Gauging Stock Price Volatility during the Financial Crisis using a

Multivariate Cointegration Analysis

Zuriyati Ahmad, Tuan Nadiah Tuan Razilah

Faculty of Business and Management, Universiti Teknologi MARA cawangan Terengganu Malaysia

Keywords: Stock Price, Financial Crisis, Cointegration, Multivariate Analysis

Abstract: The stock price performance can be an indicator to reflect current economic conditions, trends and public trust

in economic performance. Macroeconomic variables theoretically affect the stock market as the stability of

macroeconomic variables can help to stabilize the stock price. Nonetheless, previous findings show the stock

market experienced a little difficulty because the shock event happened in Malaysia. Thus, this study aims to

gauge stock price volatility the financial crises. Monthly time series data spanning from 1996 to 2014 is

applied to the multivariate model with inclusion of dummy variable of financial crisis. The normal OLS and

Johansen cointegration test are applied to examine the changes of stock price. The finding indicates a strong

impact of financial crisis is found towards the stock price when it tested using a multivariate analysis. While

a cointegration exists in the stock price model. Nonetheless, when checking for the long run equilibrium, the

financial crisis is insignificant towards the volatility of stock price in Malaysia.

1 INTRODUCTION

The stock market is a channel for the corporate sector

to raise capital for investment and business activities.

Malaysia is the potential country for investors to have

a new business opportunity due to the economic

growth of this country. Maziah, Anisah and Hadhifah

(2013), have described the Malaysian stock market is

growing in line with the rapid economic development

in the past decade. The Malaysian stock market has

experienced dramatic changes in the past two decades

(Zaherawati, et. al, 2010). The stock market related

closely to economic growth and it is also a strong

economic growth that increases the stock

performance.

The stock market serves as a guide in shaping a

country's economic growth. The establishment of the

stock market exchange allows government and

corporate institutions to raise capital quickly in

accelerating economic development (Kyereboah-

Coleman & Agyire-Tettey, 2008). Previous

researchers indicate that the stock price performance

can be an indicator to reflect current economic

conditions, trends and public trust in economic

performance. Macroeconomic variables theoretically

affect the stock market because the stability of

macroeconomic variables can help to stabilize stock

price too. In fact, Mohamed et. al (2009) state that the

changes in macroeconomic variables can fully reflect

the current share price

Nevertheless, the stock market experienced a

little difficulty because the natural event of problems

in the macroeconomic variables that happen in

Malaysia (Aisyah, Zahirah & Fauziah, 2009). For

example, a steep decline during 1997 when the

financial crisis hit. The Asian financial crisis has

made a tremendous outbreak to the Malaysian

economy and in turn fluctuate the stock price

severely. Before 1996, for example KLCI has reached

more than 1200 points and most investors believe that

this trend will be maintained. However, it fell to 500

points due to the financial bubble burst (Asmy et al,

2009).

The scenario that Malaysia has facing during the

Asian Financial Crisis in 1997 and the global

financial crisis in 2008 has led a slowdown in the

stock market. Asian countries, including Malaysia

suffered heavily as an impact of the financial crisis

(Zaherawati et al, 2010). Due to that, Malaysia is

having a volatility of stock price due to

macroeconomic factors and financial crisis. Thus, this

study aims to gauge the volatility of stock price

during the financial crises.

The remaining of the paper is organized as

follows. Section 2 discusses the literature review. The

methodology under consideration will be highlighted

Ahmad, Z. and Tuan Razilah, T.

Gauging Stock Price Volatility during the Financial Crisis using a Multivariate Cointegration Analysis.

DOI: 10.5220/0009327005850590

In Proceedings of the 2nd Economics and Business International Conference (EBIC 2019) - Economics and Business in Industrial Revolution 4.0, pages 585-590

ISBN: 978-989-758-498-5

585

in section 3. Section 4 discusses result and discussion.

Lastly, section 5 offers conclusion.

