Order ◽  
Order Linear ◽  
Financial Time ◽  
Generalized Correlation

Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks, conventional higher order neural network designs, and higher order linear regression networks for financial time series prediction. The correlation higher order neural network design is shown to give the highest accuracy for prediction of stock market share prices and share indices. The simulations compare the performance for three different training algorithms, stationary versus non-stationary input data, different numbers of neurons in the hidden layer and several generalized correlation higher order neural network designs. Generalized correlation higher order linear regression networks are also introduced and two designs are shown by simulation to give good correct direction prediction and higher prediction accuracies, particularly for long-term predictions, than other linear regression networks for the prediction of inter-bank lending risk Libor and Swap interest rate yield curves. The simulations compare the performance for different input data sample lag lengths.

Robert Engle and Clive Granger: New Spheres of Economic Research (Nobel Prize in Economics 2003)

2004 ◽  
pp. 37-48
Author(s):  
G. Kantorovich ◽  
M. Touruntseva
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Nobel Prize ◽  
Financial Time Series ◽  
Market Analysis ◽  
Time Series Model ◽  
Economic Research ◽  
Estimation Methods ◽  
Financial Time ◽  
Financial Market Analysis

This paper is dedicated to the achievements of Robert Engle and Clive Granger which allowed to overcome a serious crisis in macroeconomics and financial market analysis. The main concepts of cointegration theory and different estimation methods of cointegration equations are considered in the first part of the paper. The areas of application of cointegration theory and possible extensions are briefly described as well. The financial time series model with conditional heteroskedastisity is analyzed in the second part of the paper. The main prerequisites of the method suggested by R. Engle are formulated and its extensions and areas of application are defined.

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