An Empirical Study on Influence of Inflation on

www.theinternationaljournal.org > RJSSM: Volume: 05, Number: 4, August 2015 Page 41

An Empirical Study on Influence of Inflation on performance of Sensex in India

Dr. Aparna Mishra

Asst. Professor

BanarsidasChandiwala Institute of Professional Studies,

Sector 11, Plot no. 9, Dwarka, New Delhi 110075

Dr Ajay Kumar Chauhan

Asst. professor

Institute of Management Technology, Gaziabaad

Raj Nagar, Hapur Road, Block 15, Sector 2, Raj Nagar, Ghaziabad, Uttar Pradesh 201002

Abstract

Stock Market plays a vital role in any countries economic growth and development. They have always

been an area of serious concern for policy makers, economists and researchers. They are often defined

as the barometer of any economy because they reflect the change and direction of pressure on the

economy.The available literature suggests thatsince the inception of stock markets researchers are

making attempts to establish relationship between change in macroeconomic factors and stock market

returns. The main domestic macroeconomic factors affecting the stock market in long run are

industrial production;inflation(wholesale price index) and interest rate. The present research study

makes an attempt to study the lag-lead relationship between Inflation and stock returns after analyzing

the inflation into expected and unexpected components. The stock returns-inflation relationship was

examined during the period 1998 to 2008 using indexes of BSE Sensex with WPI(Wholesale Price

Index). The period is characterized by different reforms in Indian economy and the global meltdown.

Therefore, boom and recessionary phases were observed during this period. The results obtained

through all standard econometric tests showed that there is no relation between stock returns and

inflation during the studied period. The unit root tests, Granger causality test and regressions were

performed for the purpose. The unitroot tests indicated that both the series, i.e., Inflation and Sensex

returns are stationary.The Granger causality test results suggested no significant relation between stock

returns and inflation.

Thus, the results of the study suggest that there exists no significant relation between inflation and

stock returns in the post-reform period in India. It implies that stock returns do not provide a hedge

against inflation. It can be said that investors aim at better returns and do not invest in stocks to hedge

against inflation.

Key Words: Inflation, WPI, Sensex Return, Macro Economic Variables, Stationary, Volatility

Introduction:

Stock market plays a vital role in any country‟s economic growth and development. A healthyand

flourishing stock market has been considered relevant for national economic growth by channelizing

capital toward investors and entrepreneurs. An economy is said to be efficientif it has a good banking

system and a good stock market exhibiting upward trend. Earliera country was considered strong and

efficient if it exhibited a sustained growth of GrossDomestic Product (GDP) and per capita income.

But, of late it has been recognized thatstock market exerts greater influence on national economy.

Market capitalization, savings,investment, consumption and sound banking and insurance system are

considered to be afew important indicators of economic growth. Several researchers have investigated

the relation between stock returns and inflation over many years. Equities have always traditionally

been regarded as a good hedgeagainst inflation because equities are claim against physical assets

whose real returns shouldremain unaffected by inflation. The Fisher (1930) hypothesis, in its most

familiar version,states that “the expected nominal rate of return on stock is equal to expected inflation

plusthe real rate of return”, where the expected real rate of return is independent of expectedinflation.

In other words, Fisher hypothesis implies that stocksoffer a hedge against inflation.Although a number

of papers have investigated the Fisher hypothesis, the emphasis hasbeen on the developed countries.


Recently, the focus of research has been shifted towarddeveloping countries, partly due to rapid

growth and increasing liberalization in thesecountries. For instance, Spyrou (2001) and Floros (2004)

examined stock returns-inflationrelation in Greece, using the Johansen cointegration test. They found

that there is nosignificant long-run relationship between inflation and stock returns in Greece. Davis

andKutan (2003) investigated the Fisher effect in 13 developed and developing countries andfound

evidence that Fisher effect is not supported in international stock returns. Al-Khazaliand Pyun (2004)

investigated the statistical relationship between stock prices and inflationin nine countries in the

Pacific Basin. Using the Johansen cointegration test, they concludedthat stock prices in Asia reflect a

time-varying memory associated with inflation shocks thatmake stock portfolios a reasonably good

hedge against inflation in the long run. Fama (1981) argued that the negative relationship between

stock returns andinflation has its basis in the money demand theory and the quantity theory. Fama‟s

hypothesisstates that rising inflation rates reduce real economic activity and demand for money.

Wheneconomic activity dips, it negatively affects the future corporate profits and, hence stockprices.

The negative relationship between inflation and the stock returns is on account ofthe „proxy effect‟ in

the sense that it reflects the detrimental consequence of inflation onreal economic activity. According

to Fama, the statistical relationship between inflation andstock returns should disappear once the effect

of real output growth is controlled for. Geske and Roll (1983) studied the relationship forthe US for the

period from 1953:1 to 1980:12.The major empirical findings of the study were that, there is a

negativerelationship between stock returns and beginning of the period short-term interest

rate,contemporaneous change in short-term interest rates, and unanticipated inflation. Ahmadand

Mustafa (2005) studied the relationship for Pakistan, for the period from 1972-2002using monthly and

annual data. Results revealed that relationship between real returns and unexpectedgrowth and

unexpected inflation are negative and significant. Kim (2003) employed quarterly data for Germany

for the period from 1971 to 1994. Symmetric and asymmetric Granger causality test was

performed.Results demonstrated the negative correlation between stock returns and inflation and

theindicative role of stock returns in the real activity in an asymmetric manner of causality.Nelson

(1976) using the monthly data, studied the relationship for the US for the postwarperiod, 1953-1972.