2 LITERATURE REVIEW

Stock price as defined by Mishkin (2007) is a

securities claimed on the income and assets of the

corporation known as the shares traded. In efficient

capital markets, stock prices react quickly to the new

information available. Therefore, stock price reflects

all stock’s information which causing the investors

are not allowed to use the information that already

available to forecast stock prices movement and make

profits from the shares traded. Besides, stock prices

also reflect future expectations corporate

performance and profitability.

Stock price is a crucial indicator for portfolio

management. The investment decision can be

affected by volatility and fluctuation of the stock

market. Many studies (Adamu, 2010; Rafaqet & Ali,

2012; Sakthivel et al., 2014; Gabriel & Manso, 2014;

Lee and Jeong, 2014; Kishor and Singh, 2014) have

been conducted in measuring the impact of financial

crises and stock market.

Theoretically, the financial crisis led to a great

uncertainty in global stock markets (Olusola, 2011).

It gives a big impact towards the economic growth of

a country. During the financial crisis, many financial

institutions have collapsed and the government was

forced to come up with other alternatives to save the

financial system. Thus, studies on the impact of

financial crises on stock price volatility have been

carried in many countries and region. For example, in

Nigeria, Adamu (2010) studies the stock market

volatility before and during the crisis. The period is

divided into 24 months each to study the volatility of

market returns which are between 2006 – 2007, and

2008 – 2009. Using variance or standard variation to

examine the volatility of stock market and the result

indicates that during the period 2006 – 2007, the stock

market is less volatile than the period 2008 – 2009.

While in India, Sakthivel et al (2014) using JGR

GARCH model to examine the pre and post crises.

Sakthivel et. al (2014) conclude that global financial

crisis gives a negative impact to the stock returns and

increases the volatility in the Indian stock markets.

Using almost similar method of E GARCH, Rafaqet

and Ali (2012) examine the volatility of stock market

during the financial crisis in Pakistan and also India.

Results reveal that crisis negatively contributes to

volatility in stock price for both markets, but the

effect is slightly weak. Nonetheless, crisis gives

stronger impact towards the Indian market, represents

by Bombay stock exchange compared to Pakistan

stock market (Karachi stock exchange). The

difference might be due to the fact that India has a

bigger economy and its stock market is more open

than Pakistan.

Kishor and Singh (2014) investigate the stock

return volatility relationship of emerging economies

from 2007 to 2013 which also includes the financial

crisis of 2008 and its impact on emerging economies

of the world. GARCH model is used to examine the

impact of news coming from US which is affecting

the returns of global index S&P 500 as well as the

returns generated by the indices of the BRICS

countries. The result indicates there is impact of the

global financial crisis on stock returns BRICS.

Except the market share of Brazil and China, the

volatility of the stock market of Russia, India and

South Africa is slightly affected by the global crisis.

Gabriel and Manso (2014) investigate

interdependencies and the linkages between

international stock markets in the short run in

European during the global financial crisis and the

Dot-Com crisis. Using a Granger causality test, a

causation is found from financial crisis to the

European stock market showing the financial crisis

led to volatility of stock market.

Studying on North America and Europe, Lee and

Jeong (2014) examine the effects of the global

financial crisis on the stock market of these two

regional equity markets from the period January 2000

and December 2012. It is found that the Northeast

Asian equity markets remain independent from

European and global stock market movements in the

analyzed period. The volatility of the stock market

has increased temporally during the global financial

crisis in European.

3 METHOD

This study will be benefitted from monthly data set of

19 years covering from 1996 to 2014 with the total

number of observations is 228. The observation is

adequate for the method chosen. Sample is restricted

to this time span in order to get uniformness of the

data set and considering the availability of the data.

Data is obtained from International Financial Statistic

and Bank Negara Malaysia statistical bulletin.

For modelling framework, the impact of financial

crisis which is the dummy variable of financial crises

is included. The dummy variable (FC) indicates the

period of financial crises. In its original form, it is set

1 if financial crisis occurred. Otherwise it is set 0,

yielding a sequence isolated 1s, surrounded by 0s.

