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DEDICATIONS

This dissertation is dedicated to my late father, and my mother for their enduring moral and

financial support.

Thank you, uncle, Dorcas and Ruvimbo

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ACKNOWLEDGEMENTS

I wish to acknowledge my profound gratitude to the many people who contributed in various

ways to make this project a reality. The preparation of this document would not have been

possible without the invaluable and unequal support received from these people in their different

capacities.

Most of all I would like to thank the Lord for the guidance. In addition I would like to thank my

supervisor, Mr. Mandishekwa for his tremendous efforts and guidance that really helped me in

completion of this project. I would also like to thank my econometrics lecturer, Mr. Dzingirai

who made me know this module, that I can do a project on it. I would like to thank the entire

Economics department as a whole for guidance.

I would also want to acknowledge the tremendous support received from my family and friends.

To Sylvia Musekiwa and Sikelela Ncube thank so much for your machine-you saved

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ABSTRACT

This study examines the dynamic interactions between the stock market and inflation, 90-day

Treasury bill rate and exchange rate in Zimbabwe for the period 1995:01 to 2007:03. The study

uses tests for Granger causality. The stock market has been on an upward trend for most of the

period under study. The depreciation of the currency on the parallel market and the changes in

inflation can explain the recent rapid increase in stock prices. Granger causality tests using the

Sims approach reveal uni-directional causality between inflation and industrial share index and

bi-directional between exchange rate and the industrial share index and bi-directional causality

from industrial share index to interest rates

TABLE OF CONTENTS

iii

DEDICATIONS..............................................................................................................................i

ACKNOWLEDGEMENTS..........................................................................................................ii

ABSTRACT..................................................................................................................................iii

TABLE OF CONTENTS.............................................................................................................iv

1.0 Introduction.......................................................................................................................1

1.3 Objectives of the Study..........................................................................................................4

1.4 Significance of the Study.......................................................................................................5

1.5 Research Hypothesis..............................................................................................................5

1.6 Research Questions................................................................................................................5

1.7 Assumptions...........................................................................................................................6

1.9 Organization of the Study......................................................................................................6

CHAPTER 2...................................................................................................................................7

LITERATURE REVIEW.............................................................................................................7

2.0 Introduction............................................................................................................................7

2.1 Theoretical Review................................................................................................................7

2.2 Empirical Review...................................................................................................................9

2.3 Conclusion...........................................................................................................................14

METHODOLOGY......................................................................................................................15

3.0Introduction...........................................................................................................................15

3.1 Model Specification.............................................................................................................15

3.2 Diagnostic Tests...................................................................................................................17

3.2.1 Unit Roots Tests................................................................................................................17

3.2.2 Integration and Cointegration Tests..................................................................................17

3.2.3 Causality Tests..................................................................................................................18

3.3 Justification of Variables.....................................................................................................18

3.3.1 Exchange Rates.............................................................................................................18

3.3.2 Consumer Price Index (As a Measure of Inflation)......................................................18

3.3.3 Interest Rates.................................................................................................................19

3.3.4 The Stochastic Error Term (The Disturbance Term)....................................................19

3.4 Data Characteristics.............................................................................................................20

3.5 Strengths and Weaknesses of the Model..............................................................................20

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3.5 Conclusion...........................................................................................................................20

CHAPTER FOUR.......................................................................................................................21

DATA PRESENTATION...........................................................................................................21

4.0 Introduction..........................................................................................................................21

4.1 Unit Roots Tests and Cointegration Results........................................................................21

4.2 Interpretation of Results.......................................................................................................24

4.3 Conclusion...........................................................................................................................25

CHAPTER FIVE.........................................................................................................................26

RECOMMENDATIONS and CONCLUSIONS.......................................................................26

5.0 Introduction..........................................................................................................................26

5.1 Policy Recommendations.....................................................................................................26

5.2 Suggestions for Future Research..........................................................................................27

5.3 Conclusions..........................................................................................................................28

REFERENCES............................................................................................................................29

APPENDIX...................................................................................................................................34

List of Tables

v

Table one-optimal lag length……………………………………………………..................21

Table two-results of unit roots tests…………………………………………….. .................22

Table 3-causality results…………………………………………………………..................23

Table four-direction of causality..............................................................................................24

List Of Figures

vi

Figure one: Trend in the industrial index…………………………………2

Figure two: Trend of the inflationary pressure……………………………3

List of appendix

Appendix A: data set……………………………………………………34

vii

Appendix B unit roots tests……………………………………………..36

Appendix C causality tests………………………………………………40

viii

CHAPTER ONE

INTRODUCTION

1.0 IntroductionSince stock markets are vital for any well defined financial system of a country, this research

will explore the linkages and the relationships between the Zimbabwe Stock Exchange (ZSE)

industrial index and the highlighted macroeconomic variables of consumer price index (CPI),

short term nominal interest rates(90-day TB-rate) and exchange rates(ZWD/USD). The

Zimbabwe stock exchange acts as a secondary market giving a platform for companies to raise

capital through ‘issues’ to the investing public. This chapter encompasses: the background of the

study, statement of the problem, objectives, significance of the study, research questions,

research hypothesis, limitation/delimitations, assumptions and organization of the study.

1.1 Background of the Study

The Zimbabwe stock exchange is a small dynamic stock exchange. It was open to investors since

1993.It formalized its operations following passing of the Stock Exchange Act which was

implemented in 1974 by then it was the Rhodesian Stock Exchange (RSE).Since then the

Zimbabwe stock exchange has grown immensely to become one of the most important equities

exchange in Africa and a provider of services that ease the increasing of capital and the dealings

of shares. In Africa the Zimbabwe stock exchange boast as the third largest bourse after the

Johannesburg Stock Exchange (JSE) of South Africa and Casablanca Stock Exchange of

Morocco. According to its website (www.zse.co.zw), more than seventy five (75) local and

international companies are now listed with the local bourse.

The Zimbabwe stock exchange reports on trading prices daily .Basically there are two weighted

indices; the industrial index and the mining index .The industrial index uses data from various

industrial counters listed with the Zimbabwe stock exchange and it cuts across all the sectors of

the economy. The mining index on the other hand uses strictly data from the mining sector of the

economy (ZSE website).

1

According to Adept solutions (1999), developments in the Zimbabwe Stock Exchange have been

largely influenced by the prevailing unstable macroeconomic conditions, characterized by low

interest rates, dual interest rates, exchange control, shortage of foreign currency and

hyperinflation as well as the unstable political situation, land reforms and decline in foreign

participation.

Over the years 1995 to 2005 the trend between the Zimbabwe Stock exchange and the selected

macroeconomic variables of interest rates, exchange rates and consumer price index was rather

positive. The industrial index was fluctuating below the inflation rate (represented by the CPI in

this case) and the interest rates from the beginning of the period under study but later caught up

with inflation rate as from 2006.Besides the soaring interest rates and ever increasing inflation

rate as well as persistent depreciation in the Zimbabwean dollar against major currencies such as

the united states dollar, the ZSE sustained an upward trend (2006 monetary policy statement).

The trend in the ZSE index is shown in the figure below.

Figure One: Trend In Industrial Index

A04M04J04J04A04S04O04N04D04J05F05M05A05

0

50

100

150

200

Index%

Source Zimbabwe Stock Exchange

It is due to the above mentioned background that The African Stock Exchange Association

(ASEA) rated the ZSE as the best performing bourse on the African continent for the year 2005..

