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REAL STOCK RETURNS, VOLATILITY AND REAL ECONOMIC
ACTIVITY: EVIDENCE FROM NIGERIA
RUFUS AYODEJI OLOWE1
This paper uses the VAR model to investigate the inter relationship between real
economic activity, real stock returns, real economic activity volatility and real stock
return volatility in Nigeria using quarterly data over the period 1985.2 to 2009.3 in
the light of financial reforms, stock market crash and the global financial crisis.
EGARCH (1, 2) and EGARCH (1, 3) were used to estimate real economic
activity and real stock return volatilities respectively. The results showed that
there is no causal relationship between real economic activity and real stock
returns. This is consistent with the results of Binswanger (2000, 2004) and Mao
and Wu (2007).
The results also show there is a one-way causality going through from real
economic activity to real economic activity volatility; real economic activity to
real stock return volatility; real stock returns to real stock return volatility; and
real economic activity volatility to real stock return volatility. The results showed
that past volatilities in real stock returns and real economic activity negatively
influenced real economic activity volatility. The results also show that private
sector credit influences real economic activity. The result also shows that real
treasury bill yield negatively influences real economic activity volatility. The
results also show that the introduction of new capital requirement for banks and
introduction of new capital requirement for insurance influences real stock
returns. The result also show that the global financial crisis influences real stock
return volatility.
Policy makers should introduce policies towards increasing real output of
companies in Nigeria. The quality of financial reporting of companies in Nigeria
should also be enhanced.
Field of Research: Stock market, Economic Activity, EGARCH, VAR, Granger causality,
Financial Reforms, Global Financial crisis, Stock market crash
JEL: O40, G1
1.
INTRODUCTION
The debate on whether the stock market can serve an important indicator for the
prediction of future economic activity or vice versa has been well documented. Most of
the authors focused on the inter relationship between stock prices and real economic
activity. However, the dramatic change in stock market volatility during financial crises
(such as the 1987 stock market crash, the 1997 East Asia crisis, and the 1998 Russian
financial crisis) and periods of uncertainty (such as the 1962 Cuban missile crisis) has made
it necessary to examine also the impact of stock market volatility on the economy (Guo,
2002) . The recent global financial crisis and associated declines in economic activity
experienced by a number of emerging market economies has made it imperative to
1
Associate Professor of
[email protected]
Finance,
University
of
Lagos,
Akoka,
Lagos,
Nigeria.
E-mail:
examine the links between stock prices, and real economic activity or output and their
volatilities.
Engle (1982) introduced the autoregressive conditional heteroskedasticity
(ARCH) to model volatility. Engle (1982) modeled the heteroskedasticity by relating the
conditional variance of the disturbance term to the linear combination of the squared
disturbances in the recent past. Bollerslev (1986) generalized the ARCH model by
modeling the conditional variance to depend on its lagged values as well as squared
lagged values of disturbance, which is called generalized autoregressive conditional
heteroskedasticity (GARCH). Since the work of Engle (1982) and Bollerslev (1986), the
financial econometrics literature has been successful at measuring, modeling, and
forecasting time-varying return volatility which has contributed to improved asset pricing,
portfolio management, and risk management, as surveyed for example in Andersen,
Bollerslev, Christoffersen and Diebold (2006a, 2006b).
Schwert (1989a) argues that stock market volatility, by reflecting uncertainty about
future cash flows and discount rates, provides important information about future economic
activity. Campbell et al. (2001), citing work by Lilien (1982), reason that stock market
volatility is related to structural change in the economy. Structural change consumes
resources, which depresses gross domestic product (GDP) growth. Another link between
stock market volatility and output rests on a cost-of-capital channel. That is, an increase in
stock market volatility raises the compensation that shareholders demand for bearing
systematic risk. The higher expected return leads to the higher cost of equity capital in the
corporate sector, which reduces investment and output. Consistent with these
hypotheses about the link between stock market volatility and economic activity,
Campbell et al. (2001) show that—after controlling for the lagged dependent variable—
stock market volatility has significant predictive power for real GDP growth. Bhide (1993)
argues that speculations and volatility in stock markets may reduce investment
efficiency, which has detrimental effect to economic growth. Mauro (1995) indicate that
the development of stock market will reduce economic growth through decreasing the
public's precautionary savings.
Guo (2002) shows that if the cost of capital is the main channel through which
volatility affects future output, stock market returns have a more important role in
forecasting economic activity than volatility does. Several empirical studies found a strong
relationship between stock returns and real economic activity( Fama,(1981; Fischer and
Merton,1984; Barro, 1990; Fama, 1990; Schwert, 1990; Peiro, 1996; Domian and
Louton, 1997; Foresti, 2006; Choi et al., 1999 ; and Hassapis and Kalyvitis, 2002).
Some other studies provided evidence that stock market performance is not correlated
with real economic activity (Stock and Watson,
1990, 1998; Ffu, 1993).
Binswanger (2000, 2004) and Mao and Wu (2007) argued that the that the relation
between stock returns and real economic activity in the US has disappeared since the
early 1980's indicating that stock return ceased to lead real economic activity. Most of
the studies discussed so far are for developed economies. Few studies have been
conducted for emerging economies on the interrelationship between stock returns and
real economic activity. Rangvid (2001) provided evidence on the relation between stock
return and real economic activity for several emerging economies. Mauro (2003)
provided evidence for emerging and advanced countries while Kaplan (2008) provided
evidence for Turkey. Little or no work has been done on the relation between stock
returns or its volatility on real economic activity in Nigeria. This paper attempts to fill this
gap.
The recapitalization of the banking industry in Nigeria in July 2004 and the
Insurance industry in September 2005 boosted the number of securities on Nigerian
stock market increasing public awareness and confidence about the Stock market. The
increased trading activity on the stock market could have affected the volatility of the
stock market. However, since 1 April 2008, investors have been worried about the
falling stock prices on the Nigerian stock market.
The global financial crisis of 2008, an ongoing major financial crisis, could have
affected stock volatility. The crisis which was triggered by the subprime mortgage crisis
in the United States became prominently visible in September 2008 with the failure,
merger, or conservatorship of several large United States-based financial firms exposed
to packaged subprime loans and credit default swaps issued to insure these loans and
their issuers. On September 7, 2008, the United States government took over two
United States Government sponsored enterprises Fannie Mae (Federal National
Mortgage Association) and Freddie Mac (Federal Home Loan Mortgage Corporation)
into conservatorship run by the United States Federal Housing Finance Agency
(Wallison and Calomiris, 2008; Labaton and Andrews, 2008). The two enterprises as at
then owned or guaranteed about half of the U.S.'s $12 trillion mortgage market. This
causes panic because almost every home mortgage lender and Wall Street bank relied
on them to facilitate the mortgage market and investors worldwide owned $5.2 trillion of
debt securities backed by them. Later in that month Lehman Brothers and several other
financial institutions failed in the United States (Labaton, 2008). The crisis rapidly
evolved into a global credit crisis, deflation and sharp reductions in shipping and
commerce, resulting in a number of bank failures in Europe and sharp reductions in the
value of equities (stock) and commodities worldwide. In the United States, 15 banks
failed in 2008, while several others were rescued through government intervention or
acquisitions by other banks (Letzing, 2008). The financial crisis created risks to the
broader economy which made central banks around the world to cut interest rates and
various governments implement economic stimulus packages to stimulate economic
growth and inspire confidence in the financial markets. The financial crisis dramatically
affected the global stock markets. Many of the world's stock exchanges experienced the
worst declines in their history, with drops of around 10% in most indices ( Kumar, 2008).
In the US, the Dow Jones industrial average fell 3.6%, not falling as much as other
markets (Cox, 2008). The economic crisis caused countries to temporarily close their
markets.
