The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform
Robert D St Louis - One of the best experts on this subject based on the ideXlab platform.
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automatic arma identification using neural networks and the extended Sample Autocorrelation Function a reevaluation
Decision Support Systems, 2000Co-Authors: Tim Chenoweth, Robert Hubata, Robert D St LouisAbstract:Abstract Recently, several researchers have attempted to use neural network approaches in conjunction with the extended Sample Autocorrelation Function (ESACF) to automatically identify ARMA models. The work to date appears promising, but generalizations are limited by the fact that the test and training sets for the neural networks were generated from random perturbations of prototype ESACF tables. This paper develops test and training sets by varying the parameters of actual ARMA processes. The results show that the ability of neural networks to accurately identify the order of an ARMA(p,q) model from its transformed ESACF is much lower than reported by previous researchers, and is especially low for time series with fewer than 100 observations.
Antony Gichuhi Waititu - One of the best experts on this subject based on the ideXlab platform.
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consistency of the model order change point estimator for garch models
Journal of Mathematical Finance, 2018Co-Authors: Irene W Irungu, Peter N Mwita, Antony Gichuhi WaitituAbstract:GARCH models have been commonly used to capture volatility dynamics in financial time series. A key assumption utilized is that the series is stationary as this allows for model identifiability. This however violates the volatility clustering property exhibited by financial returns series. Existing methods attribute this phenomenon to parameter change. However, the assumption of fixed model order is too restrictive for long time series. This paper proposes a change-point estimator based on Manhattan distance. The estimator is applicable to GARCH model order change-point detection. Procedures are based on the Sample Autocorrelation Function of squared series. The asymptotic consistency of the estimator is proven theoretically.
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limit theory of model order change point estimator for garch models
Journal of Mathematical Finance, 2018Co-Authors: Irene W Irungu, Peter N Mwita, Antony Gichuhi WaitituAbstract:The limit theory of a change-point process which is based on the Manhattan distance of the Sample Autocorrelation Function with applications to GARCH processes is examined. The general theory of the Sample autocovariance and Sample Autocorrelation Functions of a stationary GARCH process forms the basis of this study. Specifically the point processes theory is utilized to obtain their weak convergence limit at different lags. This is further extended to the change-point process. The limits are found to be generally random as a result of the infinite variance.
Joachim Werner - One of the best experts on this subject based on the ideXlab platform.
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model identification of integrated arma processes
Multivariate Behavioral Research, 2008Co-Authors: Tetiana Stadnytska, Simone Braun, Joachim WernerAbstract:This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can be applied to either nontransformed or differenced series, so the advantages and drawbacks of both procedures were compared. The best results were 79% of correct identifications for SCAN and 80% for ESACF. For some models and parameterizations, the accuracy of SCAN and ESACF was disappointing. The key finding of the study is that both human experts and automated methods provide inconsistent model identifications. Hence an elaborated strategy for model selection combining different techniques was developed and demonstrated on 2 empirical examples.
Tim Chenoweth - One of the best experts on this subject based on the ideXlab platform.
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automatic arma identification using neural networks and the extended Sample Autocorrelation Function a reevaluation
Decision Support Systems, 2000Co-Authors: Tim Chenoweth, Robert Hubata, Robert D St LouisAbstract:Abstract Recently, several researchers have attempted to use neural network approaches in conjunction with the extended Sample Autocorrelation Function (ESACF) to automatically identify ARMA models. The work to date appears promising, but generalizations are limited by the fact that the test and training sets for the neural networks were generated from random perturbations of prototype ESACF tables. This paper develops test and training sets by varying the parameters of actual ARMA processes. The results show that the ability of neural networks to accurately identify the order of an ARMA(p,q) model from its transformed ESACF is much lower than reported by previous researchers, and is especially low for time series with fewer than 100 observations.
Irene W Irungu - One of the best experts on this subject based on the ideXlab platform.
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consistency of the model order change point estimator for garch models
Journal of Mathematical Finance, 2018Co-Authors: Irene W Irungu, Peter N Mwita, Antony Gichuhi WaitituAbstract:GARCH models have been commonly used to capture volatility dynamics in financial time series. A key assumption utilized is that the series is stationary as this allows for model identifiability. This however violates the volatility clustering property exhibited by financial returns series. Existing methods attribute this phenomenon to parameter change. However, the assumption of fixed model order is too restrictive for long time series. This paper proposes a change-point estimator based on Manhattan distance. The estimator is applicable to GARCH model order change-point detection. Procedures are based on the Sample Autocorrelation Function of squared series. The asymptotic consistency of the estimator is proven theoretically.
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limit theory of model order change point estimator for garch models
Journal of Mathematical Finance, 2018Co-Authors: Irene W Irungu, Peter N Mwita, Antony Gichuhi WaitituAbstract:The limit theory of a change-point process which is based on the Manhattan distance of the Sample Autocorrelation Function with applications to GARCH processes is examined. The general theory of the Sample autocovariance and Sample Autocorrelation Functions of a stationary GARCH process forms the basis of this study. Specifically the point processes theory is utilized to obtain their weak convergence limit at different lags. This is further extended to the change-point process. The limits are found to be generally random as a result of the infinite variance.