This rise was well above of the Casablanca Stock Exchange one of its closest rivals.

One of the major challenges to the economy at the inception of reforms was high inflation. This

high level of inflation resulted in reduced operations and company closures because of cash flow

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mismatches. The inflation level for the year 2007 was very high compared to SADC partners

thus eroding the economy’s export competitiveness. It was fuelled by high budget deficit, high

interest rates, deteriorating terms of trade, electricity and fuel shortages. (RBZ publications) The

graph below illustrates the trend of the inflationary pressures that affected the economy.

Figure two: Trend Of The Inflationary Pressure

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 20060

200

400

600

800

1000

1200

cpi

Source: Reserve Bank Of Zimbabwe

The trend of the consumer price index indicates that there was an upward path of the inflation

between 1995 and 2004. This upward trend in inflation continued even up to 2007. These

inflationary trends are expected to have depressed stock market activity by lowering business

activity. Because it is the main signal for business in market driven economies, an increase in

inflation raises the country’s economic risk and thus can scare off foreign investors.

When reforms were introduced the local currency had to be devalued to promote exports. The

local currency was then pegged at about Z$38/US$ from 1998 until it was devalued again by

44.4% in August 2000 to Z$55/US$(RBZ 1997). The influence that the devaluation of the

Zimbabwean dollar could have had on the performance of the ZSE is not clear. It depends on the

net benefits to both exporters and importers.

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Interest rates were relatively high because of limited competition in the banking system. In

October 1995, the RBZ redefined the rediscount rate to reflect its medium term view on inflation

and thus maintained it at 29.5% when inflation was above 22 %.( RBZ 1996). High inflation

resulted in the minimum bank lending rates being raised negatively impacting on equity

investment. Positive real rates of interest were maintained in the money market.

The impact the Treasury bill rate could have had on the stock market is obviously negative

because an increase in the Treasury bill rate is expected to have to channel funds from the stock

market to the money market.

1.2 Statement of the Problem

Basically according to theoretical and economic postulations, a good stock market performance

is due to good macroeconomic performance, Smith et al (1994). However in the case of

Zimbabwe, the country was experiencing the worst melt down during most of the period under

review but the stock market has sustained an upward trend. According to the ZSE website, it was

rated as the as the best performing stock exchange in the world rising capital markets in 2007.

The researcher therefore intends to investigate why the ZSE under those unfortunate

circumstances has managed to defy the odds and emerge as the best performing stock exchange

among the world emerging capital markets by looking at the relationship between the ZSE’s

industrial index and the selected macroeconomic variables.

1.3 Objectives of the StudyIn carrying out this research, the researcher intends to fulfill the following objectives:

To establish whether the selected macroeconomic variables are crucial in explaining the

behavior of the stock market.

Scrutinize the long run relationship between the performance of ZSE and the selected

macroeconomic variables(interest rates, exchange rate and consumer price index)

To contribute to the international debate regarding the relationship between these variables.

In so doing the researcher will avail some new literature on the sophisticated relationship

among the selected macroeconomic variables and the stock market industrial index.

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1.4 Significance of the StudyIn tackling this topic, the researcher will get an informed perspective between the ZSE industrial

index, interest rates, exchange rates and consumer price index. The institution, Midlands State

University is going to harness this work as one of the foundations for further research to the

related areas as well as teaching aids. The information from this research can also be lead to

effective policy implementation by policy makers. To the investor it aids investment decision in

different economic climate.

Although research in the area has been well documented and well covered in both the developed

and developing worlds including Zimbabwe-for example Oyama (1997) investigated the

relationship between the stock market and macroeconomic variables in Zimbabwe using the

Revised Discount Model, ECM and Multifactor Return Model, Sadosky (2001) studied the

interaction between the stock market and economic activity in the US and Kurihara (2006)

investigated the relationship between macroeconomic variables and daily stock prices in Japan

the period covered by the researcher is of great interest to many stakeholders who have

questioned the ability of macroeconomic fundamentals in explaining ZSE given the illicit

dealings that caused speculative behavior on the local bourse.

In addition, given the fact that the year 2007 was almost the dusk of the hyperinflationary

environment and the use of the Zimbabwean dollar as legal tender in Zimbabwe the researcher is

likely to close the curtain in the Zimbabwean dollar era.

1.5 Research Hypothesis H0: macroeconomic variables does not Granger cause ZSE index

H1: macroeconomic variables Granger cause ZSE index

1.6 Research QuestionsIn tackling this research, the researcher should answer the following questions:

Which of the selected macroeconomic variables has greatest influence on the performance of

the Zimbabwe stock exchange?

Is there any causal relationship between the selected macroeconomic variables and the ZSE

industrial index?

If there is any causality what is the direction of the causality?

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1.7 Assumptions In carrying out this research the researcher assumed the following:

All the information about the variables in question is authentic and satisfactorily reliable.

Therefore all the data sources used (CSO, RBZ and IMARA) are reliable in collecting their

data.

The econometric package (E-views student’s 3.1 versions) used correctly computes the

relationship between the variables under consideration.

The causal ordering is unknown. It is unknown which variable is a cause and which one is a

result of the relation.

1.9 Organization of the Study This research will be conducted in a systematic way as follows:

Chapter two offers both the theoretical and empirical literature. Chapter three considers the

methodology employed by the researcher in the study. It also highlights the data used. More so,

it justifies the inclusion of certain variables in the study. Chapters four presents and analyze the

findings from chapter three (results will obtained using E-views student’s 3.1 version).Chapter

five summarizes the research findings and also proposing policy recommendations drawn

directly from this study.

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CHAPTER 2

LITERATURE REVIEW

2.0 IntroductionSir Isaac Newton once said, “It is by standing on the shoulders of giants that you can manage to

see more than others.” The researcher therefore exploits the ideas put forth by different

individuals about the particular issues in this chapter in order to add value to his research.

This chapter reviews both theoretical and experimental underpinnings of this study. Literature on

the various studies on stock market performance is examined to give lessons to the current study.

Theoretical review analyses the theoretical premises of this while empirical assess past

researches for both developing and developed economies that are relevant to this study. There

are various opinions reflecting wide views towards the impact of real interest rates, exchange

rates and consumer price index on stock market performance. The behavior of the world’s stock

market performances exhibit diverse responses to the various macroeconomic variables in

question.

2.1 Theoretical Review

Irving Fisher (1930) found that real interest rates were equal to nominal interest rates minus

expected inflation. This macroeconomic relationship is known as the Fisher Effect. The Fisher

Effect is unique in that it incorporates expected inflation as opposed to actual inflation rates into

the analysis. This is crucial to this study because it allows the use of rational expectations model.

The Fisher Effect is primarily an alternative way of measuring real interest rates and is used as a

means of relating interest rates and inflation expectations to stock prices. To fully understand the

relationship between the Fisher Effect and stock prices, it is necessary to understand the

individual relationships between inflation expectations, interest rates, and the stock market.

An Efficient Market is market in which the values of securities at any instant in time fully reflect

all available information resulting in the market value and the intrinsic value being the same.

Propounded by Fama (1970) EMH also states that the prices of shares will immediately adjust to

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new information. There are three different types of efficiency in an efficient market. These are

weak form, semi strong form and strong form.

The weak form fully incorporates information about past stock prices. Stock prices are said to

follow a random walk as the information on stock comes in a random manner. Attempts to use

technical analysis are futile in predicting future prices. In the semi strong form, prices

incorporate all publicly available information including published accounting statements as well

as historical price movements. Thus any information that can be extracted is already reflected in

the price.