The goal of the study is to gauge stock price

during the financial crisis. Stock price however can

also be influenced by various macroeconomic factors

such as exchange rates, inflation, economic condition

EBIC 2019 - Economics and Business International Conference 2019

586

and interest rate. In order for a linear regression

model to be more realistic, these macroeconomic

factors which work as controlling variables are added

in gauging the impact of financial crisis on stock price

volatility. Hence, the estimated linear regression

model is as follows;

SP = α + β

CPI+ β

IR - β

ER + β

IPI - β

FC + ɛ

SP = stock price

CPI = consumer price index

IR = interest rate

ER = exchange rate

IPI = industrial production index

FC = dummy of financial crisis

α = constant

β= coefficient

ɛ = Error term

Since most macroeconomic time series are subjected

to some type of trend, it is important to confirm

stationary or non stationary properties of variables

chosen. Thus, it is imperative to test for the presence

of the unit root test. The ADF unit root test is based

on the null hypothesis H

: each variable chosen is not

I(0). If the calculated ADF statistics is less than the

critical value the null hypothesis is rejected,

otherwise accepted.

The ADF unit root test can be performed by

estimating the regression;

= ρy

t-1

+ α

Δy

+....+ α

ρ-1

Δy

t-ρ+1

+ε

As ordinary least square (OLS) is subjected to

spurious problem, cointegration analysis will be used

by taking the Johansen cointegration test.

Cointegration aims at explicitly dealing with the

relationship between non stationary time series and

spurious result can be avoided. This test is applied to

check whether the long run equilibrium relationship

exists between the variables. The cointegration test

will be based on two tests; trace test statistics and the

maximum eigenvalue test statistics.

Johansen cointegration test (Johansen, 1988 and

Johansen and Juselius, 1990) is used for testing

cointegration. Johansen cointegration test can be also

applied to check whether the long run equilibrium

exists between the variables to achieve the objective.

4 RESULTS AND DISCUSSION

Table 1 presents the result of estimation for

coefficient and the significance of the model. In the

multivariate regression, all the controlling variables

can reject the hypotheses where there are no

relationships between the controlling variables and

the volatility of stock price

Table 1: Results multivariate model

Variabl

Coefficien

Standar

error

T statistics

C 65.5883 0.4318 151.8915

CPI 25.06204 13.1311 1.9086*

IR 25.1234 3.6946 6.8001***

IPI 1.7837 1.3261 1.3451

ER 421.4466 17.0108 24.7752**

FC 72.4184 3.2484 22.2936**

Notes: *** 1% level of significance

**5% level of significance

*10% level of significance

As stated earlier the OLS is subjected to spurious

problem thus the Augmented Dickey-Fuller (ADF)

model is used to test the time series data correlated

each other. This test is conducted in order to check

whether the series of the data is stationary or non-

stationary. Table 1 provides the summary of

stationary test. Using 5% significance level, all

variables fail to reject the null hypothesis of non

stationary at the level form. Conversely, all variables

are stationary at their first difference form, since the

null hypothesis of non stationary can be rejected. All

variables are I(1) or integrated of order 1. The dummy

variable Financial Crisis is not included in the

Stationary Test as it was argued in Glynn and Perera

(2007) that dummy variable is an exception in this

test.

Table 2: Stationary Test

Variable Level

I(0)

First

differences

I(1)

ADF

Value

SP 0.959 21.218 3.457***

CPI 0.712 5.952 3.458***

IR 2.544 3.892 3.459***

IPI 0.906 3.394 2.874**

FC 2.829 4.398 3.458***

All the variables in Table 2 are significant at 1 %

significance level except for industrial production

index, which is significant at the 5 % level. The result

shows that all the variables are non-stationary in

level, but are stationary after first differencing. Since

the result from ADF has confirmed a series at first

Gauging Stock Price Volatility during the Financial Crisis using a Multivariate Cointegration Analysis

587

differences stationarity, the test of unit root strongly

suggest that all variables are integrated of order one

or I (1) by adjusting the maximum lag to 16. Since all

the variables are in the same order of integration then

it will continue to apply the technique of co-

integration.

