Measuring Volatility of Nairobi Stock Exchange Using Garch and Egarch Models

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  • Topic: Stock market, Autoregressive conditional heteroskedasticity, Stock market index
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ASSESMENT OF CONSISTENCY OF THE NSE ALL SHARE INDEX AND NSE 20 SHARE INDEX IN MEASURING THE NAIROBI SECURITIES EXHANGE CHARACTERISTICS

OSORE DAVID LUVEMBE

A Research Proposal Submitted To The Department Of Business Administration In Partial Fulfillment Of The Requirement For The Award Of The Degree Of Bachelor Of Commerce Of Chuka University College

CHUKA UNIVERSITY COLLEGE
NOVEMBER 2011

DECLARATION AND RECOMMENDATION
Declaration:

I declare that this project is my own original work and has not been presented for award of any degree in this or any other university.
Signed:…………………………… Date……………………………..

OSORE DAVID LUVEMBE.

C12/60097/08 OR BB1/0096/08.

Recommendation

This research has been submitted for examination with my approval as University Supervisor. Signature………………………… Date………………………………

MR.WAGALA, A,
Department of business administration.
Chuka University College.

ABSTRACT

TABLEOF CONTENTS
DECLARATION AND RECOMMENDATIONII
ABSTRACTIII
TABLEOF CONTENTSIV
ABBREVIATIONS AND ACRONYMSVII
CHAPTER ONE1
INTRODUCTION1
1.2 Statement of the problem.3
1.3 Purpose of the study.3
1.4 Objectives.3
1.5 Hypothesis.3
1.6 Significance of the study3
1.7 Scope of the study.3
1.8 Limitation of the study4
CHAPTER TWO4
LITERATURE REVIEW4
2.1 History of the NSE4
2.2 The role of the Nairobi Securities Exchange6
2.3 ARCH model7
2.5 GARCH model.8
2.6 EGARCH model.9
2.7 Indices study for the Nairobi Securities Exchange and the New York Securities Exchange.10
2.7.1 Index calculation methodology10
2.7.2 Index formula.11
2.7.3 computational precision.11
2.7.4 Data correction policy12
CHAPTER THREE13
RESEARCH METHODOLOGY13
3.1 introduction13
3.2 research design13
CHAPTER FOUR14
RESULTS AND DISCUSSION14
4.1 introduction14
4.2 Preliminary analysis.14
Figure 4.114
Figure 4.215
Table 4.316
Table 4.416
4.2 Summery statistics17
Table 1.17

ABBREVIATIONS AND ACRONYMS
ARCH - Autoregressive Conditional Heteroscedasticity.
DASS – Delivery And Settlement System.
GARCH - Generalized Autoregressive Conditional Heteroscedasticity IGARCH – Integrated Generalized Autoregressive Conditional Heteroscedasticity. NSE – Nairobi Securities Exchange.
NASI – Nairobi All share Index.

CHAPTER ONE
INTRODUCTION
1.1. Background of the Study
Financial markets can move quite dramatically, and stock prices appear too volatile to be justified by changes in fundamentals. Such observations have been under scrutiny over the years and are still being studied vigorously (LeRoy and Porter, 1981; Shiller, 1981; Zhong et al., 2003). Volatility as a phenomenon as well as a concept remains central to modern financial markets and academic research. The link between volatility and risk has been to some extent elusive, but stock market volatility is not necessarily a bad thing. In fact, fundamentally justified volatility can form the basis for efficient price discovery, while volatility dependence implies predictability, which is welcomed by traders and medium- term investors. The importance of volatility is widespread in the area of financial economics. Equilibrium prices, obtained from asset pricing model, are affected by changes in volatility, investment management lies upon the mean variance theory, while derivatives valuation hinges upon reliable volatility forecasts. Portfolio managers, risk arbitrageurs, and corporate treasures closely watch the volatility trends, as changes in prices could have a major impact on their investment and risk management decisions. Speculators are usually seen with some sort of resentment by the wider community. Form the early days, scholar have either supported that speculators stabilize prices (Smith, 1776; Mill, Friedman, 1953) or argued that speculators make money at the expense of others. In emerging economies like Kenya, there have been a great focus on market volatility and its effect on...
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