In the strong-form all information public or private is incorporated. It makes the most stringent

demand on information since it says that even the information available only to those closely

concerned with the firms has already been taken up and incorporated in the price. The main

fundamental implication of EMH is that if the markets are efficient then it is impossible for

investors to exploit information in order to earn excess returns over a sustained period of time

(Howells and Bain 2002).

As for the effect of macroeconomic variables on the stock market EMH suggests that

competition among profit maximizing investors in an efficient market will ensure that all the

relevant information currently known about changes in macroeconomic variables are fully

reflected in current stock prices so that investors will not be able to earn abnormal profits

through prediction of future prices, Chong and Goh (2003).

The Capital Asset Pricing Model (CAPM) was developed by Sharpe and Litner in 1964. It is an

equation that equates the expected rate of return on a stock to the risk free rate and the risk

premium for the stock’s systematic risk, Martin and Scot jnr (1996). Basically, the CAPM

illustrates the relationship between expected return on an individual security and the beta of the

security. The beta according to Hull (1997) is a parameter that shows the relationship between

the return on a portfolio of stocks and the return on the market.

In all, the model argues that the investor requires excess returns to compensate for any risk that is

correlated to the risk in the return from the stock market but requires no excess return for other

risks. The Arbitrage Pricing Model (APM) was developed by Ross (1976). According to the

APM returns vary from their expected amounts because of unanticipated changes in a number of

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basic economic forces such as industrial production, inflation rates, term structure of interest

rates and the difference in interest rates between high and low risk bonds, Martin and Scot jnr

(1996). It suggests that the risk of a security is reflected in its sensitivity to unexpected changes

in important economic forces.

The Classical case of investment and savings theory strongly argue that the savings are invested

as a result of interest mechanism. They propel that in an economy where demand and supply

forces are at play, savings and investments are equated in the economy. They suggest that both

investments and savings are functions of interest rates. Savings are positively related to the rate

of interest. As interest rates rise, people are induced to save more and the convex is typically

true. If on the other hand, the interest rates decline, depositors are discouraged to save and they

transfer their money from the money market to equity market and the stock market will start to

perform well (Arnold, 1998).

On the other hand, investment is inversely related to interest rates. Therefore, as interest rates

rises, investments fall since the cost of borrowing rises also. However under the

hyperinflationary environment such as the case in Zimbabwe, people will rush to the equity

market where positive capital gains prevail. Hence most investors will rush to the stock market

where capital gains will be inevitable, Arnold (1998).

From the above scenario, it should come to light that low interest rates reflect themselves in

limited savings generation, especially in a hyperinflationary climate. In fact, investors will be

more concerned about real return that is nominal interest rates less inflation rate. Therefore, they

will discard the money market and invest in the stock market and property market where real

returns are positive. It should be clearly noted that investors are rationale and aim to maximize

capital gains, Arnold (1998).

2.2 Empirical Review

Gunasekarage et al (2004) examined the influence of macroeconomic variables on stock market

equity values in Sri Lanka using the Colombo all share price index to represent the stock market

and money supply ,the Treasury bill rate (as a measure of interest rates ),the consumer price

index as (a measure of inflation ) and the exchange rates as the macroeconomic variables. With

9

monthly data for the 17-year period from January 1985 to December 2001 and employing the

usual battery test which included unit roots, cointegration, and the VECM, they examined long

run and short run relationships between the stock market index and economic variables. The

VECM analysis provided support for the argument that the lagged values of macroeconomic

variables have a significant influence on the stock market.

Rigobon and Brian (2001) used an identification technique based on the heteroscedasticity of

stock market returns to identify the reaction of real interest rates to the stock market performance

in United States of America. They found that real interest rates reacts significantly to stock

market movements, with a 5% rise or fall in the industrial index increasing the likelihood of a 25

basis point tightening (easing) by about half. The authors decompose both daily and weekly

movements in interest rates and stock prices from approximately 1985 to 1999. Their results

suggest that stock market movements have a significant impact on short-term interest rates,

driving them in the same direction as the change in stock prices. The authors attribute this

response to the anticipated reaction of monetary policy to stock market increases. They

acknowledge that this interpretation should be taken a bit cautiously.

Mukherjee and Naka (1995) applied Johansen’s (1988) VECM to analyze the relationship

between the Japanese stock market and exchange rates , inflation , money supply ,real economic

activity ,long term government bond rate and call money rate. They concluded that a

cointegrating relation indeed existed and that stock prices contributed to this relation. Maysami

and Goh (2000) examined such relationship in Singapore. They found that inflation ,money

supply growth , changes in short and long term interest rate and variations in exchange formed

an cointegrating relation with changes in Singapore stock market levels.

Vuyyuri (2005) investigated the cointegrating relationship and causality between the financial

and real sectors of the Indian economy using monthly observations from 1992 through December

2002. The financial variables used were interest rates, inflation rate, exchange rate stock return

and the real sector was proxied by industrial productivity. Johansen (1988) multivariate

cointegration test supported the long run equilibrium relationship between the financial sector

and the real sector. Granger test showed unidirectional Granger causality between financial

sector and real sector of the economy.

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Hardouvelis(1987), Keim(1985), Litzenberger and Ramaswamy (1982) empirically investigated

whether the main economic indicators such as inflation, interest rates ,TB returns, exchange

rates, and money supply effective explain the share returns in Turkey using the Johansen’s

(1988) cointegration analysis. They precisely concluded that there was a negative correlation

between interest rates and Standard and Poor’s index.

William Breen, Lawrence Glosten, and Ravi Jangannathan (1989) completed a study of the

relationship between the Treasury bill rate and the stock market in India using the usual battery

tests (VECM, unit roots tests cointegration tests). The study found that an inverse relationship

between stock index returns and Treasury bill interest rates exists when a value-weighted stock

index is used. The reasoning behind this negative relationship is that, when interest rates rise, the

expected earnings streams decline because of the higher cost of borrowing and financing

expenditures. Because earnings reports play a dramatic role in stock prices, a rise in interest rates

that adversely affects earnings reports will lead to lower stock prices (Breen et al, 1989). In

summary, the Fisher Effect should have a negative relationship with the S&P 500.

Masih and Masih (1996), Kwon et al (1997) Cheung and Ng (1998) and Nasseh and Strauss

(2000) examine the impact of several macroeconomic variables including real interest rates,

inflation rate, real economic activity, long term government bond rate and call money rate on

stock market performance in both developed and emerging economies. Most studies found that

real interest rate among other macroeconomic variables have significant influence on the stock

market and/or the existence of a long-run relationship between these macroeconomic variables

and stock prices.

Oyama (1997) looked closely at the relationship between stock prices and the macroeconomic

variables in Zimbabwe using Revised Discount Model, Error Correction Model and Multifactor

Return Generation Model. This study used quarterly time series broad money supply(M2) ,three

month Treasury Bill rate and the IFC stock return index which considers both capital and

dividends payments. The sample period was set from the first quarter of 1991 to the fourth

quarter of 1996. He noted that by using an error- correction model to stock returns and growth

rate of money and Treasury bill rates has been quite stable since 1991, except during the period

of partial capital account liberalization.

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An analysis of individual stock returns indicates that the Zimbabwe Stock Exchange assimilates

changes in some important macro variables quite consistently. Still, the contributions of these

macroeconomic variables cannot explain the volatile movements of stock returns during the

period from late 1993 to 1994.