This paper investigated the inter relationship between real economic activity, real
stock returns, real economic activity volatility and real stock return volatility in Nigeria using
quarterly data over the period 1986.4 and 2009.3 in the light of the structural adjustment
programme, banking reforms, insurance reform, stock market crash and the global
financial crisis. The rest of this paper is organised as follows: Section two discusses an
overview of the Nigerian stock market while Section three discusses Theoretical
background and literature. Section four discusses methodology while the results are
presented in Section five. Concluding remarks are presented in Section six.
2.
OVERVIEW OF THE NIGERIAN STOCK MARKET
The Nigerian Stock Exchange (NSE) which started operation in 1961 with 19 securities
has grown overtime. As at 1998, there are 264 securities listed on the NSE, made up of
186 equity securities and 78 debt securities. By 2008, the number of listed securities
has increased to 301 securities made up of 213 equity securities and 88 debt securities.
Table 1 highlights the trends in the number of listed securities on the Nigerian Stock
market. Table 2 shows the trend in the trading transactions in the Nigerian stock
market. Between 1980 and 1987, there was hardly any trading transaction on the equity
market. Government and industrial loan stocks dominated the transactions on the
Nigerian stock market (Olowe, 2008). Table 2 shows that the value of equity traded as a
proportion of total value of all securities traded, equity traded as a proportion of total
market capitalisation and equity traded as a proportion of GDP are all zero between
1971 and 1987. However, since 1988 the value of equity traded transaction has been
increasing in the Nigerian stock market (Olowe, 2008). Table 2 shows that equity
traded as a proportion of total value of all securities traded grew from 0.7348 in 1988 to
0.9988 in 1998 and to 0.9973 in 2007. Table 2 shows that between 1988 and 2005, the
equity market is still small relative to the size of the stock market. The value of equity
traded as a proportion of total market capitalisation was 0.0624 in 1988 but fell to
0.0022 in 1989. Since 1989, the value of equity traded as a proportion of total market
capitalisation has been fluctuating rising slightly to 0.0516 in 1998, increasing to 0.1059
in 2004 and falling to 0.0878 in 2005. On July 4, 2004, Central Bank of Nigeria
proposed banking reforms increasing the capitalisation of Nigerian banks to N25 billion.
In the process of complying with the minimum capital requirement, N406.4 billion was
raised by banks from the capital market, out of which N360 billion was verified and
accepted by the CBN (Central Bank of Nigeria, 2005). The introduction of the 2004 bank
capital requirements could have affected quoted securities on the Nigerian stock
exchange. The recapitalisation of the Nigerian banking industry and influx of banking
stocks into the Nigerian stock market made the value of equity traded as a proportion of
total market capitalisation to increase to 0.1761 in 2008.
Table 2 shows that the stock market is small relative to the size of the economy.
The value of equity traded as a proportion of GDP was 0.0023 in 1988 but fell to 0.0001 in
1989. Since 1989, the value of equity traded as a proportion of GDP has been fluctuating
rising slightly to 0.0033 in 1998, increasing to 0.0192 in 2004 and falling to 0.0171 in
2005. The recapitalisation of the Nigerian banking industry which led to influx of banking
stocks into the Nigerian stock market made the value of equity traded as a proportion of
GDP to increase to 0.0703 in 2008 (Olowe, 2008).
In sum, prior to 2001, the equity market appears to be small in Nigeria considering
the low values of both the value of equity traded as a proportion of total market
capitalisation and the value of equity traded as a proportion of GDP. The growth in the
equity could have due to some reasons such as introduction of universal banking,
introduction of code of corporate governance, introduction of new capital requirements for
banks and insurance companies.
In response to the trend in the world financial system, the imperatives of
globalisation and financial deregulation, in January 2001, the universal banking
system was adopted in Nigeria. A universal bank is an all-inclusive bank which in
addition to the traditional banking functions provides all other financial ser vices
including insurance and capital market services. On January 1, 2001, the Central
Bank of Nigeria recalled the existing licences of all the commercial and merchant
banks and issued them with new, uniform licences. The new licences allow the banks
to choose which segment(s) of the financial market (i.e., money market, capital market,
insurance business or any combination of these) they wish to operate in, after
considering and evaluating appropriately their own competencies. The adoption of
universal banking led to an increase in number of financial institutions engaged in capital
market services. The adoption of universal banking could have affected the relation
between stock return or its volatility and the real economic activity.
Table 1: Number of Securities Listed on the Nigerian Stock Exchange, 1980-2008
Year
Equity
Debt
Total
Securities
Securities
1980
91
66
157
1981
93
70
163
1982
93
75
168
1983
93
86
179
1984
94
83
177
1985
96
85
181
1986
99
87
186
1987
100
85
185
1988
102
86
188
1989
111
87
198
1990
131
86
217
1991
142
97
239
1992
153
98
251
1993
174
98
272
1994
177
99
276
1995
181
95
276
1996
183
93
276
1997
182
82
264
1998
186
78
264
1999
196
73
269
2000
195
65
260
2001
194
67
261
2002
195
63
258
2003
200
65
265
2004
207
70
277
2005
214
74
288
2006
202
86
288
2007
212
98
310
2008
213
88
301
Source:
Olowe (2009b)
.
Table 2:
Year
Trading Transactions on the Nigerian Stock Exchange
Govt.
Equities
Total
ET/TVT ET/TMC
Securities and
Industrial
Loan
1980
388.7
388.7
0
0
1981
304.8
304.8
0
0
1982
215
215
0
0
1983
397.9
397.9
0
0
1984
256.5
256.5
0
0
1985
316.6
316.6
0
0
1986
497.9
497.9
0
0
1987
382.4
382.4
0
0
1988
225.5
624.8
850.3
0.7348 0.0624
1989
582.4
27.9
610.3
0.0457 0.0022
1990
158.5
66.9
225.4
0.2968 0.0041
1991
98.7
143.4
242.1
0.5923 0.0062
1992
91.7
400
491.7
0.8135 0.0128
1993
348.2
456.2
804.4
0.5671 0.0096
1994
192.3
793.6
985.9
0.8050 0.0120
1995
50.8
1,788.00
1,838.80
0.9724 0.0099
1996
62.8
6,916.80
6,979.60
0.9910 0.0242
1997
107.9
10,222.60
10,330.50
0.9896 0.0363
1998
15.8
13,555.30
13,571.10
0.9988 0.0516
1999
0.8
14,071.20
14,072.00
0.9999 0.0469
2000
8.1
28,145.00
28,153.10
0.9997 0.0596
2001
35.6
57,648.20
57,683.80
0.9994 0.0870
2002
2.6
59,404.10
59,406.70
1.0000 0.0777
2003
6520.1
113,882.50
120,402.60 0.9458 0.0838
2004
2047.5
223,772.50
225,820.00 0.9909 0.1059
2005
8252.7
254,683.10
262,935.80 0.9686 0.0878
2006
1665
468,588.40
470,253.40 0.9965 0.0915
2007
1136.5
1,074,883.90
1,076,020.4 0.9989 0.0809
2008
3528.9
1,675,609.80
1,679,138.7
0.9979 0.1761
0
Source: Olowe (2009b); Central Bank of Nigeria Annual 0Report and Accounts, Various issues.
ET/GDP
0
0
0
0
0
0
0
0
0.0023
0.0001
0.0001
0.0003
0.0004
0.0004
0.0005
0.0006
0.0017
0.0036
0.0050
0.0044
0.0061
0.0122
0.0086
0.0134
0.0196
0.0175
0.0252
0.0520
0.0703
Notes: ET represents value of equity securities traded. TVT represents total value of all securities traded.
