Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models
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ABSTRACT
In this research we focused on Multivariate Generalised Autoregressive Conditional Heteroskedasticity models for volatility series using response vector of variances. The work aimed at developing alternative multivariate GARCH models characterised by either autoregressive or moving average process. Isolated Multivariate Generalised Conditional Heteroskedasticity, ISO-MGARCH (p,0) models and Isolated Multivariate Generalised Conditional Heteroskedasticity, ISO-MGARCH(0,q) models are identified from MGARCH (p,q) model under specific conditions. To ascertain the models applicability, the isolated univariate and multivariate GARCH (2,0) models were fitted to volatility measures of Nigeria average, urban and rural consumer price indices from January 1995 to December 2019 after the series were subjected to stationarity checks using the positive definiteness property of the sub-autocovariance or autocorrelation matrices of individual vector processes as components of the cross-covariance or cross-autocorrelation matrix to ascertain the stationarity of the series,. The volatility series were also subjected to autocorrelation and partial autocorrelation checks, as applicable to stationary autoregressive moving average process, where single autoregressive and moving average models are identified under certain conditions. This justified the isolation of pure autoregressive and pure moving average MGARCH models. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Schwarz’s Information criterion (SIC) compare the isolated multivariate GARCH models with the existing univariate GARCH models, and its simulated values, the results revealed the same comparative advantage in capturing volatility series.
TABLE OF CONTENTS
Page
Title Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgments v
Table of Contents vi
List of Tables viii
List of Figures ix
Abstract x
CHAPTER 1: INTRODUCTION
1.1 Background of the Study 1
1.2 Statement of the Problem 9
1.3 Objective of the Work 9
1.4 Justification of the Study 10
1.5 Scope of Study 11
1.6 Significance of the Study 11
CHAPTER 2: REVIEW OF RELATED LITERATURE 12
2.1 Review of Models 12
2.2 Empirical Review 21
CHAPTER 3: METHODOLOGY 28
3.1 Cross-covariances 28
3.2 Auto-correlations and Cross-auto-correlations 31
3.3 Positive Definitness of Auto-correlations and Cross-
auto-correlation matrices 33
3.4 Univariate Case 34
3.4.1 Testing for ARCH Effects 34
3.4.2 Model estimation 36
3.4.3 Post estimation test 36
3.5 Multivariate Case 36
3.6 Volatility Measure 38
3. 7 Conditions for Model Identification 42
3.7.1 Proof 42
3.8 Model Selection Criteria 44
3.8.1 Akaike Information Criterion (AIC): 44
3.8.2 Bayesian Information Criterion (BIC): 45
3.8.3 Schwarz’s Information Criterion (SIC): 45
CHAPTER 4: DATA ANALYSIS AND DISCUSSION OF RESULTS 46
4.1 Numerical Verification 46
4.2 Components of the Autocorrelation and Cross-autocorrelations 47
4.3 Positive Definiteness of 3x3 Component Autocorrelation Matrices 49
4.4 Positive Definiteness of 9x9 Autocorrelation Matrix 49
4.5 Graphical Analysis 53
4.6 Univariate GARCH (p,0) Model Estimates 55
4.7 Isolated Multivariate GARCH (p,0) Model Estimates 57
CHAPTER 5: SUMMARY AND CONCLUSION 64
5.1 Summary and Conclusion 64
5.2 Recommendation 66
References 67
Appendices 71
LIST OF TABLES
4.1 Parameter Estimates of the Univariate GARCH (2,0) Models 56
4.2 Parameter Estimates of the Multivariate GARCH(2,0) Models 57
4.3 The parameter estimates for 3000 Data Points Simulated Values 59
4.4 Information Criteria 62
4.5 Information Criteria for Simulated Values 62
LIST OF FIGURES
4.1 Return series of Average CP 53
4.2 Return Series of Urban CPI 53
4.3 Return Series of Rural CPI 54
4.4 Autocorrelation Function of Average Consumer Price Index 54
4.5 Partial Autocorrelation Function of Average Consumer Price Index 54
4.6 Autocorrelation function the residual of MGARCH model 61
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APA
EYO, A. C. (2023). Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models . Michael Okpara University of Agriculture. Retrieved June 7, 2026, from http://repository.mouau.edu.ng/works/some-special-classes-of-multivariate-generalized-autoregressive-conditional-heteroscadasticity-garch-models-7-2
MLA
EYO, AWAKESSIEN CLEMENT. "Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models ." Michael Okpara University of Agriculture, 31 Aug. 2023, http://repository.mouau.edu.ng/works/some-special-classes-of-multivariate-generalized-autoregressive-conditional-heteroscadasticity-garch-models-7-2. Accessed June 7, 2026.
Chicago
EYO, AWAKESSIEN CLEMENT. "Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models ." Michael Okpara University of Agriculture (2023). Accessed June 7, 2026. http://repository.mouau.edu.ng/works/some-special-classes-of-multivariate-generalized-autoregressive-conditional-heteroscadasticity-garch-models-7-2