Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique

Previous studies revealed that Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) outperformed Vector Autoregression (VAR) when data exhibit heteroscedasticity. However, EGARCH estimation is not efficient when the data have leverage effect. Therefore, in this study the weaknesses of VAR and EGARCH were modelled using Combine White Noise (CWN). The CWN model was developed by integrating the white noise of VAR with EGARCH using Bayesian Model Averaging (BMA) for the improvement of VAR estimation. First, the standardized residuals of EGARCH errors (heteroscedastic variance) were decomposed into equal variances and defined as white noise series. Next, this series was transformed into CWN model through BMA. The CWN was validated using comparison study based on simulation and four countries real data sets of Gross Domestic Product (GDP). The data were simulated by incorporating three sample sizes with low, moderate and high values of leverages and skewness. The CWN model was compared with three existing models (VAR, EGARCH and Moving Average (MA)). Standard error, log-likelihood, information criteria and forecast error measures were used to evaluate the performance of the models. The simulation findings showed that CWN outperformed the three models when using sample size of 200 with high leverage and moderate skewness. Similar results were obtained for the real data sets where CWN outperformed the three models with high leverage and moderate skewness using France GDP. The CWN also outperformed the three models when using the other three countries GDP data sets. The CWN was the most accurate model of about 70 percent as compared with VAR, EGARCH and MA models. These simulated and real data findings indicate that CWN are more accurate and provide better alternative to model heteroscedastic data with leverage effect

Modeling the asymmetric in conditional variance

The purpose of this study is to model the asymmetric in conditional variance of Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) with Combine White Noise (CWN) model to obtain suitable results. Combine white noise has the minimum information criteria and high log likelihood when compare with EGARCH estimation.The determinant of the residual covariance matrixvalue indicates that CWN estimation is efficient. Combine white noise has minimum information criteria and high log likelihood value that signify suitable estimation. Combine white noise has a minimum forecast errors which indicates forecast accuracy.Combine white noise estimation results have proved more efficient when compared with EGARCH model estimatio

Validation of combine white noise using simulated data

Recent studies reveal that the data that exhibits heteroscedasticity are modelled by Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH).Nevertheless, EGARCH model estimation is not efficient when the heteroscedasticity data have leverage effect.In this study, an algorithm is developed which is called Combine White Noise (CWN).The standardized residuals of EGARCH errors (heteroscedastic variance) are decomposed into equal variances (white noise series). The white noise series are transformed into Combine White Noise Model (CWN).The assessments of the model are based on data simulation.The simulated data of 200 and 300 sample sizes of EGARCH are generated.The generated EGARCH data are based on low, moderate and high values of leverage and skewness.Each of these generated EGARCH data is used for the estimation of EGARCH and Moving Average (MA). The same generated EGARCH data are transformed to obtain CWN data and VAR data for the estimation of CWN and VAR.Each CWN results outperformed every result of the existing models.These results confirm that CWN is the appropriate model for estimation.The CWN model fit best in the transformed 200 sample sizes of EGARCH generated data with moderate leverage and moderate skewness. While the best forecast is in the transformed 200 sample sizes of EGARCH generated data with high leverage and moderate skewness. 200 sample sizes of EGARCH generated data with right values of leverage and skewness are better than using 300 sample sizes to have reliable output

Modeling the heteroscedasticity in data distribution

The main objective of this study is to provide a model that will uplift the weaknesses of the existing model for efficient estimation. Generalized autoregressive conditional heteroscedasticity (GARCH) family models weaknesses were overcome by the new Combine White Noise (CWN) model which proved to be more efficient.CWN estimation passed stability condition, stationary, serial correlation, the ARCH effect tests and it also passed the Levene’s test of equal variances using both Australia (A.U.) and United States (U.S.) GDP data sets. The CWN estimation produced better results with minimum information criteria and high log likelihood values in both U.S. and A.U. data estimation.CWN has the minimum forecast errors which were better results when compare with the GARCH model dynamic evaluation forecast errors in both countries.The determinant of the residual of covariance matrix values revealed that CWN was efficient in the two countries, but A.U.was more efficient.Based on every result in the empirical analysis of the two countries, CWN was the more appropriate model