TMC represents Total market capitalisation. GDP represents Gross domestic prices at current prices
On September 5, 2005, the Federal Government of Nigeria announced the
recapitalization of Insurance and Reinsurance companies as N2 billion for life insurance
companies, N3 billion for non-life operators, N5 billion for composite insurance
companies and N10 billion for re-insurers (NAICOM, 2008). In the process of complying
with the minimum capital requirement, substantial money was raised by insurance
companies from the capital market.
The introduction of the new capital requirements for banks in 2004 and insurance
companies in 2005 with the prospect of increase in volume of activities on the Nigerian
stock market could have brighten the confidence of investors in the Nigerian economy
and the stock market, thus, encouraging investment in capital market securities and
increasing capital formation. They could also have affected the volatility of the stock
market.
The Nigerian Stock Exchange index has grown overtime. The index grew from
134.6 on January 1986 to 65005.48 by March 18, 2008 falling to 63016.56 by April 1,
2008. Since April 1, 2008, Nigerian stock exchange index has been falling. As at January
16, 2009, the Nigerian stock exchange index stood at 27108.54. Figure 1 shows the trend
in the Nigerian Stock Exchange index over the period January 1986 to December 2008.
Figure 1:
Trend in the Nigerian Stock Exchange Index over the period, January 1986
to December, 2008
70000
60000
50000
40000
30000
20000
10000
0
86
88
90
92
94
96
98
00
02
04
06
08
Nigerian S toc k E xchang e Index
3.
LITERATURE REVIEW
The relationship between stock prices and real economic activity has been well
documented. Various explanations have been offered as to different channels through
which stock prices are connected to real activity. Morck et al. (1990) explanations
imply that firms and managers base their investment decisions on information provided
by the stock market and the stock prices reflect the present discounted value of all
future dividends (see Kaplan (2008)). This implies that stock prices should lead the real
activity as long as stock price movements are related to fundamentals (Kaplan, 2008).
Greenwood and Smith (1997) argue that stock market performance affects real
economic activity through lowering the cost of mobilizing savings and thereby facilitating
investment in the most productive technologies. Levine (1991) and Benchivenga, Smith
and Starr ( 1996) argue that the stock markets affects real economic activity by
providing liquid capital through which they contribute to growth. Holmstrom and Tirole
(1993) argue that the stock market affects real economic activity by providing increasing
incentives to get information about firms to investors. Deveraux and Smith (1994) and
Obstfeld (1994) argue that the stock markets affects real economic activity by providing
opportunities for risk reduction through global diversification make high- risk-high return
domestic and international projects viable, and , consequently, allocate savings
between investment opportunities more efficiently. Mauro (2003) stock market affects
real economic activity by increasing the wealth of investors and hence increasing
consumption.
The volatility of stock prices in the stock market has been of concern to researchers.
Stock return volatility which represents the variability of stock price changes could be
perceived as a measure of risk faced by investors. Shiller (1981) argues that stock
prices are more volatile than what is justified by time variation in dividends. Similarly,
Schwert (1989b) concludes that stock market volatility cannot be fully explained by
changes in economic fundamentals.
Engle (1982) introduced the autoregressive conditional heteroskedasticity
(ARCH) to model volatility. Engle (1982) modeled the heteroskedasticity by relating the
conditional variance of the disturbance term to the linear combination of the squared
disturbances in the recent past. Bollerslev (1986) generalized the ARCH model by
modeling the conditional variance to depend on its lagged values as well as squared
lagged values of disturbance, which is called generalized autoregressive conditional
heteroskedasticity (GARCH). Since the work of Engle (1982) and Bollerslev (1986), the
financial econometrics literature has been successful at measuring, modeling, and
forecasting time-varying return volatility which has contributed to improved asset pricing,
portfolio management, and risk management, as surveyed for example in Andersen,
Bollerslev, Christoffersen and Diebold (2006a, 2006b).
The links between stock market volatility and real economic activity or output has
been investigated. Schwert (1989a) argues that stock market volatility, by reflecting
uncertainty about future cash flows and discount rates, provides important information about
future economic activity. Campbell et al. (2001), citing work by Lilien (1982), reason that
stock market volatility is related to structural change in the economy. Structural change
consumes resources, which depresses gross domestic product (GDP) growth. Another link
between stock market volatility and output rests on a cost-of-capital channel. That is, an
increase in stock market volatility raises the compensation that shareholders demand for
bearing systematic risk. The higher expected return leads to the higher cost of equity
capital in the corporate sector, which reduces investment and output. Consistent with
these hypotheses about the link between stock market volatility and economic activity,
Campbell et al. (2001) show that—after controlling for the lagged dependent variable—
stock market volatility has significant predictive power for real GDP growth. Bhide (1993)
argues that speculations and volatility in stock markets may reduce investment
efficiency, which has detrimental effect to economic growth. Mauro (1995) indicate that
the development of stock market will reduce economic growth through decreasing the
public's precautionary savings.
Guo(2002) shows that if the cost of capital is the main channel through which volatility
affects future output, stock market returns have a more important role in forecasting
economic activity than volatility does. Most empirical test on the relationship between
stock prices and real economic activity focus on returns not volatility. Several empirical
studies Fama (1981), Fischer and Merton (1984), Barro (1990), Fama (1990), Schwert
(1990), Peiro (1996), Domian and Louton (1997), Choi et al., (1999), Hassapis and
Kalyvitis, 2002) and Foresti (2006) found a strong relationship between stock returns and
real economic activity. Stock and Watson (1990,1998) provided evidence that stock
market performance is not correlated with real economic activity.
Binswanger (2000, 2004) and Mao and Wu (2007) argued that the that the relation
between stock returns and real economic activity has disappeared since the early
1980's indicating that stock return ceased to lead real economic activity. Binswanger
(2000) found the results for USA while Binswanger (2004) found similar result for
Canada Japan and the four European G-7 countries . Mao and Wu (2007) found similar
result for Australia using VAR methodology.
Empirical studies have also been conducted on the causality relationship between
stock market performance and real economic activity. Fama (1990), Schwert (1990),
Doong (2001), Canova and De Nicolo (1995) and Mauro (2003) finds that an increase in
stock market returns cause an increase in real economic activity. Mao and Wu (2007)
found a bidirectional long-run Granger causality between stock market prices and real
economic activity in Australia. Gjerde and Saettem (1999); and Know and Shin (1999)
provide evidence that the stock market performance is not a leading indicator for real
economic activity.
Most of the studies discussed are for developed countries. Few studies have been
conducted for emerging economies on the interrelationship between stock returns and
real economic activity. Rangvid (2001) provided evidence on the strong relation
between stock return and real economic activity for several emerging economies. Mauro
(2003) provided evidence for emerging and advanced countries while Kaplan (2008)
provided evidence for Turkey. Little or no work has been done on the relation between
stock returns or its volatility on real economic activity in Nigeria. This paper attempts to
fill this gap.
4.
METHODOLOGY
4.1
THE DATA
The time series data used in this analysis consists of quarterly data obtained from
Central Bank of Nigeria (2008), National Bureau of Statistics and the daily official list of
the Nigerian Stock Exchange over the period 1985.2 to 2009.3. In this study, real stock
return is defined as:
(1  SRt )
RSRt =
-1
(1)
(1  INFt )
Where
SRt represents stock return at time t. SRt is obtained as ln(NSIt/NSIt-1). NSIt
mean Nigerian stock Exchange index at time t while NSIt-1 represent
Nigerian Stock Exchange index at time t-1.
INFt
represents inflation rate at time t obtained as CPIt/CPIt-1 -1. CPIt
represents consumer price index at time t.
Growth rate in real gross domestic product (RGDP) is the proxy for real
economic activity and is obtained as REGDPt/REGDPt-1 – 1. REGDPt represents real
gross domestic product at quarter t obtained by deflating gross domestic product at
current prices by the consumer price index at quarter t.
RGDP and RSR will be used in obtaining the real GDP and real stock returns
volatilities.