Once the order of integration is established for

each variable, this paper continues to evaluate the

cointegration test for the data series. The

cointegration test is used to determine whether a

linear combination of the series has a long run

equilibrium.

Table 3: Johansen Cointegation Test Result

Unrestricted Cointegration Rank Test (Trace)

Hypothesized

No. of CE(s)

Eigenvalue Trace

statistics

0.05

Critical

Value

None * 0.1850 105.0128 95.7536

At most 1 0.0887 57.3468 69.8188

At most 2 0.0598 35.6852 47.8561

At most 3 0.0483 21.3014 29.7970

At most 4 0.0288 9.74921 15.4947

At most 5 0.0125 2.9362 3.8141

Unrestricted Cointegration Rank Test (maximum

Eigenvalue)

Hypothesized

No. of CE(s)

Eigenvalue Max eigen

Statistic

0.05

Critical

Value

None * 0.1850 47.6659 40.0775

At most 1 0.0887 21.6615 33.8768

At most 2 0.0598 14.3837 27.5843

At most 3 0.0483 11.5522 21.1316

At most 4 0.0288 6.81293 14.2646

At most 5 0.0125 2.9362 3.8414

Result from the Johansen cointegration test is

demonstrated in Table 3. From the analysis of the

maximum eigenvalue, the model shows the presence

of one cointegrating equation since these statistics

exceed their critical value at the 5% significance

level. Since there is a cointegrating relationship

between independent variable, the null hypothesis of

no cointegration can be rejected.

Based on the Johansen co-integration test, the

trace test (105.0128) are higher than the critical value

(95.7536) whereas the max-eigen value (47.6659) is

also higher than the critical value (40.0775). Since the

trace test and max-eigen is higher than critical value

it shows there is a long run equilibrium. The test

suggests that null hypothesis is rejected at the 5 %

significance level, which means there is at least one

co-integration equation.

The existence of cointegration implies variables

are cointegrated and there is a meaningful long run

relationship. Testing with provision of four lags, the

model exhibits no serial correlation and no normality

problem. Thus, the study has proceeded to the next

step to find out the magnitude of the long run

relationship. Estimated long run equilibrium model is

as follows;

Table 4: Results of long run equilibrium

Variable Coefficient Standard

error

T statistics

CPI 55.4430 13.2603 4.1811***

IR 306.5999 54.0475 5.6727***

IPI 0.2200 9.5646 0.0230

ER 285.27 183.851 1.5516

FC 244.7160 145.126 1.6862

Notes: *** 1% level of significance

In multivariate regression result (Table 1), consumer

price index is found to be positively and statistically

significant in affecting the stock price in Malaysia.

The result in the long run (Table 4) also shows the

consumer price index the magnitude of positive

relationship and significantly affects the stock price.

The stock price will increase if the consumer price

index responds to increase in economy as founded by

Yahya et al (2012), Priyanka and Kumar (2012),

Sikalao-Lekobane and Lekobane (2014), Seyed,

Zamri and Lai (2011), Mahmoud, Sara and Khaled

(2016), and Asmy et al (2009). When inflation

increases because of an increase in demand that

exceeds the current supply, the firm income will

increase along with their dividends, which will make

the stock more attractive and more people are willing

to invest in market share, hence, the stock price will

increase. Moreover, stock prices are a good hedge

against inflation. It is because equity as value

protection from threat of inflation and has a claim on

a real asset to prove that the higher the inflation rate,

the higher the demand for a particular share.