Box and Jenkins‟ ARIMA method was used. The studydemonstrated a negative relationship between

stock returns and both expected andunexpected inflation.Samarokoon (1996) studied the relationship

between stock returns and inflation forSri Lanka, using the monthly and quarterly data for the period

1985 to 1996.The Box and Jenkins‟ ARIMA model was used. Empirical findings showed stock

returnsdo not provide hedge to Sri Lankan inflation. Jaffe and Mandelker (1976) utilized themonthly

data from 1953 to 1971 to study the relationship for the US. Results of thestudy revealed no relation

between stock returns and inflation. Kaul (1987) using dataseries in annual form for the post-war

period analyzed the relationship for the US(1953-1983), Canada (1951-1983), the UK (1957-1983),

and Germany (1957-1983).Findings of the study indicated that negative inflation-real activity relations

reinforcedby counter-cyclical monetary responses, explain the negative relation between stockreturns

and inflation witnessed in the post-war period.

Adam and Frimpong (2010) studied the relationship for Ghana for the sample period1991-2007.

Cointegration analysis was employed and the findings showed strongsupport for hedge hypothesis.

The evidence confirms that Ghana market is efficient ininflationary environment as investors are

compensated with high stock returns. Chopen andZhong (2001) studied the post-war period from 1968

to 1996. Vector Error CorrectionModel (VECM) of Johansen and Juselius (1992 and 1994) was

employed. The results revealedthe presence of economically meaningful long-run

relationships.Balduzzi (1994) studied the proxy hypothesis for the period from 1977 to

1990,employing the Vector Autoregressive (VAR) and Vector Moving Average (VMA)

models.Results revealed that inflation itself is responsible for most of the dynamic interactions

withstock returns. Lee(2008) analyzed the causal relationship in the UK. The sample period ranged

from 1830to 2000. The sample period was further divided into two subperiods, 1830-1969 and 1970-

2000.Unit root test, cointegration test, Bivariate Vector Autoregressive (BVAR) and GARCHmodels

were employed. The empirical findings of the study reported that there is a significantnegative

correlation between unpredictable stock returns and inflation for the subperiod 1970-2000. However,


unpredictable stock returns were hardly correlated to unpredictable inflationduring the same

subperiod.Adrangi and Chatrath (1997) investigated possible negative relationship between thereal

stock returns and unexpected inflation for Brazil. Bhattacharya and Mukharjee (2003) studied the BSE

Sensex for the period ranging from1992-93 to 2000-01. They employed different techniques, such as

unit root tests,cointegration test, long-run Engel and Granger causality test, and the recently

developedToda and Yamamoto (1995) test. Results revealed existence of a bidirectional

causalityrelationship between stock returns and rate of inflation.Shanmugam and Misra (2008) studied

an emerging economy like India during thepre- and post-reform periods, 1980 to 2004, covering 288

months. The studycontributed to the stock returns-inflation relation in India. It tested whether the

Indianstock market provided an effective hedge against inflation. Ordinary Least Square

(OLS)regression method was employed. As the single equation treatment can lead to aninconsistent

estimate, therefore a two-step OLS procedure was employed to study therelationship between real

returns and inflation. The results demonstrated that the stockreturns and inflation are negatively related

when the whole sample period of 24 yearsis considered. With the lead of about six months, real

activity and inflation are negativelyrelated, while real activity positively influences the real returns.

The results provideda strong support to Fama‟s proxy effect. In addition, the analysis of sub periods

indicated that Fama hypothesis was valid only in the pre-reform period and not in the post-

reformperiod. The real stock returns were found to be independent of inflation during the post

reformperiod.

In this backdrop, the present study seeks to examine the stock returns-inflation relationshipin India

using the sample of BSE Sensex 1998:1 to 2008:12.The period is characterized by different reforms in

Indian economy and the global meltdown.Therefore, boom and recessionary phases of the economy

are observed during this period. During this period, the data series is analyzed with Wholesale Price

Index (WPI). The study attempts to understand the relationship between stock returns and inflation to

empirically assess and understandthe relationship between them in the post-reform period in India. The

empirical results of unit root, Granger causality,VAR model and Impulse Response Function (IRF) are

presented and discussed. Finally, theconclusion is offered.

Data and Methodology

Data

The study uses weekly values of BSE Sensex. The BSE Sensex indexes are considered as barometer of

Indian equitymarket and account for a major part of market capitalization and turnover. WeeklyWPI

are used as measures of inflation. WPI is a representative of the prevailing price situation because of

itswider coverage. It is available with a smaller lag ofone week, which is used extensively as a

measure of rising pricesin India, and important monetary and fiscal policy changes are often linked to

it.. The sample size chosen for the study gives asufficient number of observations to apply time series

methods. The index values of BSESensex are obtained from www.bseindia.com, while WPI data are

obtained from Central Statistical Organization(CSO) and Reserve Bank of India (RBI) respectively.