The control variables used in this study includes growth in Money supply, growth
in private sector credits (PSG), growth rate in real exports to United States (REXG),
growth in real exchange rate (REXRG) and real Treasury bill yield (RTR).
Money Supply
Friedman and Schwartz (1963) argued that the growth rate of money supply would
affect the aggregate economy and hence the expected stock returns. An increase in M2
growth would indicate excess liquidity available for buying securities, resulting in higher
security prices. Empirically, Hamburger and Kochin (1972) and Kraft and Kraft (1977)
found a strong linkage between the two variables, while Cooper (1974) and Nozar and
Taylor (1988) found no relation. Money supply used in this study is the broad money
supply (M2). This is defined as M1 + quasi-money. Quasi-money is defined as Time,
savings and Foreign currency deposits of Commercial and Merchant banks. Growth rate
in money supply (MSG) is calculated as (M2t/M2t-1)-1.
Private Sector Credit
Private sector credit (PSC) is the proxy for banking development. Levine and Zervos
(1998) among others have examined the impact of banking development on economic
growth. Credits granted by banks could have influenced real economic activity and real
stock returns in Nigeria. In this study, growth in private sector credit (PSG t) is obtained
as PSCt/PSCt-1 – 1. PSCt represents private sector credits at quarter t.
Real export
Growth in real export could have positive impact on the economy due to increase in
foreign exchange earnings. As at 2009, the United States is the highest trading partner
of Nigeria. In this study real exports (REXPt) represents real export to United States at
quarter t obtained by deflating export to United States in Nigerian currency in quarter t
by consumer price index in quarter t. Growth in real export (REXGt) is obtained as
ln(REXPt/REXPt-1). The quarterly data on exports to United states was downloaded from
the website of the US. Census bureau.
Real exchange rate
Mukherjee and Naka (1995) argued that there is a positive correlation between stock
market returns and change in exchange rate. However, the impact of exchange rate on
the economy or the stock market could have been affected by the inflation rates. In this
study, the impact of real exchange rate on the stock market and the economy will be
examined. The bilateral nominal exchange rate, defined as the domestic currency price
of the U.S. dollar (EXH), is converted into a real exchange rate by multiplying the
nominal rate by the ratio of the U.S. CPI to the domestic CPI. Growth in real exchange
rate (REXRG) is obtained as ln(EXHt/EXHt-1). The US. CPI data was downloaded from
the website of the Economic Research department of the Federal Reserve Bank of St.
Louis.
Interest Rate (Treasury Bill yield)
Mukherjee and Naka (1995) argued that changes in both short- and long-term
government bond rates would affect the nominal risk-free rate and thus affect the
discount rate. Leon (2008) and Zafar, Urooj and Durrani (2008) found interest rates to
have a strong positive predictive power for stock returns but weak predictive power for
volatility. In this study the impact of short term interest rate (such as real treasury bill
yield) on the economy and stock market is examined. Real treasury bill yield (RTR) is
obtained as ((1+TRYt)/(1+INFt))-1. The nominal treasury bill yield (TRY) at quarter t is
obtained as ln(TRPt)/TRPt-1). The nominal price of a treasury bill with 91 days to
maturity in quarter t (TRPt) is N100. The nominal price of a treasury bill with 91 days to
maturity at quarter t-1 (TRPt-1) is obtained as N100(1- (91/365)TRt). TRt represents
treasury bill rate in quarter t.
Financial Reform variables
Financial reforms are represented with dummy variables in this study. Four financial reforms
are included in this study. The first reform is the introduction of structural adjustment in
September 1986 defined as SAP. The value 0 is entered for the quarters on/or before
September 1986 and 1 for other quarters.
The second financial reform is the deregulation of the capital market in January 1993
defined as CAPD. The value 0 is entered for the quarter before 1993 and 1 for other quarters.
The third financial reform is the introduction of Universal banking in January 2001
defined as UB. The value 0 is entered for the quarter before January 2001 and 1 for other
quarters.
The fourth financial reform is the introduction of new capital requirements of N25 billion
for banks in July 2004 defined as BC. The value 0 is entered for the quarters before July 2004
and 1 for other quarters.
The fifth financial reform is the introduction of new capital requirements for Insurance
companies in September 5, 2005 defined as INS. The value 0 is entered for the quarters
before September 5, 2005 and 1 for other quarters.
Stock market crash variable
Since April 1, 2008, stock prices on the Nigerian Stock market has been declining. The
stock index fell from 63016.56 on April 1, 2008 to 27108.4 on March 2, 2009. To
account for the stock market crash (MCS) in this paper, a dummy variable is set equal
to 0 for the quarters before April 1, 2008 and 1 thereafter.
Global Financial Crisis variable
In this study, September 7, 2008 is taken as the date of commencement of the global
financial crisis. On this day, the United States government took over two United States
Government sponsored enterprises Fannie Mae (Federal National Mortgage
Association) and Freddie Mac (Federal Home Loan Mortgage Corporation) into
conservatorship run by the United States Federal Housing Finance Agency. The global
financial crisis (GFC) will be accounted for in the paper by setting a dummy variable
equal to 0 for the quarters before September 7, 2008 and 1 thereafter.
4.2
PROPERTIES OF THE DATA
The summary statistics of the real GDP growth (RGDP), real stock returns (RSR),
money supply growth (MSG), private sector credit growth (PSG), real export growth
(REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series
are given in Table 3. The mean return for the real stock returns (RSR), real GDP growth
(RGDP), money supply growth (MSG), private sector credit growth (PSG), real export
growth (REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR)
series are 0.0227, 0.0059, 0.0682, 0.0764, 0.0431, 0.0117 and -0.0439 respectively
while their standard deviations are 0.1465, 0.1342, 0.0956, 0.1185, 0.4338, 0.2220 and
0.0715 respectively.
The skewness for the real GDP growth (RGDP), money supply growth (MSG),
private sector credit growth (PSG), real export growth (REXG), real exchange rate
growth (REXRG) and real treasury bill yield (RTR) series are 2.8102, -0.6874, 1.7892,
0.5888, 1.7149, 4.5056 and -0.4151respectively. This shows that the distribution, on
average, is positively skewed relative to the normal distribution (0 for the normal
distribution) for the RGDP, MSG, PSG, REXG and REXRG series while the distribution
is negatively skewed relative to the normal distribution (0 for the normal distribution) for
the RSR and RTR series. This is an indication of a non-symmetric series. The kurtosis
for the real GDP growth (RGDP), money supply growth (MSG), private sector credit
growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real
treasury bill yield (RTR) series are very much larger than 3, the kurtosis for a normal
distribution. Skewness indicates non-normality, while the relatively large kurtosis
suggests that distribution of the return series is leptokurtic, signaling the necessity of a
peaked distribution to describe this series. This suggests that for the RGDP, RSR,
MSG, PSG, REXG, REXRG and RTR series, large market surprises of either sign are
more likely to be observed, at least unconditionally. The Ljung-Box test Q statistics for
real stock returns (RSR), money supply growth (MSG), private sector credit growth
(PSG), real export growth (REXG) and real exchange rate growth (REXRG) series are
all insignificant at the 5% for all reported lags confirming the absence of autocorrelation
in the RSR, MSG, PSG, REXG and REXRG series. However, the Ljung-Box test Q
statistics for the real GDP growth (RGDP) and real treasury bill yield (RTR) are
significant at the 5% level confirming the presence of autocorrelation in the RGDP and
RTR series. The Jarque-Bera normality test rejects the hypothesis of normality for the
real GDP growth (RGDP), real stock returns (RSR), money supply growth (MSG),
private sector credit growth (PSG), real export growth (REXG) and real exchange rate
growth (REXRG) but accept the hypothesis for real treasury bill yield (RTR) series.