In conclusion, Oyama (1995) noted that money supply (M2) and TB rate had an impact on the

performance of stock prices in Zimbabwe since 1991. It should be brought to light that many

theories, literature and empirical evidence strongly believes that the performance of stock market

can be largely be affected by inflation rate, money supply growth, interest rate and drought.

Ibrahim (1999) also investigated the dynamic interactions between the KLSE composite index

and seven macroeconomic variables (industrial production index, money supply M1 and M2,

consumer price index, foreign reserves, credit aggregates and exchange rate using the Johansen’s

(1988,1991,1992b) and Johansen and Juselius (1996) multivariate cointegration techinique.

Observing that macroeconomic variables led the Malaysian stock indices he concluded that

Malaysian stock market was informationally-inefficient.

Chong and Goh (2003) results were similar to the above they showed that stock prices, economic

activities, real interest rates and real money balances in Malaysia were linked in the long run

both in the pre and post capital control sub periods.

Sadosky (2001) studied the interaction between the stock market and economic activity in the

United States using monthly data from 1974 to 1994. The macroeconomic variables considered

were the US production index, consumer price index and three month Treasury bill rate. The real

stock prices were calculated by deflating the nominal standard and poor index 500(S&P 500) by

the CPI. He used the Granger causality tests and noted that causality runs from inflation to

changes in interest rates. Interest rates on the other hand, predict changes in real stock returns

and changes in stock prices predict changes in industrial production. He also concluded that there

was no evidence of Granger causality running from real stock returns to inflation.

Maysami and Sims (2002, 2001a, 2001b) employed the Error Correction Modeling (ECM)

technique to examine the relationship between macroeconomic variables and the stock returns in

12

Hong Kong and Singapore (Maysami and Sims 2002b), Malaysia and Thailand (Maysami and

Sims 2001a), Japan and Korea (Maysami and Sims 2001b).

Through the employment of Hendry (1986) approach which allows making inferences in the

short run relationship between macroeconomic variables as well as the long run adjustments to

equilibrium, they analyzed the influence of interest rates, inflation, money supply, exchange rate

and real activity along with a dummy variable to capture the impact of the 1997 Asian financial

crisis. The results confirmed the influence of macroeconomic variables on the stock market

indices in each of the six countries under study though the type of magnitude of the association

differed depending on the country’s financial structure.

Kurihara (2006) chooses the period March 2001-September 2005 to investigate the relationship

between macroeconomic variables and daily stock prices in Japan. He takes Japanese stock

prices, U.S. stock prices, exchange rate (yen/U.S. dollar), the Japanese interest rate etc. The

empirical results show that domestic interest rate does not influence Japanese stock prices.

However, the exchange rate and U.S. stock prices affect Japanese stock prices. Consequently, the

quantitative easing policy implemented in 2001 has influenced Japanese stock prices. Doong et

al. (2005) investigate the dynamic relationship between stocks and exchange rates for six Asian

countries (Indonesia, Malaysia, Philippines, South Korea, Thailand, and Taiwan) over the period

1989-2003. According to the study, these financial variables are not cointegrated. The result of

Granger causality test shows that bidirectional causality can be detected in Indonesia, Korea,

Malaysia, and Thailand. Also, there is a significantly negative relation between the stock returns

and the contemporaneous change in the exchange rates for all countries except Thailand.

Bhattacharya and Mukherjee (2003) investigated Indian markets using the data on stock prices

and macroeconomic aggregates in the foreign sector including exchange rates, money supply as

well as interest rates and concluded that there is no significant relationship between stock prices

and exchange rates. In another study, Muhammad and Rasheed (2003) examined the relationship

between stock prices and exchange rates of four South Asian countries namely Bangladesh,

India, Pakistan and Sri Lanka and found that there is no significant relationship between the

variables in India and Pakistan, either in the short-run or long-run. However, they found a bi-

directional relationship in the case of Bangladesh and Sri Lanka.

13

2.3 Conclusion

Based on the afore mentioned literature, one can safely say that countable studies in Zimbabwe

and other emerging markets as well as developed nations have used macroeconomic

fundamentals to analyze the performance of the stock market. Most studies used cross-country

data hence most focus was based on the difference in structures of economies and may explain

differences in economic performance across economies. The proceeding chapter focuses on the

methodological approach to be used in this study.

14

CHAPTER 3

METHODOLOGY

3.0IntroductionThis episode focuses on the method and the techniques to be used to come up with the solution to

the hypothesis. Furthermore the model to be employed for testing the data is also explained. In

this chapter major aspects such as model specification, the estimation of the Sims (1972) basing

on the Granger multivariate model, the unit root tests and cointegration tests are also discussed.

3.1 Model SpecificationIn this particular study, the researcher will make use of quantitative variables from time series

data. The data will be of quarterly set up. Analysis of the data will be through the econometric

model adopting Granger and Sims multivariate approach. The researcher shall make use of the

econometric package of E-views 3.1 that will employ modern techniques in modeling of time

series data.

Although there is no universal definition of causation, Granger (1969) launched an approach

based on time series data in order to determine causality. Causality in econometric sense refers to

the ability to predict. Thus causality in Granger (1969) and Sims (1972) sense: a variable x is

said to granger cause y if present y can be predicted by greater accuracy by using past values of x

rather than not using such past values all other information being identical, from the definition of

causality if β1=β2=β3=0 the x does not granger cause y. However if any of the β coefficients are

non zero then x does granger cause y.

There are different types of situations under which Granger causality test can be applied. These

include;

The simple bivariate Granger causality where there are two variables and their lags.

A multivariate Granger causality where more than two variables are considered. This is

used where it is supported that more than one variable can influence the results.

Granger causality can also be tested in a Vector Autoregressive (VAR) framework where

a multivariate model is extended so as to test for simultaneity of all included variables.

15

The researcher will employ Sims’ test basing on the Granger definition of causality. A simple

regression equation which includes all the explanatory variables in this research can be stated

as:

Indt= β0 + β1Irt + β2CPIt+β3Ert + εt……………………………………………3.0

Where indt is industrial index in period t

Irt is interest rate in period t

CPIt is consumer price index in period t

Ert is the Zimbabwean dollar to United States dollar exchange rate

The researcher chooses to employ the Sims (1972) test, based on Granger’s (1969) definition of

causality. In Sims approach, a Granger causality relationship is expressed in two pairs of

regression equations by simply twisting independent and dependant variables as follows:

Xt =β1,1Xt –1 + β1,2Xt –2 +…+β2,1Yt –1 + β2,2Yt- 2 + …+mm1,t. …………(3.1)

Yt =β2,1Yt –1 + β2,2Yt- 2 +…+β1,1Xt –1 + β1,2Xt –2 +……+mm2,t………...(3.2)

Xt =β1,1Xt –1 + β1,2Xt –2 +……+mm1,t………………………………….(3.3)

Yt =qq2,1Yt –1 + qq2,2Yt- 2 + …+mm2,t………………………………………………………(3.4)

Where the Xt ( interest rates) Consumer price index and the exchange rate) which are the

exogenous variables and the Yt ( industrial share index)which is the endogenous variable

Equations (3.1) and (3.2) are called unrestricted whilst (3.3) and (3.4) are restricted.