Methodology

In order to examine the relationship between stock returns and inflation time serieseconometrics tools,

such as unit root tests, Granger causality test and regression analysisare employed. The unit root tests,

Augmented Dickey-Fuller (ADF) andKwiatkowski, Phillips, Schmidtand Shin (KPSS) (Kwaitkowski

et al., 1992), are employed to test if the data series is stationaryor not. Further, to examine the causal

links between stock returns and inflation during entiresample period and for the subperiods, Granger

(1969) causality test is employed. Multiplelinear regression equation is also estimated to examine the

relationship between stock returnsand inflation. Vector Auto Regression is performed along with

IRF(Impulse Response Function) to test for robustness ofthe results.

Stock Market and Inflation in India

The relationship between stock market prices and inflation is of great importance from the policy point

of view. High inflation in the economy is considered harmful for the growth of the economy. Increase

in inflation is considered as a negative signal by the Central banks, especially in the developing

economies such as Indian economy. The monetary policy of a developing economy in most of the


cases is focused to control the increase in inflation in the country. In high inflationary period the banks

increases the lending rates as well as deposit rates. This will increase the cost of capital for the industry

and attract investors to invest theirsavings in debt securities. In such a scenario the stock market have

more bearish sentiments, showing the downside movements. However, in real world the relationship

between stock price and inflation is more complex. The stock market prices may be related to the

domestic inflation and even if domestic inflation may not affect the quantity produced directly there

can be substantial impact of stock market prices on quantity produced.

In the developed world the stock market controls the real sector immensely whereas in the Indian

context the stock market used to be quite superfluous in this respect. That is because only a few

players control the stock market. However, over time the government intervention has tried to rule out

such “bull effect” and has made stock market more competitive which in return is expected to have

made both the stock market and other macro variables sensitive to each other. This motivation

prompted for theinvestigation into the questions such as“What is the relationship between inflation and

stock returns?” Holdingstocks is often thought to provide a good hedge against inflation, since the

payments to equity holders are not fixed in nominal terms and represent a claim on real assets.

The majority of empirical studies that have investigated the signof this relationship have found it to be

negative. Various explanationsof this puzzling empirical phenomenon have been proposed, including

alink through real activity. So, that real activity is negatively related to inflationbut positively related

to stock returns and therefore stock returnsand inflation is positively related. Clearly, inflation and

stock returns oughtto be simultaneously related given that the rate of inflation will affect the discount

rate applied to cash flows and therefore the value of equities,but the performance of the stock market

may also affect consumerdemand and therefore inflation through its impact on householder

wealth(perceived or actual).

In stock market in general which represent claims against the real assets of a business, may serve as a

hedge against inflation. Consequently, investors would sell financial assets in exchange for real assets

when expected inflation is pronounced. Thus, stock prices in nominal terms should fully reflect

expected inflation and relationship between stock prices and expected inflation should be found

positively correlated. Equities are assumed to be a hedge against inflation due to the fact that they

represent a claim to real assets and, hence the real change on the price of the equities should not be

affected. If we consider that firms are in a position to predict their profit margins and since equities are

claims on current and future earnings, it also follows that the stock market operates as a hedge against

inflation, at least in the long run.The earnings should be consistent with the inflation rate, and hence

the real value of the stock market should remain unaltered in the long run. The argument that stock

market serves as a hedge against inflation, implies that investors are fully compensated for increases in

the general price level through corresponding increases in nominal stock market returns and thus the

real returns remain unaffected. In other words, the argument is that the real value of the stock market is

immune to inflation pressures. This has been tested in the literature numerous times.

Changes in expected inflation are negatively related to stock returns. The present research study makes

an attempt to study the lag-lead relationship between stock returns and inflation after analyzing the

inflation into expected and unexpected components. It will examine whether expected and unexpected

components of inflation influence stock prices in positive direction or in negative direction, and

whether stock market responds to these components of inflation proactively or reactively. In the

research study, an effort is made to analyse the relationship between the level of inflation and stock

return behaviour. The effort is made to understand the nature of the time series data, the long-term

equilibrium relationship between the WPI and SENSEX, the contemporaneous and causal relationship

between the WPI and SENSEX. This paper is contributingto the emerging line ofresearch linking stock

return predictability to economic real activities (WPI).

The graphical representations of the behaviour of SENSEX and WPI during the sample period are

shown in fig 1.1 and fig 1.2. The figure indicates the presence of increasing trend in both the

variables.


0

4,000

8,000

12,000

16,000

20,000

24,000

4/6/98

8/24/9

8

1/11/9

9

5/31/9

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10/18

/99

3/6/00

7/24/0

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12/11

/00

4/30/0

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9/17/0

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6/24/0

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/02

3/31/0

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8/18/0

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1/5/04

5/24/0

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2/28/0

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4/24/0

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1/29/0

7

6/18/0

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11/5/

07

3/24/0

8

8/11/0

8

12/29

/08

BSE

Fig 1.1: SENSEX behaviour during the sample study.