Modelling the Error Term of Australia Gross Domestic Product

The main aim of this study is to model the Gross Domestic Product (GDP) with the new Combine White Noise (CWN) Model and compare the results with the Vector Autoregressive (VAR) Model and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) Model which are the existing models. The CWN model estimation yields best results with least information criteria and high log likelihood values. While the EGARCH model estimated yields better results with least information criteria and high log likelihood values when compared with VAR model. CWN has the least forecast errors which are indications of best results when compare with the EGARCH and VAR models, dynamic evaluation forecast errors. The minimum forecast error values indicate forecast accuracy. The determinant of the residual of the covariance matrix value indicates that CWN is efficient, while the determinant of the residual of the covariance matrix value indicates that VAR is not efficient. The total results testify that CWN is the most right model. To model the data that exhibit conditional heteroscedasticity with leverage effect in Australia and other societies in the world efficiently, CWN is recommende

Evaluating combine white noise with US and UK GDP quarterly data

The main objective of this study is to evaluate the Combine White Noise (CWN) model for the confirmation of its effectiveness in addressing the error term challenges.CWN models the leverage effect appropriately with better estimation results of which the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model cannot handled.The determinant of the residual co variance matrix values indicates that CWN estimation is efficient for each country.CWN has a minimum forecast errors which indicates forecast accuracy by estimating the countries data individually.The overall results indicate that CWN estimation provide more efficient and better forecast accuracy than EGARCH estimation.This boosts the economy

Modeling the error term of regression by combine white noise

This paper examines the utilization of combination model technique to model the standardized residual exponential generalized autoregressive conditional heteroscedastic (EGARCH) errors.The technique combine white noise (CWN) is found to be more efficient and overcome EGARCH weaknesses. The estimation results using Combine White Noise model satisfies stability condition and passes stationary, serial correlation, and the ARCH effect tests.It fails the histogram-Normality tests but passes the Levene’s test of equal variances. Combine White Noise has minimum values of information criteria. From the results of the dynamic evaluation forecast errors, Combine White Noise has the minimum forecast errors which are indications of better results when compare with the EGARCH model dynamic evaluation forecast errors. Combine White Noise processes show the best fit with forecast accuracy

Comparing vector autoregressive (VAR) estimation with combine white noise (CWN) estimation

The purpose of this study is to compare one of the existing models, which is VAR model with the new Combine White Noise model. The VAR models have not been able to model the conditional heteroscedasticity and the leverage effect exhibited by the data. Likewise, GARCH family models cannot model leverage effect. The Combine White Noise (CWN) has proved more efficient and takes care of these weaknesses. CWN has the minimum information criteria and high log likelihood when compare with VAR estimation. The determinant of the residual covariance matrix value indicates that CWN estimation is efficient. It passes the Levene’s test of equal variances. CWN has a minimum forecast errors which indicates forecast accuracy. All its outcomes outperform all the outcomes of VAR widely

Modeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity Processes

This study has been able to reveal that the Combine White Noise model outperforms the existing Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Moving Average (MA) models in modeling the errors, that exhibits conditional heteroscedasticity and leverage effect. MA process cannot model the data that reveals conditional heteroscedasticity and GARCH cannot model the leverage effect also. The standardized residuals of GARCH errors are decomposed into series of white noise, modeled to be Combine White Noise model (CWN). CWN model estimation yields best results with minimum information criteria and high log likelihood values. While the EGARCH model estimation yields better results of minimum information criteria and high log likelihood values when compare with MA model. CWN has the minimum forecast errors which are indications of best results when compare with the GARCH and MA models dynamic evaluation forecast errors. Every result of CWN outperforms the results of both GARCH and M

Modelling the asymmetric volatility with combine white noise across Australia and United Kingdom GDP data set

The objective of this investigation presents Combine White Noise (CWN) Model that outperform the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). This study employed the GDP data set of two countries to compare the results of the new CWN Model with existing EGARCH Model.The empirical analysis for the two countries revealed that CWN proved to be more appropriate model.The inference of CWN yielded a reliable outcome of lower information criteria with higher log likelihood values in each country data evaluation while EGARCH revealed higher information criteria and lower log likelihood values when comparing the two models. CWN provided a better forecast output with lower forecast errors values in each country whereas EGARCH offered higher values of forecast errors. CWN estimation was efficient in both countries as the determinant of the residual of covariance matrix is approximately zero while AU has better estimation efficiency than UK. This will assist the policy makers to plan for reliable economy of a society