According to Granger’s definition of causality:

Y does not cause X if and only if β2,1=β2.2=………=β2,p=0…….. (3.5)

X does not cause Y if and only if β1,1=β1,2=………=β1,p=0……. (3.6)

16

3.2 Diagnostic TestsDiagnostic tests are conducted on a model in order to determine whether any assumptions of the

normal regression model are violated. In this research the researcher will conduct the unit roots

and cointegration tests. However if there is any cointegration relationship it can re-parameterised

as an Error-Correction Model. So if the variables in the equation are cointergrated the researcher

will as a matter of fact use ECM which will contain short and long run effects. However if there

is cointergration there should Granger causality in at least one direction. Hence if the researcher

find that there is cointergration he will then test the direction of causality using Granger

causality.

3.2.1 Unit Roots TestsMost time series in economics demonstrates a trend over time. These time series are not

stationery implying that they don’t satisfy the requirements of weak stationery. Hence the unit

root test is viewed as the initial stage to determine whether the stock market (represented by the

industrial index) and the macroeconomic variables in question are a stationery process. The

researcher cannot do away with unit root tests since their existence can lead to spurious

regression. Spurious regression implies that relationship between variables can look statistically

significant but in actual fact there will be no meaningful relationship among the variables.

Basically there are various methods for testing unit roots and these include: Augmented-Dickey

Test (ADF), extension to the dickey fuller test for example Pantula tests, Phillips Peron tests,

Kwaitowski-Phillips-Schmidt-shin (KPS), Elliot-Rothenberg-stock point optimal (ERS) as Ng-

Perron tests. However in all the cases the idea is to search for data generating process(DGP) pure

random walk, random walk with a drift and random walk with a drift and time trend. The

researcher will implement the ADF tests since they are relatively easy to understand and

compatible with E-Views 3.

3.2.2 Integration and Cointegration TestsThe researcher will also conduct integration and cointegration tests. Cointegration can generally

be defined as an econometric concept which mimics the existence of the long run equilibrium

relationship among econometric variables. Cointegration tests are important in determining the

presence and nature of equilibrium economic relationship.

17

3.2.3 Causality TestsThe researcher chooses to employ the Sims (1972) test, based on Granger’s (1969) definition of causality. In Sims approach, a Granger causality relationship is expressed in two pairs of regression equations by simply twisting independent and dependant variables as follows:.

With Sims test, the direction of causality is judged as follows:

The result of F test Direction of causality

(a) (3.5) holds, (3.6) does not hold : X causes Y (X®®Y)

(b) (3.5) does not hold, (3.6) holds : Y causes X (Y®®X)

(c). Neither (3.5) nor (3.6) hold : Feedback between X and Y (X««Y)

(d) Both (3.5) and (3.6) hold : X and Y are independent

3.3 Justification of Variables

As alluded to above in the model specification, the researcher shall not use all of the variables

that can explain the performance of the stock market index but a few shall be used due to time

and cost of data gathering restrictions.

3.3.1 Exchange RatesExchange rates are hypothesized to have a positive relationship with the stock prices. Assuming

that the Marshal –Lerner condition holds , a depreciation in the Zimbabwean dollar will lead to

an increase in the demand for the Zimbabwean exports their by increasing cash flows to the

country. According to Makherjee and Naka (1995) if the local currency is expected to appreciate

the market will attract invest and through the contagion effect among macro economic variables,

the rise in demand will push up the stock market level suggesting a positive correlation among

exchange rates and the stock market.

3.3.2 Consumer Price Index (As a Measure of Inflation)Previous studies by Fama and Schwert (1977), Jaffe and Mendelker (1976) pointed to a positive

relationship between inflation and stock prices. The researcher therefore hypothesized a positive

relationship when he said an increase in the rate of inflation is likely to lead to economic

tightening policies which in turn increase the nominal risk free rate and hence the discount rate in

18

the discount valuation model. Ceteris paribus, inflation will move the stock market indices in the

opposite direction through the opposite movements of assets. Hence a negative impact of

inflation on the stock market is expected. The researcher employs the quarterly inflation rates as

measured by the consumer price index.

3.3.3 Interest RatesSince the RBZ and the financial sector at large keep their information confidential, the researcher

faced an impossible task to obtain continuous flowing rate hence he employed the 90 day

Treasury bill rate obtained from the RBZ website. The Treasury bill rate has been employed by

many researchers among them Gunasekarage et al (2004) in their study in Sri Lanka. Basically,

the interest qualifies as a bench mark for all other interest rates and given the obvious fact that

interest rates move together in the same direction in an economy, the Treasury bill rate can be a

best proxy of interest rates movements. The Treasury bill rate is a better estimate for the nominal

interest rates since it takes into consideration the opportunity cost of holding stock instead of

other short term money market instruments that are relatively liquid. This rate offers an

alternative destination for saving to investors who would compare the yield in the stock market

and the yield in the bond market.

Interest rates can result in a bullish market since low interest rates can lead to an outflow of

funds from the fixed to the variable yield market. Given that there is a positive relationship

between nominal interest rates and the risk free return of the valuation models, nominal interest

rates should move the stock market indices in an opposite direction. Hence a negative

relationship between Treasury bill rate and stock market indices.

3.3.4 The Stochastic Error Term (The Disturbance Term)Including this variable allows the model to be stochastic. This error term captures explicitly the

size of some errors or misses in our model. One can justify the error term on the basis that it

measures inaccuracy of some measured variables. In addition it also captures human

indeterminacy. Lastly it can cater for the omission of innumerable chance events.

Summing up the arguments in the above discussion, the following signs are expected for the

specified model:

19

Stock Market Index = f (TBR, CPI, , ER)

- - +

3.4 Data Characteristics

The study uses the industrial index as the dependent variables and three macroeconomic

variables as the independent variables for the Zimbabwean economy ranging from 1995(1) to

2007(4). The data is collected from various publications of the Reserve Bank of Zimbabwe,

Central Statistical Office, ZSE and IMARA. In an effort to limit the volatility of the data, the

study uses quarterly data. In years where there is only monthly data, an average figure is

calculated for each quarter. The researcher used Lisman and Sandee (1964) technique that was

also used for generating quarterly data by Kereke( 1996.)

Variables to represent the industrial sector index, , interest rate, exchange rate and inflation are

respectively the ind, TB rate, ER (Z$X/US$1)and CPI. It should be noted that the exchange rate

is defined as Zimbabwean dollars (Z$) per unit of the United States of America dollar (US$).

The USA dollar is used because most international transactions are quoted in USA dollars.

3.5 Strengths and Weaknesses of the ModelThe model used [Sims(1972) multivariate model] is advantageous in that it include many

variables that influence the stock prices hence increases the predicting power of the model.

However this model though it is multivariate in nature, excludes some variables which can be

important in influencing stock prices. These variables can include political climate and policies

in the country, money supply etc.

3.5 ConclusionGiven the necessary information or data, it will then be possible to give the direction of causality

between the ZSE performance and the macroeconomic variables in question following the

procedures stated and provide outcomes that will help the formulation of policies that will

stimulate the resuscitation of the Zimbabwe economy. Results of this study are presented in

chapter 4

20

CHAPTER FOUR

DATA PRESENTATION

4.0 IntroductionAnalysis and presentation of findings of the previous chapter are dealt with in this episode. The

results to be presented in this chapter have been obtained using E-Views 3.1 version. This

chapter will make use of tables to summarize the results. However full results are results are

presented in appendix B.