From the results, interest rate shows a positive

magnitude and highly significant relationship

between stock price in both OLS and long run

equilibrium. When the interest rate is increasing, the

stock price will increase. The finding is parallel with

Ray (2012), Garza-Garcia and Yue (2010), Sikalao-

Lekobane and Lekobane (2014) and Yogaswari,

Nugroho and Astuti (2011). When interest rate

increase, demand on the deposit will increase rather

than going for investment because the cost of

borrowing is costly. Therefore, the return on the

deposit may increase. The interest rate will also

directly affect the rate of discount in valuation model

of stock price, in another word, future cash flow and

current cash flow receive by the investor would be

affected.

For industrial production index, the findings from

multivariate analysis in OLS and long run equilibrium

EBIC 2019 - Economics and Business International Conference 2019

588

show there are positive relationships but insignificant

impact towards stock price. Yahya et al. (2012), Naik

and Padhi (2012), Garza-Garcia and Yue (2010), Ray

(2012), Seyed, Zamri and Lai (2011) and Aamir,

Muhammad, Rehan and Hamza (2014) indicate the

positive relationship between industrial production

index and stock price. When the industrial production

has produced more products, it shows that the firms

provide more profit or return on shareholders who are

investing in those firms. When shareholders can get

high returns, the demand of stock is increasing and

stock prices will be higher.

The result from OLS shows that exchange rate has

negative and significant relationship with stock price.

Nonetheless, in the long run it shows that exchange

rate stock price has positive and insignificant

relationship towards stock price. The finding from

OLS is consistent with Yahya et al (2012), Ray

(2012), Naik and Padhi (2012), Sikalao-Lekobane

and Lekobane (2014) and Aamir et al. (2014). As the

exchange rate is depreciated, the share price will rise,

while if the exchange rate is appreciated, the stock

price will decline. The reason is the status of the

country is depending on the value of exports

(international trade). The declining in value of

currency will encourage more exports. Therefore,

when there is a lot of export occurs for that product,

the sales and revenue will increase which lead to

increase in the stock price.

Lastly, the major focus of the study is the financial

crisis. Finding from multivariate analysis reveals that

financial crisis has a negative and statistically

significant relationship with the volatility of stock

price. T statistics is found to be at 22.2936 with

significance level of 1%. A negative magnitude

indicates when financial crisis happens stock price

will decrease. It creates an impact and instability to

the stock market. Finding obtained is parallel with

Adamu (2010), Sakthivel et al (2014), Gabriel and

Manso (2014), Lee and Jeong (2014), Rafaqet and Ali

(2012), Kishor and Singh (2014). Financial crisis

creates instable volatility of stock price as people will

tend to sell the stock that can give a loss to them and

the price of that particular stock will be decreased.

In the long run equilibrium, a magnitude of

positive relationship is found which is not compatible

with the theory. This finding however can be rejected

as the financial crisis does not statistically significant

with stock price. Even though there is a cointegration

in the model, in the long run equilibrium financial

crisis does not show the impact to the stock price. The

impact of shock event might occur instantly on stock

price has been covered by many researchers (Rafaqet

& Ali, 2012; Sakthivel et. al, 2014) using EGARCH

and GARCH method. The impact of financial crisis is

seemed not to be delayed or lagged after periods of

time.

5 CONCLUSIONS

The purpose of the study is to gauge the volatility of

stock price during the financial crises. This study has

developed a framework to be tested using the OLS

method and cointegration analysis. The robustness of

the result was tested under various aspects. It was

found that the financial crisis does influence the

volatility of stock price using the OLS method. While

all the macroeconomic variables which work as

control variables are significant with the expected

signs as found in the previous research. These

macroeconomic factors are really important in

influencing the volatility of stock price in Malaysia.

Nonetheless, contrary result is found when testing

with a more dynamic cointegration method. In the

long run equilibrium is insignificant relationship is

revealed. In conclusion the finding of the study

supports the claim that stock price is affected during

the financial crisis. Nonetheless, when testing using

the long run equilibrium the insignificant result is

found showing the impact does not exist in the long

run equilibrium.

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