Figures 2, 3, 4, 5, 6, 7 and 8 shows the quantile-quantile plots of the real GDP growth
(RGDP), real stock returns (RSR), money supply growth (MSG), private sector credit
growth (PSG), real export growth (REXG), real exchange rate growth (REXRG) and real
treasury bill yield (RTR) series. Figures 2, 3, 4, 5, 6, 7 and 8 clearly show that the
distribution of the real GDP growth (RGDP), real stock returns (RSR), money supply
growth (MSG), private sector credit growth (PSG), real export growth (REXG) and real
exchange rate growth (REXRG) series show a strong departure from normality. The
distribution of real treasury bill yield (RTR) is approximately normal.
The Ljung-Box test Q2 statistics for the real stock returns (RSR) series are
significant at the 5% for all reported lags confirming the presence of heteroscedasticity
in the stock returns return series. However, the Ljung-Box test Q2 statistics for the real
GDP growth (RGDP), private sector credit growth (PSG) and real export growth (REXG)
series are insignificant at the 5% for all reported lags.
Table 4 shows the results of unit root test for the real GDP growth (RGDP),
money supply growth (MSG), private sector credit growth (PSG), real export growth
(REXG), real exchange rate growth (REXRG) and real treasury bill yield (RTR) series.
The Augmented Dickey-Fuller test statistics for the real GDP growth (RGDP), real stock
returns (RSR), money supply growth (MSG), private sector credit growth (PSG), real
export growth (REXG) and the real exchange rate growth (REXRG) series are less than
their critical values at the 1%, 5% and 10% level. This shows that the real GDP growth,
real stock returns, money supply growth, private sector credit growth, real export
growth (REXG) and the real exchange rate growth (REXRG) series have no unit root.
Thus, RGDP, RSR, MSG, PSG REXG and REXRG are I (0) variables and there is no
need to difference the data. However, RTR has a unit root. The results further showed
that RTR is first difference stationary. Thus, RTR is an I(1) variable. There is a need to
difference the RTR data.
In summary, the analysis of the real GDP growth (RGDP), money supply growth
(MSG), private sector credit growth (PSG), real export growth (REXG), real exchange
rate growth (REXRG) and real treasury bill yield (RTR) series indicates that the
empirical distribution of the real GDP growth, real stock returns, private sector credit
growth and real export growth series is non-normal, with very thick tails. The
leptokurtosis reflects the fact that the market is characterised by very frequent medium
or large changes. These changes occur with greater frequency than what is predicted
by the normal distribution. The empirical distribution confirms the presence of a nonconstant variance or volatility clustering. Thus, GARCH model will be used in estimating
the volatilities of real GDP growth (RGDP) and real stock returns.
Table 3:
Summary Statistics and Autocorrelation of the Real economic activity,
Stock returns Return, Private Credit Growth and Real exports growth
series over the quarters, 1985.2 – 2009.3
RGDP
RSR
Mean
0.0227
0.0059
Median
0.0087
0.0129
Maximum
0.7970
0.2787
Minimum
-0.1969 -0.4714
Std. Dev.
0.1465
0.1342
Skewness
2.8102 -0.6874
Kurtosis
15.1656
4.0797
Jarque-Bera 733.3220 12.4773
Probability
(0.0000)* (0.0020)*
Observations
98
98
Ljung-Box Q Statistics
Q(1)
2.5843
3.8339
(0.1080) (0.0500)
Q(2)
8.3686
5.7408
(0.0150)* (0.0570)
Q(3)
8.6320
5.7414
(0.0350)* (0.1250)
Q(4)
12.5910
5.7521
(0.0130)* (0.2180)
Q(5)
14.2640
5.7770
(0.0140)* (0.3290)
2
Ljung-Box Q Statistics
Q2(1)
0.0013 24.6500
(0.9710) (0.0000)*
Q2(2)
0.0055 31.0260
(0.9970) (0.0000)*
Q2(3)
0.2428 32.4690
(0.9700) (0.0000)*
Q2(4)
0.5528 32.4910
(0.9680) (0.0000)*
Q2(5)
0.7766 32.5280
(0.9790) (0.0000)*
Notes: p values are in parentheses.
MSG
PSG
REXG
REXR
RTR
0.0682
0.0764
0.0431
0.0117 -0.0439
0.0547
0.0611 -0.0033 -0.0217 -0.0367
0.5135
0.5610
1.8614
1.3562
0.1410
-0.1208 -0.3910 -0.7050 -0.3681 -0.2382
0.0956
0.1185
0.4338
0.2220
0.0715
1.7892
0.5888
1.7149
4.5056 -0.4151
8.4468
7.9344
7.9506 28.0353
3.6677
173.431 105.083 148.110 2890.861
4.635
(0.0000)* (0.0000)* (0.0000)* (0.0000)* (0.0985)
98
98
98
98
98
0.6548
(0.4180)
1.2829
(0.5270)
2.6721
(0.4450)
5.6579
(0.2260)
5.8623
(0.3200)
1.4553
(0.2280)
1.5974
(0.4500)
1.6041
(0.6580)
1.7066
(0.7900)
9.2004
(0.1010)
2.4611
(0.1170)
3.8300
(0.1470)
4.9195
(0.1780)
4.9954
(0.2880)
5.0613
(0.4080)
0.5543
(0.4570)
1.0771
(0.5840)
1.1914
(0.7550)
1.7930
(0.7740)
1.7941
(0.8770)
5.1505
(0.0230)*
12.6110
(0.0020)*
15.3070
(0.0020)*
50.9010
(0.0000)*
51.9280
(0.0000)*
0.3378
5.6307
0.0561
0.0070
1.9146
(0.5610) (0.0180) (0.8130) (0.9340) (0.1660)
2.2612 17.4230
0.0734
0.0488
2.2751
(0.3230) (0.0000)* (0.9640) (0.9760) (0.3210)
3.1150 19.1480
0.3329
0.1577
2.2874
(0.3740) (0.0000)* (0.9540) (0.9840) (0.5150)
3.5801 25.1270
0.7682
0.2758 22.9920
(0.4660) (0.0000)* (0.9430) (0.9910) (0.0000)*
3.5811 34.0660
5.9987
0.3711 22.9930
(0.6110) (0.0000)* (0.3060) (0.9960) (0.0000)*
*
indicates significant at the 5% level
Figure 2:
Quantile-Quantile Plot of Quarterly Real GDP Series over the period,
1985.2 – 2009.3
.5
.4
Quantiles of Normal
.3
.2
.1
.0
-.1
-.2
-.3
-.4
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Quantiles of RGDP
Figure 3:
Quantile-Quantile Plot of Quarterly Real stock return series over the
period, 1985.2 – 2009.3
.4
.3
Quantiles of Normal
.2
.1
.0
-.1
-.2
-.3
-.4
-.5
-.4
-.3
-.2
-.1
.0
Quantiles of RSR
.1
.2
.3
Figure 4:
Quantile-Quantile Plot of Money Supply growth Series over the period,
1985.2 – 2009.3
.4
Quantiles of Normal
.3
.2
.1
.0
-.1
-.2
-.2
-.1
.0
.1
.2
.3
.4
.5
.6
Quantiles of MSG
Figure 5:
Quantile-Quantile Plot of Quarterly Private credit growth Series over the
period, 1985.2 – 2009.3
.4
Quantiles of Normal
.3
.2
.1
.0
-.1
-.2
-.3
-.4
-.2
.0
.2
Quantiles of PSG
.4
.6
Figure 6:
Quantile-Quantile Plot of Quarterly real export growth series over the
period, 1985.2 – 2009.3
1.2
Quantiles of Normal
0.8
0.4
0.0
-0.4
-0.8
-1.2
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
2.0
Quantiles of REXG
Figure 7:
Quantile-Quantile Plot of Quarterly real exchange rate growth series over
the period, 1985.2 – 2009.3
.6
Quantiles of Normal
.4
.2
.0
-.2
-.4
-.6
-0.4
0.0
0.4
0.8
Quantiles of REXRG
1.2
1.6
Figure 8:
Quantile-Quantile Plot of Quarterly real interest rate yield series over the
period, 1985.2 – 2009.3
.15
.10
Quantiles of Normal
.05
.00
-.05
-.10
-.15
-.20
-.25
-.3
-.2
-.1
.0
.1
.2
Quantiles of RTR
Table 4:
Unit Root Test of the Real GDP, Real Stock returns, Private Credit growth
and Real export over the quarters, 1985.2 – 2009.3
Variable Exogenous
Augmented Dickey-Fuller test
Statistic
Critical Values (%)
1% level 5% level 10% level
RGDP
RSR
MSG
PSG
REXG
REXRG
RTR
D(RTR)
L
L
L
L
LC
L
L
L
LC
LTC
L
-9.2714
-7.8532
-4.7619
-1.5984
-10.9978
-12.3183
-10.5207
-1.5097
-2.5876
-3.1274
-14.8393
-2.5893
-2.5890
-2.5903
-2.5901
-3.4992
-2.5890
-2.5890
-2.5898
-3.5014
-4.0586
-2.5898
-1.9442
-1.9442
-1.9444
-1.9443
-2.8916
-1.9442
-1.9442
-1.9443
-2.8925
-3.4583
-1.9443
-1.6145
-1.6146
-1.6144
-1.6145
-2.5828
-1.6146
-1.6146
-1.6145
-2.5834
-3.1552
-1.6145
Notes: The appropriate lags are automatically selected employing Akaike information criterion. L
indicates unit root test using level observation only . LC indicates the result of the unit root test by
including a constant in the test equation. LTC indicates the result of the unit root test by including a
constant and trend in the test equation. FD represents the first difference. The first difference has no
constant in the test equation. The bolded item indicates stationary.
