Outliers Detection, Correction of Financial Time-Series Anomalies and Distributional Timing for Robust Efficient Higher-Order Moment Asset Allocations

2009 ◽  
Author(s):  
Bertrand B. Maillet ◽  
Paul M. Merlin
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Higher Order ◽  
Order Moment ◽  
Financial Time ◽  
Outliers Detection ◽  
Asset Allocations

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.

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.

scholarly journals Application of Higher-Order Neural Networks to Financial Time-Series Prediction

2006 ◽  
pp. 80-108 ◽  
Author(s):  
John Fulcher ◽  
Ming Zhang ◽  
Shuxiang Xu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Higher Order ◽  
Financial Time ◽  
Polynomial Coefficients ◽  
Higher Order Neural Networks ◽  
Piecewise Continuous

Financial time-series data is characterized by nonlinearities, discontinuities, and high-frequency multipolynomial components. Not surprisingly, conventional artificial neural networks (ANNs) have difficulty in modeling such complex data. A more appropriate approach is to apply higher-order ANNs, which are capable of extracting higher-order polynomial coefficients in the data. Moreover, since there is a one-to-one correspondence between network weights and polynomial coefficients, higher-order neural networks (HONNs) — unlike ANNs generally — can be considered open-, rather than “closed-box” solutions, and thus hold more appeal to the financial community. After developing polynomial and trigonometric HONNs (P[T]HONNs), we introduce the concept of HONN groups. The latter incorporate piecewise continuous-activation functions and thresholds, and as a result are capable of modeling discontinuous (or piecewise-continuous) data, and what is more to any degree of accuracy. Several other PHONN variants are also described. The performance of P(T)HONN and HONN groups on representative financial time series is described (i.e., credit ratings and exchange rates). In short, HONNs offer roughly twice the performance of MLP/BP on financial time-series prediction, and HONN groups around 10% further improvement.

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