120

140

160

180

200

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260

4/6/98

8/24/9

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6

1/29/0

7

6/18/0

7

11/5/

07

3/24/0

8

8/11/0

8

12/29

/08

WPI

Fig 1.2: Wholesale Price Index during the sample study

0

2

4

6

8

10

12

14

4/6/98

8/24/9

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1/11/9

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6/18/0

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08

Change (%)

Fig 1.3: Wholesale Price Index (Calculated in percentage change) during the sample study

The table 1.1 and 1.2 indicates the descriptive along with the distribution of SENSEX and WPI series

during the sample study. The results indicate that the mean value of SENSEX during the sample period

is 7086 with the standard deviation of 4905. The high Standard Deviation value indicates the high

level of deviations in SENSEX during the sample period. The results also indicate the presence of

positive skewness and leptokurtic behaviour of SENSEX prices because of which, the distribution is

also not normal as indicated by probability value (0.000) of Jarque- Bera test statistic (260.1895).The

highest value of SENSEX is found to be 20812 during the period. The minimum SENSEX value

during the sample period is found to be 2651.


Table 1.1: Descriptive Statistics of BSE SENSEX

0

20

40

60

80

100

120

4000 6000 8000 10000 12000 14000 16000 18000 20000

Series: BSE

Sample 4/06/1998 12/29/2008

Observations 561

Mean 7086.166

Median 4905.890

Maximum 20812.65

Minimum 2651.780

Std. Dev. 4631.286

Skewness 1.144197

Kurtosis 3.101120

Jarque-Bera 122.6481

Probability 0.000000

The results as indicated in table 1.2 and table 1.3 represents the descriptive statistics of the WPI series

and the growth rate in inflation in India during the sample period (1998 to 2008). The results indicate

that the average value of WPI index and its growth rate is 179and 5.27 percent respectively with the

standard deviation of 28 and 2.16. In India it is observed that the level of inflation is having too much

variations and it poses a serious problem in front of RBI to effectively control it. The high level of

variance in WPI series and its growth rate indicates the volatile nature of level of inflation in the

country. The presence of low level of positive skewness and leptokurtic behaviour in the distribution

of the series is observed in the results.

The probability distribution of the WPI series is also not normal as indicated by probability value

(0.000) of Jarque- Bera test statistic (31.616). The highest value of WPI index is observed as 241

during the period. The highest growth rate in inflation is found to be 12.82 percent. The minimum IIP

Index and its growth rate during the sample period are found to be 136 and 1.06 respectively.

Table 1.2: Descriptive Statistics of WPI Index

0

10

20

30

40

50

60

70

140 150 160 170 180 190 200 210 220 230 240

Series: WPI

Sample 4/06/1998 12/29/2008

Observations 561

Mean 179.2658

Median 173.8000

Maximum 241.7000

Minimum 136.3000

Std. Dev. 28.25341

Skewness 0.395817

Kurtosis 2.148003



Table 1.3: Descriptive Statistics of growth in WPI Index

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13

Series: WPI_CHANGE

Sample 4/06/1998 12/29/2008

Observations 561

Mean 5.275767

Median 5.201238

Maximum 12.82171

Minimum 1.062500

Std. Dev. 2.163216

Skewness 0.946173

Kurtosis 4.864633



1.2Testing for Stationary/Non Stationary series

A time series is said to be strictly stationary if all the moments of its probability distribution (such as

mean, variance, skewness, kurtosis etc.) are invariant over time and weakly stationary process (also

called covariance stationary or 2nd-order stationary)if its mean, variance and auto-covariance remain

the same no matter at what point of time they will be measured.The stationary nature of the time series

(SENSEX and WPI) is analysed using Correlogram and unit root test.


1.2.1 Correlogram

The Correlogram is the graphical representations of the set of autocorrelation coefficients (ACF) and

Partial autocorrelation coefficients (PACFs) at various lags. It is observed that anon-stationary series

will show very high autocorrelation close to 1.The Correlogram of the WPI and SENSEX data is

shown in fig 1.4 and fig 1.5 shown below:

Fig 1.4: Correlogram of SENSEX values

As shown, the Correlogram the autocorrelation function (ACF) is not exponentially dying and the

partial auto correlation function (PACF) at lag 1 is close to one. This indicates that the SENSEX

values are non-stationary in nature. But this is yet to be confirmed with the results of ADF unit root

test. If the series is found to be non-stationary, then some transformation is required to make the series

stationary.

Fig 1.5: Correlogram of WPI Series


As shown in the Correlogram of the wholesale price index values, the autocorrelation function (ACF)

is not exponentially dying and the partial auto correlation function (PACF) at lag 1 is close to one.

This indicates that the WPI values may be non-stationary in nature, which will be reconfirmed with the

results of ADF unit root test.