4.1.0 Determining the Optimal Lag Lengthy: Akaike information criterion and Schwarz

criterion

Many researchers had tested causality up to the fourth lag. In addition most of them used the rule

of thumb that lags shoulb be at least one third to one quarter o the sample size , Gujarati (2005).

However the researcher used the Akaike and Schwarz criterions.

Table 1: Optimal Lag Length

Lag 0 1 2 3 4 5

AIC 47.00549 41.51206 41.06884 37.226 30.55358 43.52028

SC 47.1579 41.81799 41.37771 37.53815 30.8812 43.83830

Using the Akaike information criterion and the Schwatz criterion the researcher would use a

maximum of four (4) lags in testing the direction of causality. This is because at four lags both

the AIC and the SC were at their lowest. Also it is after the fourth lag that both values began to

increase

4.1 Unit Roots Tests and Cointegration Results All the variables were subjected to unit roots tests and the findings are presented fully in

appendix B:The table below illustrates the results from unit roots tests:

21

Table 2: Results of Unit Root Tests

Variable ADF statistic Critical value Level of stationarity

Industrial index 9.363213 1.9473 I(0)

Consumer price index 16.93724 -1.9473 I(0)

Interest rates -6.700062 -1.9474 I(1)

Exchange rates 3.743794 -1.9474 I(0)

Εt -6,40992 -3.915304* I(0)

*The value was calculated using the McKinnon(1991) method.

I(0) implies that the variable is stationery at level and hence it has no unit roots and I(1) indicates

that the variable has one unit root and will become stationery after its first difference.

Since all the variables are not I(0)-the 90 day Treasury bill rate becoming stationery after the first

difference while other variables are stationery at their level, there is a possibility that the

variables are cointegrated. Put it differently, there exist a long term equilibrium relationship

between the variables in question. The researcher runs the OLS on the residual (see appendix B

for detailed information). He then used the McKinnon’s (1991) method of calculating critical

values .He then used the calculated critical values to compare with the ADF statistic from E-

Views. For the residual to be stationery be stationery, the ADF statistic should be greater than the

calculated level of significance. McKinnon’s formula can be stated as:

C(p)=𝛟∞+𝛟1(T-1)+𝛟2(T-2)

Where p is the probability which is usually at 5%, the 𝛟s are obtained from the McKinnon values and are found in the tables and n is the sample size. The researcher calculated the critical values as below:

C(5%)=(-3.7429)+(-8.352)(50-1)+(-13.41)(50-2)

=-3.91530049

In this research since 6.40901>-3.91530049 this implied that the residual is stationery. Since the residual is stationery this reinforced the notion that there is a long run equilibrium relationship between the variables. If there is a cointergration relationship this would imply that there should be Granger causality in at least one direction.Gujarati (2005)

22

To enable the researcher to establish the causal link between the macroeconomic variables and the Zimbabwe stock exchange industrial index, causality tests were employed and the results were as follows. The researcher tested the direction of causality up to four lags.

4.2 Results Presentation

H0: macroeconomic variables do not granger cause ZSE index

H1:macroeconomic variables granger cause ZSE index

Table 3 Causality Results

Direction of causality

Number of lags

f-critical (5%) f-calculated Probability Decision

ind→ir 1 2.76 3.60537 0.06374 Reject H0

ir→ind 1 2.76 0.00037 0.98473 Accept H0

cpi→ind 1 2.76 61.4928 4.4E-10 Reject H0

ind↔cpi 1 2.76 0.24624 0.62205 Accept H0

er→ind 1 2.76 18.9361 0.00000 Reject H0

ind→er 1 2.76 6609.98 0.00000 Reject H0

Ind→ir 2 2.76 2.21573 0.12110 Accept H0

ir→ind 2 2.76 0.17016 0.84408 Accept H0

cpi→ind 2 2.76 4925.29 0.00000 Reject H0

ind→cpi 2 2.76 210.750 0.00000 Reject H0

er→ind 2 2.76 10.6683 0.00017 Reject H0

ind→er 2 2.76 3733.37 0.00000 Reject H0

cpi→ind 3 2.84 310.027 0.00000 Reject H0

ind→cpi 3 2.84 208.120 0.00000 Reject H0

er→ind 3 2.84 1341.36 0.00000 Reject H0

ind→er 3 2.84 315017. 0.00000 Reject H0

ir→ind 3 2.84 64.1310 1.6E-15 Reject H0

ind→ir 3 2.84 2.60785 0.06447 Accept H0

cpi→ind 4 2.84 222.122 0.00000 Reject H0

ind→cpi 4 2.84 16.9760 4.6E-08 Reject H0

er→ind 4 2.84 190.085 0.00000 Reject H0

ind→er 4 2.84 46115.8 0.00000 Reject H0

ir→ind 4 2.84 5.35758 0.00160 Reject H0

ind→ir 4 2.84 0.88734 0.48081 Accept H0

To come up with the direction of causality, the researcher compared the F-critical at 5% with the F-value. Alternatively this could be done by comparing the F-value with the probability and one will obtain the same value.

Table 4: Direction of Causality

variables lag Direction of causalityIndustrial index and cpi 1 Uni-directional causality from

23

CPI to industrial index2 Bi-directional causality(ind↔cpi)3 Bi-directional causality(ind↔cpi)4 Bi-directional causality(ind↔cpi)

Industrial index and exchange rate

1 Bi-directional causality(ind↔er)

2 Bi-directional causality(ind↔er)3 Bi-directional causality(ind↔er)4 Bidirectional causality(ind↔er)

Industrial index and interest rates.

1 Unidirectional causality(ind→ir)

2 Bi-directional causality(ind↔ir)3 Uni-directional causality(ir→ind)4 Uni-directional

causality(ir→ind)the

4.2 Interpretation of ResultsFrom the unit root tests of the variables and the residuals, it can be seen that the variables has a long run equilibrium relationship since they are cointegrated. Industrial index, consumer price index and exchange rates are stationery at level. This implies that they have no unit roots. Interests rates on the other hand are intergrated of order one. This means that they are difference stationery and become stationery after differencing once. In addition since they are cointegrated this suggests that they have Granger causality at least in one direction, Gujarati (2005)

Tables 3 and 4 indicate that the direction of causality depends critically on the number of lagged terms included. If one takes consumer price index for example; when one lag is included, causality runs from C.P.I to ZSE index(uni-directional causality) but as the number of lags are increased then the is bi-directional causality

Wickremasinge (2006) shows reported that there is bidirectional causality between short term interest rates and United States of America stock prices. However in this research the student found a unidirectional causality between the short term interest rates and the Zimbabwean stock market. This maybe mainly due to the fact that the macroeconomic conditions in Zimbabwe during the period under study was so volatile that they cannot be matched with those in the US which were stable during the period under study. The causality results between interests rates and the Zimbabwe stock exchange indicates that during the period under study, information about interest rates could be used to predict the stock prices. However it was impossible to use information about stock prices to predict the nominal short term interest rates.

Since there is a bi-directional(feedback) causality between industrial index, consumer price index and exchange rates it implies that it was possible to predict say stock prices given the information on exchange rates or consumer price index and vice versa.

24

Clearly it can be seen that there is causality in at least one direction from the above scenarios. This therefore indicates causation. Put it differently one can use information about one variable to predict future movements in another. Thus one can use information from consumer price index to predict future movements in the ZSE index and vice versa.