4.3
MODELS USED IN THE STUDY
This study uses the Vector autoregression model (VAR) to investigate the
interrelationship between the real economic activity (RGDP), real stock returns (RSR),
real economic activity volatility (GDPV) and real stock return volatility (RSV). The VAR
will assist in assessing the direction of causality among the variables. The control
variables (MSG, PSG, REXG, REXRG and RTR) and the financial reform variables
(SAP, CAPD, UB, BC, INS, GFC and MCS) will serve as exogenous variables in all
equations. However, to formulate our VAR, real economic activity volatility (GDPV) and
real stock return volatility (RSV) needs to first be estimated.
Due to possible non-symmetric property of the real GDP growth (RGDP) and real
stock return (RSR) series, this paper employs the exponential GARCH (EGARCH)
model advanced by Nelson (1991). The EGARCH model, unlike the GARCH model,
indicates that the conditional variance is an exponential function, thereby removing the
need for restrictions on the parameters to ensure positive conditional variance. Thus,
the model used in estimating the volatility of the real GDP growth (RGDP) series is the
EGARCH (1, 2) model while the volatility of real stock return series is estimated using
the EGARCH (1, 3) model. The lag length of the RGDP used in accounting for
autocorrelation of RGDP has been chosen on the basis of Akaike information Criterion.
Due to non-normality and excess kurtosis of the RGDP and RSR series the two models
are under the assumption of Generalized Error Distribution (GED).
The mean and variance equations of the RGDP series are given as:
2
RGDPt =b0+  b j RGPGt  j + εt
j 1
t / t 1 ~ N(0, 2t ,v t )
(2)
t 1

2
(3)

  log(2t 1 )   t 1
t 1

t 1
Where vt is the degree of freedom
The EGARCH variance series from (3) will be used as an estimate of the volatility of
real GDP (GDPV).
The mean and variance equations of the RSR series are given as:
log(2t )    
Rt = b0 + dσt + εt
log(2t )    
t / t 1 ~ N(0, 2t ,v t )
(4)
3
t 1

2

   j log(2t  j )   t 1
t 1
 j1
t 1
(5)
where vt is the degree of freedom
The EGARCH variance series from (5) will be used as an estimate of the volatility of
real stock returns (RSV).
5.
THE RESULTS
Estimation of Real economic volatility and Real Stock Return Volatility
The results of estimating the EGARCH (1, 2) model for the real GDP (RGDP) series and
EGARCH-M (1, 2) model for the real stock return (RSR) series are presented in Table
5. In the mean equation of the RGDP series, b1 (coefficient of first lag of real GDP) is
significant at the 5% level confirming the correctness of adding the variable to correct
for autocorrelation in the real GDP series. However, b2 (coefficient of second lag of real
GDP) is insignificant at the 5% level.
The variance equation in Table 5 shows that the α coefficient is statistically
insignificant in the EGARCH (1, 2) model for the RGDP series. This tends to confirm the
result from preliminary testing of absence of ARCH effect in the RGDP series. Table 5
shows that the α coefficient is statistically significant in the EGARCH (1, 3) model. This
tends to confirm that the ARCH effects are very pronounced implying the presence of
volatility clustering. Table 5 shows that in the EGARCH (1,2) model, β2 coefficient (the
determinant of the degree of persistence) is statistically significant at the 5% level while
in the EGARCH (1,3) model, β3 is statistically significant at the 5% level. Even though β1
is statistically insignificant in the EGARCH (1,2) model, its value is negligible compared
to β2. Similarly, though β1 and β2 are insignificant in the EGARCH (1,3) model, their
values are negligible compared to β3. The β2 coefficients in the EGARCH (1,2) model
and β3 in the EGARCH (1,3) model are 0.9903 and 0.8236 respectively. This appears
to show that there is a high persistence in volatility in Nigeria. Table 5 shows that the
coefficients of γ, the asymmetry and leverage effects, are negative and statistically
significant at the 5% level in the EGARCH (1,3) model but insignificant in the EGARCH
(1, 3) model. The predominance negatively significance of γ in the results, appears to
show that the asymmetry and leverage effects are accepted in the EGARCH (1,3)
model of the RSR series but rejected in the EGARCH (1, 2) model of the RGDP series.
Table 6 shows the results of the diagnostic checks on the estimated EGARCH
(1,2) and EGARCH (1,3) models for the real GDP and real stock returns respectively.
Table 6 shows that the Ljung-Box Q-test statistics of the standardized residuals for the
remaining serial correlation in the mean equation shows that autocorrelation of
standardized residuals are statistically insignificant at the 5% level for the real GDP
growth (RGDP) and real stock returns (RSR) series confirming the absence of serial
correlation in the standardized residuals. This shows that the mean equations are well
specified. The Ljung-Box Q2-statistics of the squared standardized residuals in Table 6
are all insignificant at the 5% level for the real GDP growth (RGDP) and real stock
returns (RSR) series confirming the absence of ARCH in the variance equation. The
ARCH-LM test statistics in Table 6 for the real GDP growth (RGDP) and real stock
returns (RSR) further showed that the standardized residuals did not exhibit additional
ARCH effect. This shows that the variance equations are well specified in for the full
sample, all sub periods and the augmented model. In sum, all the models are adequate
for forecasting purposes.
Parameter Estimates of the EGARCH Models over the quarters, 1985.2 –
2009.3
RGDP
RSR
Mean equations
b0
0.0028
0.0179
(0.2642)
(0.1526)
b1
-0.1312
(0.0012)*
b2
-0.1393
(0.0687)
Variance equations
ω
0.3692
-0.8565
(0.5298)
(0.1119)
α
-0.1878
0.5953
(0.3439)
(0.0034)*
γ
-0.1480
-0.2828
(0.1577)
(0.0167)*
β
0.0569
-0.0373
(0.4188)
(0.6964)
β2
0.9903
0.1100
(0.0000)*
(0.1551)
β3
0.8236
(0.0000)*
GED
1.2091
2.5330
(0.0000)*
(0.0023)*
Persistence
1.0472
0.8963
LL
84.9193
70.4094
AIC
-1.5817
-1.2737
SC
-1.3412
-1.0626
HQC
-1.4845
-1.1883
N
98
98
EGARCH
EGARCH(1,2) EGARCH (1,3)
Table 5:
Notes: p values are in parentheses.