The fig 1.6 and fig 1.7 represents the Correlogram of the growth rate of WPI and SENSEX returns. In

both of the cases the partial auto correlation function (PACF) at lag 1 is significantly less than one,

which indicates the stationary nature of the series.

Fig 1.6: Correlogram of growth rate of WPI

Fig 1.7: Correlogram of SENSEX returns

1.2.2. ADF Unit Root Test

Since many financial time series (BSE SENSEX and WPI in this study) are random walk or non-

stationary time series and contains unit root. Test of the presence of the unit root in the BSE SENSEX

and WPItime series is necessary as its presence may give invalid inferences in the analysis. Table 1.4

shown below indicates the results of unit root test applied on the BSE SENSEX and WPI using ADF

test.


Table 1.4: Augmented Dicky Fuller Test of BSE SENSEX and WPI data

Series

ADF Unit Root Test Statistic

None With Intercept With Trend and

Intercept

Monthly data of BSE

SENSEX

At Level 0.133

(0.637)

1.098

(0.718)

1.841

(0.683)

At First

Difference

11.098

(0.000)

11.504

(0.000)

11.494

(0.000)

Monthly Data of IIP

At Level 6.201

1.000)

0.335

(0.916)

2.741

(0.220)

At First

Difference

7.636

(0.000)

19.139

(0.000)

19.122

(0.000)

The result indicates that both the time series BSE SENSEX and WPI are a random walk and non

stationary al their level (prices) but becomes stationary at their first difference. Hence for further

analysis the return series of SENSEX and growth rate of WPI is used. The returns of the SENSEX data

can be calculated using the following formula:

SENSEX Return = Ln (P1/p0) 1.1

Where P1 represents the SENSEX value of a particular month and Po represents the SENSEX value of

previous month.

Similarly the growth rate of the WPI data can be calculated using the following formula:

Growth Rate of Inflation =Ln (I1/I0) 1.2

Where I1 represents the WPI Index value of a particular week and Io represents the WPI value of

previous week.

1.3 The long term relationship between Inflation and SENSEX: A Cointegration Approach

The increase in the inflation rate in the economy motivates the central bank to increase the interest

rates in the economy in order to control the inflation. As a result the investors in the market also shift

their savings to debt securities due to increase in rates. The stock market in this scenario as an

indicator of the economy comes in bearish mode. Hence there may a long term equilibrium

relationship is existing but inverse in nature between inflation and the stock market. The existence of

this long term equilibrium relationship between the inflation ratemeasured by WPI and stock market

represented by BSE SENSEX can be tested using Johansan‟scointegration test. The cointegration test

originally was introduced by Granger (1981, 1983) and Engle and Granger (1987) to explain stationary

equilibrium relationship among the non-stationary variables. The cointegration test is useful in

analyzing the presence of a stationary linear combination among the non-stationary variables of the

same order. If such combination is found, an equilibrium relationship maybe existing between the

variables. The Johansen cointegration test is applied in the research study between the inflation rate

(WPI) and SENSEX weekly values. The result of the Johansen‟s Co-Integration Test is shown in table

1.5.

The result indicates that the probability value of both Trace test and Max Eigen value ofJohansen‟s

Co-Integration Test is more than five percent level of significance; hence at 95 percent level of

confidence the null hypothesis of “no Co-integrating relationship between inflation and stock market”

can be accepted. Hence as per the results obtained from the data it can be concluded that the inflation

rate in India and stock market is not having long-term equilibrium relationship between them. In fact

the stock market is affected by a large number of factors in which inflation may be one of them. Hence

due to the absence of any error correction mechanism between the stock market and inflation rate, no

long term or co integrated relation exists between them.

Table 1.5: Johansen’s Co-Integration Test on WPI and SENSEX values

Cointegration

Between

Lag

length

selected

Cointegration

test using

No. of

Cointegrating

Equations

(CEs)

Eigen

Value

Statistic Critical

value

at 5%

Probability

**


Index of

Industrial

Production

(IIP) and

SENSEX

1 to 4 ( in

first

difference

of 2

series)

Trace test

H0: r=0

(None)

H1: r ≤ 1 (At

most 1)

0.012

4.37E-

05

7.261

0.024

15.494

3.841

0.547

0.876

Max-Eigen

Value test

H0: r=0

(None)

H1: r ≤ 1 (At

most 1)

0.012

4.37E-

05

7.236

0.024

14.26

3.841

0.461

0.876

Trace test indicates 1 Cointegrating equation at 5% level of significance

Max-Eigen test indicates 1 Cointegrating equation at 5% level of significance

Denotes rejection of null hypothesis at 5% level of significance

**Mackinnon et.al.(1999) estimated p values

1.4 Relationship between WPI and SENSEX

In the research study the weekly data of WPI and SENSEX is considered for the period between Jan

1998 up to Dec 2008. In the research study the effort has been put to analyse the relationship between

inflation rate and stock market behaviour. The analysis has been done with the help of correlation.The

results of the correlation analysis are shown in the table 1.6.