4.3 Conclusion

Results from this research indicates that the industrial index, exchange rates, consumer price

index are integrated of order zero[I(0)] and the 90 day treasury bill rate is integrated of order one

implying that it has one unit root. Residual tests reveal that the residuals are stationery, another

strong indication that they are co integrated .causality tests indicate that there is causality

between the stock market and the macroeconomic variables in question even though the direction

of causality differs with each variable.

CHAPTER FIVE

RECOMMENDATIONS and CONCLUSIONS

5.0 Introduction

25

This chapter closes the study by giving a summary of research findings, pointing their

implication and also proposing policy recommendations drawn directly from the study. It also

attempts to compare the objectives of the study with the study findings. This will enable the

researcher to establish whether the objectives were met. In addition, the limitations of the study

are given together with suggestion for future study.

5.1 Policy Recommendations

Causal relationship identified in this particular study is a crucial step towards giving information

to policy makers to enhance informed formulation, effective implementation and review of

macroeconomic policies.

Since there is a causal link between C.P.I and ZSE index, it is recommended that policies

targeting inflation should not be independent from policy that regulates the activities of the

stock market. It is recommended that under the hyperinflationary environment such as those

that existed during most of the period under study, policies to reduce inflation should be

compatible with the efforts to tame the optimistic behavior at the stock market which exert

upward pressure on inflation rate.

Since unfavorable interest rates can drive the suppliers of loanable funds out of the money

market given that the stock market is a safe haven. Usually monetary policies which require

cutting down of interest rate will be self defeating vis-à-vis stock market dealings. The

researcher recommends that monitory authorities therefore should not cut the interests rates

but peg them in such a way that they act as an incentive for savings. In this way funds will

not be diverted to the stock market. In this way the demand for stocks will be relatively low

hence not exerting pressure on the stock prices to rise.

Since stock markets and the exchanges rates have a causal link, the government should allow

the floating exchange rate to rule in the economy. Thus fixing the exchange rate can give

wrong information on the future price movements on the stock market. As a result due to the

operation of the fixed exchange rate regime in Zimbabwe in 2006 and 2007 it was difficult to

use the exchange rate to predict stock prices movements on the Zimbabwe stock exchange.

Since pegging the exchange rate will lead to a wide variance between the black market rate

and the official exchange rate, people tend to buy and sell their foreign currency on the black

26

market. The proceeds from the black market will be used to buy stocks on the equities

market. This will exert pressure on the share prices to rise. Thus the stock exchange would

end up performing superbly even under unfavorable macroeconomic conditions. Hence I

recommend the authorities to operate the freely floating exchange rate regime.

5.2 Suggestions for Future Research

The study of causality in Zimbabwe should be extended to the period of the use of multiple

currencies and the period of economic recovery that is now characteristic of the Zimbabwean

economy. Future researchers should compare whether there is any deviations and differences

in results obtained in the pre-dollarization era and the post dollarization era.

In addition, future researchers should also analyze the short-run interaction between the

performance of the ZSE and macroeconomic variable. Most researchers looking at the causal

link between the stock market performance and macroeconomic variables focuses mainly on

long run interactions. Therefore future researchers can use techniques such as Variance

Decompositions and Impulse response Functions to look at the short run relationships.

Furthermore future researchers can also study the dynamic interactions using daily data since

most researchers were using most notably annual, monthly and quarterly data. This will give

more accurate results since daily figures do not deviate from each other greatly as compared

to annual or even monthly data.

More so, future researchers should include as many variables as they can. Thus future

researchers can include even variables like the legal and political environment since these

have great influence on the stock market performance.

5.4 Summary

This research looked at the causal relationship between macroeconomic variables and the

ZSE index. Chapter one looked at the introduction to the study. It considered mainly the

background of the study,research hypothesis,statement of the problem as well as objectives

of the study. Chapter two looked at the empirical and theoretical literature view. It supported

the research with theoretical and empirical postulations that supported the current study.

27

In chapter four the researcher looked at the methodology to be employed. chapter five looked

at the presentation of the data and analysis of it.

5.3 ConclusionsThis study was carried out to establish the causal link between the ZSE and the macroeconomic

variables (exchange rates, consumer price index and short term nominal interest rates. The period

of study stretches from 1995(i) to 2007(iii) and makes use of quarterly data.

Given the empirical findings a number of conclusions can be deduced. The existence of long run

relationship between the Zimbabwean stock exchange and macroeconomic variables indicates

the long-run predictability of the Zimbabwean equity prices. Put it differently, movements in the

ZSE equity prices are tied to the long-run movements in economic fundamentals. Since the

money market in Zimbabwe has negative real returns (average yield was far below inflation

rates) over the period under study the stock market remained as a hedge against inflation. Also

the causality stock market and CPI is crucial evidence that supports bullish behavior on the local

bourse. In addition stock market bubbles are evident.

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www.zse.co.zw

APPENDIX

Appendix A

Raw data

DATE ER CPI IR INDMar-95 8.44 11.50 68.72 2,942.03Jun-95 8.55 11.76 68.68 3,614.10

33

Sep-95 8.80 12.73 55.50 3,808.38Dec-95 9.31 13.29 61.24 3,972.62Mar-96 9.59 14.22 13.40 4,828.69Jun-96 9.86 14.40 15.52 5,404.42Sep-96 10.31 15.36 18.17 6,992.15Dec-96 10.82 15.49 27.63 8,786.26Mar-97 11.24 17.06 31.71 10,169.97Jun-97 11.36 17.48 30.70 10,437.40Sep-97 12.45 17.56 30.25 9,804.02Dec-97 17.69 18.57 26.03 7,196.43Mar-98 16.17 21.83 43.50 8,795.71Jun-98 18.01 22.69 58.34 7,417.93Sep-98 31.41 23.13 55.17 5,730.73Dec-98 37.31 27.23 79.75 6,408.40Mar-99 38.17 33.34 106.53 8,975.30Jun-99 38.01 35.23 162.85 9,829.47Sep-99 38.16 39.25 162.85 11,825.41Dec-99 38.14 42.72 133.00 14,426.64Mar-00 38.15 50.28 95.08 14,759.84Jun-00 38.19 56.11 150.00 15,200.66Sep-00 52.53 63.58 55.50 19,196.83Dec-00 55.06 66.31 61.24 17,984.33Mar-01 55.09 80.50 13.40 29,197.63Jun-01 55.07 94.80 15.52 39,484.24Sep-01 55.04 121.70 18.17 47,714.14Dec-01 55.04 144.60 27.63 45,351.89Mar-02 55.04 171.80 31.71 48,090.75Jun-02 55.04 203.50 30.70 77,232.98Sep-02 55.04 292.00 30.25 99,520.98Dec-02 55.04 432.30 26.03 103,495.09Mar-03 532.58 563.40 43.50 179,530.62Jun-03 826.45 945.10 58.34 277,301.90Sep-03 826.45 1,622.30 55.17 648,932.85Dec-03 826.45 3,019.90 79.75 401,542.93Mar-04 4,314.50 3,852.00 106.53 347,708.43Jun-04 5,346.90 4,674.10 162.85 693,147.07Sep-04 5,615.20 5,702.90 162.85 871,123.53Dec-04 5,712.65 7,028.70 133.00 1,097,492.53Mar-05 6,082.06 8,616.90 95.08 2,483,961.01Jun-05 9,899.14 12,354.20 150.00 2,856,530.00Sep-05 26,003.66 26,224.60 265.00 6,176,377.29Dec-05 84,587.57 48,205.60 340.00 18,483,883.97Mar-06 99,201.58 87,340.90 420.00 31,045,930.90Jun-06 100,916.00 420,000.00 510.00 54,873,198.61Sep-06 259,576.00 1,125,000.00 66.30 388,686,440.00Dec-06 258,920.00 2,800,000.00 66.30 569,864,000.00Mar-07 259,116.00 3,475,000.00 112.50 4,026,437,840.00Jun-07 255,549.00 5,300,000.00 280.00 43,133,619,830.00Sep-07 30,705,000.00 7,983,000.10 365.00 86,494,551,710.00

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Appendix B

UNIT ROOTS

B1 :AUGUMENTED DICKEY FULLER UNIT ROOTS TESTS ON CPI

ADF Test Statistic 16.93724 1% Critical Value* -2.6090 5% Critical Value -1.9473 10% Critical Value -1.6192

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test EquationDependent Variable: D(CPI)Method: Least SquaresDate: 04/14/10 Time: 05:23Sample(adjusted): 1995:2 2007:3Included observations: 50 after adjusting endpoints

Variable Coefficient

Std. Error t-Statistic Prob.

