DETERMINATION OF VOLATILITY CLUSTERING IN GARCH FAMILY MODELS USING R


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TABLE OF CONTENTS

Title page
Certification
Approval Page
Dedication
Abstract
Acknowledgement
Table of contents
List of tables
List of figures

CHAPTER ONE: INTRODUCTION
1.1       Background of the Study
1.2       Statement of Problem
1.3       Objectives of Study
1.4       Significance of study

CHAPTER TWO: LITERATURE REVIEW
2.1       Volatility and Forecasting of Exchange rate
2.2       Comparing other models with GARCH family models
2.3       Empirical Evidences in modeling Exchange rate volatility using GARCH models

CHAPTER THREE - METHODOLOGY
3.1       Source of Data
3.2. Volatility Clustering:
3.3       Estimation of volatility (conditional variance):
3.4 GARCH Model:
3.5       Standard GARCH model (sGARCH):
3.6       NGARCH (Non linear GARCH):
3.7TGARCH Model:
3.8       GJR-GARCH Model:
3.9AVGARCH Model:
3.10NAGARCH Model:
3.11Asymmetric Power ARCH (APARCH) Model:
3.12ALLGARCH/FGARCH Model:
3.13EGARCH Model:
3.14     ARCH TEST
3.15 Adjusted Pearson goodness of fit test:
3.16 Normal quantile:

CHAPTER FOUR - ANALYSIS AND DISCUSSION
4.1       Exploratory Data Analysis
4.2       Stationarity of Data
4.3Fitting the Garch Models
4.4Determination of Volatility Clustering
4.5Estimated Values of Conditional Variance (Volatility)
4.6Plot of Volatility
4.7       Forecasting
4.8       Forecast Evaluation
4.9Results and Interpretation

CHAPTER FIVE:
SUMMARY, CONCLUSION RECOMMENDATIONS
5.1       Summary
5.2       Conclusion
5.3       Recommendations
References
Appendix




ABSTRACT


This work investigated the volatility clustering of exchange rate of Nigeria Naira against the United States of America Dollar. The data used in the present study consist of the monthly exchange rates of the Naira to Dollar from January 1999 to December 2012 obtained from the Central Bank of Nigeria. The main focus is to provide a proper understanding of the theory and empirical working of GARCH family models and to determine volatility clustering. The EGARCH(2,2) model was selected as the best model from point of view of the Mean Absolute Error. The estimated parameters, plots of returns series and plot of conditional variances were used in determining the volatility clustering. There were clear evidences of volatility clustering in Exchange rate of Nigeria naira against United States dollar.





CHAPTER ONE


INTRODUCTION


1.1 Background of the study


For almost five decades, exchange rates movements and fluctuations have become an important topic of macroeconomic analysis and policy makers, particularly after the collapse of the Bretton Woods agreement in 1973 (Omojimite and Akpokodje 2010), of fixed exchange rates among major industrial countries. This volatility in exchange rate affects security valuation, investment analysis, profitability and risk management. This is because it exposes exchange rates to financial uncertainties. The volatility in the exchange rate may cause a significant impact on macroeconomic analysis such as prices, wages, unemployment and the level of output.

Exchange rate means the exchange of one currency for another price for which the currency of a country (Nigeria) can be exchanged for another country’s currency as in US (dollar). A correct exchange rate do have important factors for the economic growth for most developed countries whereas a high volatility has been a major problem to the economy of some of the African countries such as Nigeria. Some factors which definitely affect exchange rate are interest rate, inflation rate, trade balance, general state of economy, money supply and other similar macro-economic giants’ variables.

A steady exchange rate can help financial institutions to tackle and to monitor the performance of investments, financing, hedging and as well as reducing their operational risks.

Volatility can be represented by variance or standard deviation which is unconditional and does not recognize that there are interesting patterns in asset volatility e.g. time-varying and.....



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