Table 1.6: Correlation Analysis between WPI and SENSEX

Correlation Between Pearson Correlation Sig. (2-tailed)

WPI and SENSEX -.118 0.005

1.4.1 Impact of Unexpected Component on stock market

There are two components of inflation rate in India: expected and unexpected. In order to analyse the

impact of expected and unexpected component of inflation rate on stock market, the forecasting model

is developed using ARIMA forecasting method. The forecasted values are saved and subtracted from

the observed values of WPI. The difference between the actual WPI values and estimated values are

considered as the proxy of unexpected component of inflation rate. The following ARIMA model is

applied in order to find out the expected values of WPI values:

(Growth rate in WPI)t = α + β1 GWPIt-1 + β2 GWPIt-2 + β3 GWPIt-4 1.3

Where, GWPIt-1 =Growth rate in WPI at lag 1

GWPIt-2 =Growth rate in WPI at lag 2

GWPIt-4 =Growth rate in WPI at lag 4

ARIMA"Auto-Regressive Integrated Moving Average." (p,d,q)models are the most general class of

models for forecasting a time series which can be stationery by transformations such as differencing

and logging. In fact, the easiest way to think of ARIMA models is as fine-tuned versions of random-

walk and random-trend models: the fine-tuning consists of adding lags of the differenced series and/or

lags of the forecast errors to the prediction equation, as needed to remove any last traces of

autocorrelation from the forecast errors.

In ARIMA, lags of the differenced series appearing in the forecasting equation are called "auto-

regressive" terms, lags of the forecast errors are called "moving average" terms, and a time series

which needs to be made stationary is said to be an "integrated" version of a stationary series. A non-

seasonal ARIMA model is classified as an "ARIMA(p,d,q)" model, where:

p is the number of autoregressive terms,

d is the number of non-seasonal differences, and

qis the number of lagged forecast errors in the prediction equation.

To identify the appropriate ARIMA model for a time series, the process begins by identifying the

order(s) of differencefor making the series stationary and to remove the gross features of seasonality,

perhaps in conjunction with a variance-stabilizing transformation such as logging or deflating. The

results of ARIMA forecasting model applied on WPI is shown below in table 1.7:


Table 1.7 ARIMA Model

Dependent Variable: Growth Rate in WPI

Method: Least Squares

Variable Coefficient Std. Error t-Statistic Prob.

C 0.000899 0.000177 5.084772 0.0000

AR(1) 0.184216 0.042236 4.361609 0.0000

AR(2) 0.097293 0.042426 2.293216 0.0222

AR(4) 0.092815 0.042212 2.198795 0.0283

R-squared 0.065998 Mean dependent var 0.000914

Adjusted R-squared 0.060922 S.D. dependent var 0.002689

S.E. of regression 0.002606 Akaike info criterion -9.054831

Sum squared resid 0.003749 Schwarz criterion -9.023747

Log likelihood 2521.243 Hannan-Quinn criter. -9.042690

F-statistic 13.00174 Durbin-Watson stat 2.018413

Prob(F-statistic) 0.000000

The residuals of the above mentioned ARIMA model and its Correlogram are shown in fig 1.8. The fig

indicates that that the residuals are stationary and the model is assumed to be fit.

Fig:1.8: Correlogram of the residuals saved of the ARIMA model

The diagram of forecasted Inflation (WPI) series is shown in fig 1.9.

120

140

160

180

200

220

240

260

5/12/9

8

9/28/9

8

2/15/9

9

7/5/99

11/22/

99

4/10/0

0

8/28/0

0

1/15/0

1

6/4/01

10/22/

01

3/11/0

2

7/29/0

2

12/16/

02

5/5/03

9/22/0

3

2/9/04

6/28/0

4

11/16/

04

4/4/05

8/22/0

5

1/9/06

5/29/0

6

10/16/

06

3/5/07

7/23/0

7

12/10/

07

4/28/0

8

9/15/0

8

Forecasted WPI Series ± 2 S.E. Fig1.9: Forecasted WPI series


The statistics associated with the forecasted model are shown below in table1.8:

Table 1.8: Forecasting Error Statistics

Statistic Value

Root Mean Squared Error 0.4952

Mean Absolute Error 0.3201

Mean absolute Percent Error 0.1762

The difference between the forecasted values of WPI and the actual values of WPI are considered as

unexpected values of inflation. In the research study the effort is made to analyse the impact of

unexpected inflation on the stock market. The regression equation can be expressed as:

SENSEX =α + β .Unexpected Component of inflation 1.4

Where α and β are regression coefficients.

The results of the regression are shown in table 1.9:

Table 1.9: Regression Results

Dependent Variable: SENSEX Returns

Variable Coefficient Std. Error t-Statistic Prob.

Unexpected component of inflation 0.01018 0.00361 2.81935 0.005

C 0.00149 0.00178 0.83796 0.402

R-squared 0.014145 Mean dependent var 0.001502

Adjusted R-squared 0.012366 S.D. dependent var 0.042429

S.E. of regression 0.042166 Akaike info criterion -3.490812

Sum squared resid 0.984999 Schwarz criterion -3.475269

Log likelihood 972.4457 Hannan-Quinn criter. -3.484741

F-statistic 7.948784 Durbin-Watson stat 2.109635

Prob(F-statistic) 0.004984

The results of the regression model indicate that the stock market is significantly influenced by the

unexpected component of the inflation rate in the Indian economy. However the impact is very low in

magnitude as represented by low value of R- square of 0.149 percent.