CPI(-1) 0.498679 0.029443 16.93724 0.0000R-squared 0.839944 Mean dependent var 159659.8Adjusted R-squared 0.839944 S.D. dependent var 517563.4S.E. of regression 207061.7 Akaike info criterion 27.33922Sum squared resid 2.10E+12 Schwarz criterion 27.37746Log likelihood -682.4805 Durbin-Watson stat 2.157633

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B2 :AUGUMENTED DICKEY FULLER UNIT ROOTS TESTS ON EXCHANGE RATES



Augmented Dickey-Fuller Test EquationDependent Variable: D(ER)Method: Least SquaresDate: 04/14/10 Time: 05:26Sample(adjusted): 1995:2 2007:3Included observations: 50 after adjusting endpoints



ER(-1) 26.44134 7.062712 3.743794 0.0005R-squared 0.206278 Mean dependent var 614099.8Adjusted R-squared 0.206278 S.D. dependent var 4305531.S.E. of regression 3835844. Akaike info criterion 33.17747Sum squared resid 7.21E+14 Schwarz criterion 33.21571Log likelihood -828.4369 Durbin-Watson stat 1.324569

B3 :AUGUMENTED DICKEY FULLER UNIT ROOTS TESTS ON INDUSTRIAL INDEX



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Augmented Dickey-Fuller Test EquationDependent Variable: D(IND)Method: Least SquaresDate: 04/14/10 Time: 05:29Sample(adjusted): 1995:2 2007:3Included observations: 50 after adjusting endpoints



IND(-1) 1.081310 0.115485 9.363213 0.0000R-squared 0.625076 Mean dependent var 1.73E+0

9Adjusted R-squared 0.625076 S.D. dependent var 8.17E+0

9S.E. of regression 5.00E+09 Akaike info criterion 47.52451Sum squared resid 1.23E+21 Schwarz criterion 47.56275Log likelihood -1187.113 Durbin-Watson stat 2.017351

B4 :AUGUMENTED DICKEY FULLER UNIT ROOTS TESTS ON INTERESTS RATES (90 DAYTREASURY BILL RATE)

ADF Test Statistic -6.700062 1% Critical Value* -2.6100 5% Critical Value -1.9474 10% Critical Value -1.6193


Augmented Dickey-Fuller Test EquationDependent Variable: D(IR,2)Method: Least SquaresDate: 04/14/10 Time: 23:19Sample(adjusted): 1995:3 2007:3Included observations: 49 after adjusting endpoints



D(IR(-1)) -0.978616 0.146061 -6.700062 0.0000R-squared 0.483134 Mean dependent var 1.735510Adjusted R-squared 0.483134 S.D. dependent var 110.3791S.E. of regression 79.35532 Akaike info criterion 11.60595Sum squared resid 302268.8 Schwarz criterion 11.64455Log likelihood -283.3457 Durbin-Watson stat 1.973128

37

B5:UNIT ROOT TESTS FOR RESIDUAL

ADF Test Statistic -6.409017 1% Critical Value* -2.6090 5% Critical Value -1.9473 10% Critical Value -1.6192


Augmented Dickey-Fuller Test EquationDependent Variable: D(RESIDUAL)Method: Least SquaresDate: 04/14/10 Time: 23:33Sample(adjusted): 1995:2 2007:3Included observations: 50 after adjusting endpoints



RESIDUAL(-1) -0.911848 0.142276 -6.409017 0.0000R-squared 0.456011 Mean dependent var -

7211821.Adjusted R-squared 0.456011 S.D. dependent var 4.97E+0

9S.E. of regression 3.66E+09 Akaike info criterion 46.90025Sum squared resid 6.57E+20 Schwarz criterion 46.93849Log likelihood -1171.506 Durbin-Watson stat 1.974892

38

Appendix C

CAUSALITY TESTS

Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:34Sample: 1995:1 2007:3Lags: 1 Null Hypothesis: Obs F-Statistic Probability CPI does not Granger Cause IND 50 61.4928 4.4E-10 IND does not Granger Cause CPI 0.24624 0.62205

Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:36Sample: 1995:1 2007:3Lags: 1 Null Hypothesis: Obs F-Statistic Probability ER does not Granger Cause IND 50 18.9361 7.2E-05 IND does not Granger Cause ER 6609.98 0.00000

Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:37Sample: 1995:1 2007:3Lags: 1 Null Hypothesis: Obs F-Statistic Probability IR does not Granger Cause IND 50 0.00037 0.98473 IND does not Granger Cause IR 3.60537 0.06374

39

Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:38Sample: 1995:1 2007:3Lags: 2 Null Hypothesis: Obs F-Statistic Probability CPI does not Granger Cause IND 49 4925.29 0.00000 IND does not Granger Cause CPI 210.750 0.00000

Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:39Sample: 1995:1 2007:3Lags: 2 Null Hypothesis: Obs F-Statistic Probability ER does not Granger Cause IND 49 10.6683 0.00017 IND does not Granger Cause ER 3733.37 0.00000


Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:42Sample: 1995:1 2007:3Lags: 3 Null Hypothesis: Obs F-Statistic Probability CPI does not Granger Cause IND 48 310.027 0.00000 IND does not Granger Cause CPI 208.120 0.00000

Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:42Sample: 1995:1 2007:3Lags: 3 Null Hypothesis: Obs F-Statistic Probability ER does not Granger Cause IND 48 1341.36 0.00000

40

IND does not Granger Cause ER 315017. 0.00000

Pairwise Granger Causality TestsDate: 04/20/10 Time: 00:45Sample: 1995:1 2007:3Lags: 3 Null Hypothesis: Obs F-Statistic Probability IR does not Granger Cause IND 48 64.1310 1.6E-15 IND does not Granger Cause IR 2.60785 0.06447

Pairwise Granger Causality TestsDate: 04/18/10 Time: 11:09Sample: 1995:1 2007:3Lags: 4 Null Hypothesis: Obs F-Statistic Probability CPI does not Granger Cause IND 47 222.122 0.00000 IND does not Granger Cause CPI 16.9760 4.6E-08


Pairwise Granger Causality TestsDate: 04/18/10 Time: 11:11Sample: 1995:1 2007:3Lags: 4 Null Hypothesis: Obs F-Statistic Probability ER does not Granger Cause IND 47 190.085 0.00000 IND does not Granger Cause ER 46115.8 0.00000

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