*
indicates significant at the 5% level.
LL, AIC, SC, HQC and N are the maximum log-likelihood, Akaike information Criterion, Schwarz Criterion,
Hannan-Quinn criterion and Number of observations respectively
Table 6:
Autocorrelation of Standardized Residuals, Autocorrelation of Squared
Standardized Residuals and ARCH LM test of Order 4 for the EGARCH
and EGARCH-in-Mean Models over the quarters, 1985.2 – 2009.3
RGDP
RSR
Ljung-Box Q Statistics
Q(1) 3.1080 1.2981
(0.0780) (0.2550)
Q(2) 4.0836 1.4782
(0.1300) (0.4780)
Q(3) 4.3568 2.7213
(0.2250) (0.4370)
Q(4) 4.3568 6.6376
(0.3600) (0.1560)
Q(5) 4.5968 10.2780
(0.4670) (0.0680)
2
Ljung-Box Q Statistics
Q2(1) 0.0576 0.5455
(0.8100) (0.4600)
Q2(2) 0.2763 0.7202
(0.8710) (0.6980)
Q2(3) 0.7404 1.4177
(0.8640) (0.7010)
Q2(4) 0.7539 1.4450
(0.9450) (0.8360)
2
Q (5) 0.8262 1.4454
(0.9750) (0.9190)
ARCH-LM TEST
ARCH-LM (1) 0.0552 0.5154
(0.8148) (0.4746)
ARCH-LM (5) 0.1869 0.2519
(0.9668) (0.9378)
Note: p values are in parentheses
Table 7 shows the results of unit root test for the estimated real economic activity
volatility (GDPV) and real stock returns volatility (RSV) from the EGARCH variance
series from Table 5. The Augmented Dickey-Fuller test statistics show that both RSV
and GDPV have a unit root. The results further showed that both RSV and GDPV are
first difference stationary. They are, thus, I(1) variables. Thus, there is a need to
difference both the GDPV and RSV data.
Table 7:
Unit Root Tests of Real Economic Activity Volatility and Real Stock Return
Volatility over the quarters, 1985.2 – 2009.3
Variable Exogenous
Augmented Dickey-Fuller test
Statistic
Critical values
1% level 5% level 10% level
GDPV
L
-0.7949 -2.5898 -1.9443 -1.6145
LC
-0.9456 -3.5014 -2.8925 -2.5834
LTC
-1.4751 -4.0586 -3.4583 -3.1552
D(GDPV)
L -134.6310 -2.5898 -1.9443 -1.6145
RSV
L
0.3565 -2.5895 -1.9442 -1.6145
LC
-1.3163 -3.5007 -2.8922 -2.5832
LTC
-3.2723 -4.0586 -3.4583 -3.1552
D(RSV)
L -17.7076 -2.5895 -1.9442 -1.6145
Notes: The appropriate lags are automatically selected employing Akaike information criterion. L
indicates unit root test using level observation only . LC indicates the result of the unit root test by
including a constant in the test equation. LTC indicates the result of the unit root test by including a
constant and trend in the test equation. FD represents the first difference. The first difference has no
constant in the test equation. The bolded item indicates stationary.
Interrelationship between Real economic activity, Real stock returns, Real
economic activity volatility and real stock return volatility
The Vector Autoregression models (VAR) used in investigating the Interrelationship
between Real economic activity, Real stock returns, real economic activity volatility and
real stock return volatility are given as follows:
2
2
2
2
12
i1
i1
i 1
i 1
j 1
RGDPt=α1+  i1RGDPt i +  i1RSRt i +   i1D(GPV)t i +  i1D(RSV)t i +   j1FRE jt +εi1
2
2
2
2
12
i1
i 1
i 1
i 1
j 1
RSRt=α2+  i2RGDPt i +  i2RSRt i +   i2D(GPV)t i +  i2D(RSV)t i +   j2FRE jt +εi2
2
2
2
2
12
i1
i 1
i 1
i 1
j 1
D(GPV)t=α3+  i3RGDPt i +  i3RSRt i +   i3D(GPV)t i +  i3D(RSV)t i +   j3FRE jt +εi3
2
2
2
2
12
i1
i 1
i 1
i 1
j 1
RSVt=α4+  i4RGDPt i +  i4RSRt i +   i4D(GDPV)t i +  i4D(RSV)t i +   j4FRE jt +εi4
(6)
(7)
(8)
(9)
Where FREj represents the twelve financial reforms and other exogenous variables and
they include MSG, PSG, REXG, REXRG, RTR, SAP, CAPD, UB, BC, INS, GFC and
MCS.
The results of the VAR of Equations (6) to (9) are presented in Table 8. The lag length
of the VAR was selected on the basis Final prediction error and Akaike information
criterion. The VAR in Table 8 satisfies the stability condition as no root lies outside the
unit circle. The result of the VAR of Equation (6) as presented in Table 8 showed that
the coefficients of RGDP(-2), and PSG are negative and statistically significant at the
5% level while all other variables are statistically insignificant at the 5% level. This
implies that the real GDP growth two quarters ago and growth in private sector credits
negatively influenced the real economic activity. All other variables do not influence real
economic activity.
The result of Equation (7) as shown in Table 8 showed that the coefficients of
lags of RGDP, RSR, D(GDPV) and D(RSV) are all statistically insignificant at the 5%
level implying that these variables have no influence on real stock return. The statistical
insignificance of lags of RSR is in line with the weak form efficiency of the Nigerian
stock market (Olowe, 1999). Equation (7) as shown in Table 8 further showed the
coefficients of all the control variables are insignificant. However, Equation (7) shows
that the coefficient of BC is negative and statistically significant at the 5% level while the
coefficient of INS is positive and statistically significant at the 5% level. This implies that
the introduction of new capital requirements of N25 billion for banks in July 2004 negatively
influenced real stock returns while the introduction of new capital requirements for Insurance
companies in September 5, 2005 positively influenced real stock returns.
The result of Equation (8) as shown in Table 8 showed that the coefficients of
RGDP (-1), RGDP (-2), D(GDPV(-1)) and D(RTR (-1)), are negative and statistically
significant at the 5% level while all other variables are insignificant. This implies that the
real economic activity last quarter, the real economic activity two quarters ago, real
economic activity volatility last quarter, and treasury bill yield last quarter negatively
influenced real economic activity volatility.
The result of Equation (9) as shown in Table 8 showed that the coefficients of
RGDP(-1), RSR (-1), RSV(-1) and RSV(-2), are negative and statistically significant at
the 5% level while the coefficient of RSR(-2) and GFC are positive and statistically
significant at the 5% level. This implies that the real GDP growth last quarter, real stock
return last quarter, real stock return volatility last quarter and real stock return volatility
two quarters ago negatively influenced real stock return volatility. However, real stock
returns two quarters ago and global financial crisis positively influenced real stock return
volatility.
In order to properly determine the direction of causality, the Granger causality
test is implemented within the framework of a vector autoregression (VAR) model.
Estimation results obtained from using quarterly data are presented in Table 9 for the
period 1985.2-2009.3. Table 9 provides Chi-square statistics and probability values of
pairwise Granger causality/block exogeneity Wald test results between the endogenous
variables. The null hypothesis of non-causality shows that there is no causal
relationship between real economic activity and real stock returns at the 5% level.