1.4.2 The Causal relation between the inflation rate and the stock market in India

The lead lag relation between the inflation and the stock market can be analysed with the help of

Vector Auto Regression model. Thevector auto regression(VAR)modelis one of the most

successful,flexible, and easy to use models for the analysis of multivariate time series. It isa natural

extension of the Univariate autoregressive model to dynamic multivariate time series. The VAR model

has proven to be especially useful fordescribing the dynamic behaviour of economic andfinancial time

series andfor forecasting. It often provides superior forecasts to those from univariate time series

models and elaborate theory-based simultaneous equationsmodels. Forecasts from VAR models are

quiteflexible because they can bemade conditional on the potential future paths of specified variables

in themodel.

In addition to data description and forecasting, the VAR model is alsoused for structural inference and

policy analysis. In structural analysis, certain assumptions about the causal structure of the data under

investigation are imposed, and the resulting causal impacts of unexpected shocks orinnovations to

specified variables on the variables in the model are summarized. These causal impacts are usually

summarized with impulse response functions and forecast error variance decompositions.

Before analysing the VAR model the optimum lag is to be identified. The optimum lag length can be

identified with the help of lag length criteria such as Akaike information criterion, Schwartz

information criterion etc. The results of lag length criterion are shown below:


Table1.10: Lag length Criterion

VAR Lag Order Selection Criteria

Endogenous variables: SENSEX Returns

Lag LogL LR FPE AIC SC HQ

0 567.0708 NA* 0.000436* -2.062302* -2.046586* -2.056160*

1 568.6816 3.203822 0.000440 -2.053582 -2.006433 -2.035154

2 569.0001 0.631226 0.000446 -2.040146 -1.961565 -2.009433

3 571.6277 5.188094 0.000448 -2.035138 -1.925123 -1.992139

4 572.2114 1.148199 0.000454 -2.022669 -1.881222 -1.967385

5 575.4484 6.344024 0.000455 -2.019885 -1.847005 -1.952315

6 576.6271 2.301442 0.000460 -2.009588 -1.805275 -1.929733

7 581.4159 9.315493 0.000458 -2.012467 -1.776722 -1.920327

8 585.6392 8.184668 0.000458 -2.013282 -1.746104 -1.908857

* indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error

AIC: Akaike information criterion

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion

The results of lag length criterion indicate that the optimum length is one. This may be due to the fact

the stock market reacts immediately to the unexpected component of the inflation rate. Before

analysing the VAR model the optimum lag is to be identified. The optimum lag length can be

identified with the help of lag length criteria such as Akaike Information Criterion, Schwartz

information criterion etc.

In order to analyse the lead-lag relation between the inflation rate and stock market of India, the Block

Exogeneity Wald test is applied. The results of the Block Exogeneity Wald test are shown below in

table1.11. The results indicate no causal relation between the WPI rate and stock market of India.

Table: 1.11: Block Exogeneity Wald Tests

VAR Granger Causality/Block Exogeneity Wald Tests

Dependent variable: SENSEX Returns

Excluded Chi-sq Df Prob.

Unexpected Inflation Rate 0.296695 2 0.8621

All 0.296695 2 0.8621

Dependent variable: Unexpected component of inflation rate in India

Excluded Chi-sq df Prob.

SENSEX returns 0.274226 2 0.8719

All 0.274226 2 0.8719

The impulse response traces the responsiveness of the dependent variable in the VAR to shocks to

each of the endogenous variables. So, for each variable from each equation of the VAR separately, a

unit shock is applied to the error, and the effects upon the VAR system over time are noted. The

ordering of the endogenous variables may affect the results of impulse response; hence the generalized

impulses are considered for the analysis in order to neutralize the ordering effect. Figure 1.10

represents the pair wise impulse response relations between unexpected component of WPI and

SENSEX returns.


-.01

.00

.01

.02

.03

.04

.05

1 2 3 4

Accumulated Response of DLOG_SENSEX to DIFF

-.1

.0

.1

.2

.3

.4

.5

.6

1 2 3 4

Accumulated Response of DIFF to DLOG_SENSEX

Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.

Fig 1.10: Impulse Response Function

As shown in the Impulse response diagrams no significant impact of unexpected component of WPI is

found on the stock market.

The Variance decompositions offer a slightly different method for examining VAR system dynamics.

They give the proportion of the movements in the dependent variables that are due to their „own‟

shocks, versus shocks to the other variables. The results of the variance decomposition analysis for the

period of ten days are given in Table 1.12. The results indicate that 98.51 per cent of variations in the

error terms of SENSEX returns can be explained with the help of its own lagged values however 1.48

percent of its variations can be explained with the help of lagged values of unexpected WPI. Similarly

99.95 per cent of variations in the error terms of unexpected WPI can be explained with the help of its

own lagged values however 0.049 percent of its variations can be explained with the help of lagged

values of SENSEX returns.

