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
AWAKESSIEN, E (2023). Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models . Repository.mouau.edu.ng: Retrieved Nov 23, 2024, from https://repository.mouau.edu.ng/work/view/some-special-classes-of-multivariate-generalized-autoregressive-conditional-heteroscadasticity-garch-models-7-2
EYO, AWAKESSIEN. "Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models " Repository.mouau.edu.ng. Repository.mouau.edu.ng, 31 Aug. 2023, https://repository.mouau.edu.ng/work/view/some-special-classes-of-multivariate-generalized-autoregressive-conditional-heteroscadasticity-garch-models-7-2. Accessed 23 Nov. 2024.
EYO, AWAKESSIEN. "Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models ". Repository.mouau.edu.ng, Repository.mouau.edu.ng, 31 Aug. 2023. Web. 23 Nov. 2024. < https://repository.mouau.edu.ng/work/view/some-special-classes-of-multivariate-generalized-autoregressive-conditional-heteroscadasticity-garch-models-7-2 >.
EYO, AWAKESSIEN. "Some Special Classes Of Multivariate Generalized Autoregressive Conditional Heteroscadasticity (Garch) Models " Repository.mouau.edu.ng (2023). Accessed 23 Nov. 2024. https://repository.mouau.edu.ng/work/view/some-special-classes-of-multivariate-generalized-autoregressive-conditional-heteroscadasticity-garch-models-7-2