However, Table 9 shows that the direction of causality is one way from real economic
activity to real economic activity volatility; real economic activity to real stock return
volatility; real stock returns to real stock return volatility; and real economic activity
volatility to real stock return volatility. The result of no causal relationship between real
economic activity and real stock return is in line with the results obtained by Binswanger
(2000, 2004) and Mao and Wu (2007).
Table 8:
VAR results for the Interrelationship between Real economic activity, Real Stock
Return, Real economic activity volatility and real stock return volatility over the quarters,
1985.2 – 2009.3
RGDP
C
-0.2509
(-1.3621)
RGDP(-1)
-0.1206
(-1.0412)
RGDP(-2)
-0.4142
(-2.8786)*
RSR(-1)
0.2030
(1.4552)
RSR(-2)
0.0105
(0.0695)
D(GDPV(-1))
-9.0188
(-1.0457)
D(GDPV(-2))
-6.6767
(-0.8077)
RSV(-1)
0.6484
(0.6302)
RSV(-2)
-0.4453
(-0.4717)
MSG
0.1885
(1.0968)
PSCG
-0.3233
(-2.3400)*
REXG
0.0257
(0.4664)
REXR
0.1009
(0.9358)
D(RTR(-1))
0.0060
(0.0293)
SAP
0.2986
(1.6153)
CAPD
0.0108
(0.2842)
UB
-0.0287
(-0.6174)
BC
0.0312
(0.4150)
INS
-0.0270
(-0.3340)
MCS
-0.0129
(-0.0873)
GR
0.0306
(0.1856)
Adj. R-squared
0.5402
Log likelihood
93.7242
Akaike AIC
-1.5731
Schwarz SC
-1.0624
Note: t-statistics are in parentheses
RSR D(GDPV)
0.0033
( 0.0203)
-0.0472
(-0.4557)
0.2038
( 1.5836)
0.0330
( 0.2647)
-0.0929
(-0.6877)
2.2091
( 0.2863)
0.4394
(0.0594)
0.3549
(0.3856)
-0.3761
(-0.4454)
-0.2391
(-1.5549)
-0.1045
(-0.8458)
0.0646
(1.3129)
-0.0352
(-0.3651)
0.2857
(1.5686)
0.0282
(0.1704)
-0.0029
(-0.0854)
0.0448
(1.0768)
-0.1503
(-2.2380)*
0.1983
(2.7454)*
-0.2282
(-1.7251)
-0.1004
(-0.6818)
0.3386
83.4302
-1.3564
-0.8456
*
RSV
-0.0016
0.0008
(-0.6414)
-0.0643
-0.0111
-0.0270
(-7.2056)* (-3.2769)*
-0.0043
0.0009
(-2.2168)*
( 0.0878)
0.0015
-0.0666
( 0.8219) (-6.7161)*
0.0016
0.0282
( 0.8003) (2.6268)*
-0.9493
0.2550
(-8.2538)*
(0.4158)
0.0279
0.0724
( 0.2528)
( 0.1232)
-0.0002
-0.9743
(-0.0165) (-13.3192)*
0.0007
-0.7179
( 0.0575) (-10.6956)*
0.0011
-0.0068
( 0.4792)
(-0.5550)
0.0001
0.0064
( 0.0429)
(0.6474)
-0.0006
0.0046
(-0.7890)
(1.1639)
0.0022
-0.0078
(1.5283)
(-1.0203)
-0.0059
0.0187
(-2.1830)*
(1.2937)
0.0018
0.0016
(0.7152)
(0.1194)
0.0004
-0.0011
(0.7491)
(-0.4127)
-0.0012
-0.0010
(-1.8777)
(-0.2910)
0.0013
0.0072
(1.2597)
(1.3517)
-0.0017
-0.0076
(-1.6048)
(-1.3285
0.0006
-0.0083
( 0.2944)
(-0.7867)
0.0009
0.0266
(0.4105) (2.2724)*
0.8821
0.4625
478.5851
67.6863
-9.6755
-1.0250
-9.1647
-0.5142
indicates significant at the 5% level.
Table 9:
*
Granger causality/block exogeneity Wald test results between the
endogenous variables over the quarters, 1985.2 – 2009.3
Null Hypothesis
Χ2 df Prob.
RGDP does not Granger cause RSR
2.2398 2 0.3263
RGDP does not Granger cause D(GDPV) 4.8364 2 0.0891
RGDP does not Granger cause RSV
1.5576 2 0.4590
RSR does not Granger cause RGDP
2.6454 2 0.2664
RSR does not Granger cause D(GDPV)
3.6439 2 0.1617
RSR does not Granger cause RSV
0.8824 2 0.6433
D(GDPV) does not Granger cause RGDP 58.7204* 2 0.0000
D(GDPV) does not Granger cause RSR
1.6206 2 0.4447
D(GDPV) does not Granger cause RSV
0.0075 2 0.9963
RSV does not Granger cause RGDP
10.7458* 2 0.0046
RSV does not Granger cause RSR
47.0338* 2 0.0000
RSV does not Granger cause D(GDPV)
6.0839* 2 0.0477
indicates significant at 5% level
Thus, the findings in this study show that the developments in the stock market
have no impact on output in Nigeria. The banking reform in 2004 and insurance reform
in 2005 contributes to the development of the stock market as they influenced real stock
return. However, due to speculative nature of Nigerian economy, past volatilities in
stock return and real economic activity influenced real stock return volatility. The global
financial crisis also has direct positive impact on the real stock return volatility. Despite
the development of the stock market as a result of banking and insurance reform, the
real economic activity is not affected. This could possibly due to speculative bubbles in
the stock markets. Stock prices might be increasing when they are not fully supported
by fundamentals of the companies. There might be no connection between companies
performance and stock price performance. If, at all there is a connection between
companies performance and stock price performance, it could also be having been due
to manipulation by some companies - window dressing their accounts so as to have a
favourable impact on the stock market. The economy will not be affected since these
companies did not really contribute to output in Nigeria.
6.
SUMMARY AND CONCLUDING REMARKS
This paper investigated the inter relationship between real economic activity, real
stock returns, real economic activity volatility and real stock return volatility in Nigeria using
quarterly data over the period 1986.4 and 2009.3 in the light of financial reforms, stock
market crash and the global financial crisis. The results showed that there is no causal
relationship between real economic activity and real stock returns. This is consistent
with the results of Binswanger (2000, 2004) and Mao and Wu (2007).
The results also show there is a one-way causality going through from real
economic activity to real economic activity volatility; real economic activity to real stock
return volatility; real stock returns to real stock return volatility; and real economic
activity volatility to real stock return volatility. The results showed that past volatilities in
real stock returns and real economic activity negatively influenced real economic activity
volatility. The results also show that private sector credit influences real economic
activity. The result also shows that real treasury bill yield negatively influenced real
economic activity volatility. The results also show that the introduction of new capital
requirement for banks and introduction of new capital requirement for insurance
influences real stock returns. The result also show that the global financial crisis
influences real stock return volatility.
Despite the development of the stock market as a result of banking and
insurance reform, the real economic activity is not affected. This could possibly be due
to speculative bubbles in the stock market. Stock prices might be increasing when they
are not fully supported by fundamentals of the companies. There might be no
connection between companies performance and stock price performance. If at all there
is a connection between companies performance and stock price performance, it could
have been be due to manipulations by some companies -window dressing their
accounts so as to have a favourable impact on the stock market. The economy will not
be affected since these companies did not really contribute to output in Nigeria. Policy
makers should introduce economic policies towards increasing real output of companies
in Nigeria. The government should improve infrastructural facilities especially electricity
in the country so aid Nigerian companies towards contributing to real output in Nigeria.
The government should also continue to promote quality of financial reporting in Nigeria.
There should be appropriate sanction for any quoted company found to have window
dress its accounts.
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