Table1.12: Variance Decomposition

Variance Decomposition of Unexpected component of Inflation

Period

S.E.

Unexpected component

of WPI

SENSEX Returns

1 0.498012 100.0000 0.000000

2 0.498118 99.99466 0.005340

3 0.498244 99.95088 0.049120

4 0.498245 99.95069 0.049309

5 0.498245 99.95064 0.049365

6 0.498245 99.95063 0.049366

7 0.498245 99.95063 0.049366

8 0.498245 99.95063 0.049366

9 0.498245 99.95063 0.049366

10 0.498245 99.95063 0.049366

Variance Decomposition of SENSEX Returns:

Period S.E. DIFF DLOG_SENSEX

1 0.042519 1.465957 98.53404

2 0.042595 1.487420 98.51258

3 0.042607 1.487786 98.51221

4 0.042607 1.487780 98.51222

5 0.042607 1.487779 98.51222

6 0.042607 1.487779 98.51222

7 0.042607 1.487779 98.51222

8 0.042607 1.487779 98.51222

9 0.042607 1.487779 98.51222

10 0.042607 1.487779 98.51222

Cholesky Ordering: Inflation rate,_SENSEX Returns


Conclusion:

The stock valuation model asserts that stock price is equal to the sum of the discounted present value

of all future dividend payments. In other words, the stock price reflects investors‟ expectations of

future corporate earnings. Hence, stock prices commonly react to economic news The present research

study makes an attempt to study the lag-lead relationship between stock returns and inflation after

analyzing the inflation into expected and unexpected components. The high Standard Deviation value

indicates the high level of deviations in SENSEX as well as WPI series during the sample period

which indicates the volatile nature of level of inflation and market in the country. the Correlogram the

autocorrelation function (ACF) indicates that the SENSEX values and WPI values both are non

stationary in nature. In both of the cases of the Correlogram of the growth rate of WPI and SENSEX

returns the partial auto correlation function (PACF) at lag 1 indicates the stationary nature of the

seriesHence for further analysis the return series of SENSEX and growth rate of WPI is used. By using

Johansan‟scointegration test it can be concluded that the inflation rate in India and stock market are not

having long term equilibrium relationship between them. Due to the absence of any error correction

mechanism between the stock market and inflation rate, no long term or co integrated relation exists

between them. There are two components of inflation rate in India: expected and unexpected. In order

to analyse the impact of expected and unexpected component of inflation rate on stock market, the

forecasting model is developed using ARIMA forecasting method. The difference between the

forecasted values of WPI and the actual values of WPI are considered as unexpected values of

inflation.The residuals of the above mentioned ARIMA model and its Correlogram indicate that the

residuals are stationary and the model is assumed to be fit.The results of the regression model indicate

that the stock market is significantly influenced by the unexpected component of the inflation rate in

the Indian economy. However the impact is very low in magnitude. In order to analyse the lead-lag

relation between the inflation rate and stock market of India, the Block Exogeneity Wald test is

appliedThe results indicate no causal relation between the WPI rate and stock market of India. The

Impulse response diagrams no significant impact of unexpected component of WPI is found on the

stock market. The results of the variance decomposition analysis for the period of ten days have been

calculated. The results indicated that majority in the error terms of SENSEX returns and WPI values

can be explained with the help of its own lagged values however very negligible percent of its

variations can be explained with the help of lagged values of unexpected component of the opposite

variable.

Thus, the results of the study suggest that there exists no significant relation between inflation and

stock returns in the post-reform period in India. It implies that stock returns do not provide a hedge

against inflation. It can be said that investors aim at better returns and do not invest in stocks to hedge

against inflation.

Bibliography

Adam A M and Frimpong S (2010), “Can Stocks Hedge Against Inflation in the LongRun?

Evidence from Ghana Stock Market”, International Journal of Business andManagement, Vol. 5,

pp. 188-194.

Adrangi B and Chatrath A (1997), “Inflation, Output, and Stock Prices: Evidence fromBrazil”, The

Journal of Applied Business Research, Vol. 18, pp. 61-71.

Ahmad R and Mustafa K (2005), “Real Stock Returns and Inflation in Pakistan”, 21stAustralasian

Finance and Banking Conference 2008 Paper.

Al-Khazali O and Pyun C (2004), “Stock Prices and Inflation: New Evidence from thePacific-

Basin Countries”, Review of Quantitative Finance and Accounting, Vol. 22,pp. 123-140.

Balduzzi P (1995), “Stock Returns, Inflation, and the Proxy Hypothesis: A New Lookat the Data”,

Economic Letters, Vol. 48, pp. 47-53.

Barnes M, Boyd J H and Smith B D (1999), “Inflation and Asset Returns”, EuropeanEconomic

Review, Vol. 43, pp. 737-754.


Bhattacharya B and Mukharjee J (2003), “The Nature of the Causal Relationship BetweenStock

Market and Macroeconomic Aggregates in India: An Empirical Analysis in India”, Working Paper,

Jadavpur University, Kolkata

Chopen M and Zhong M (2001), “Stock Returns, Inflation and the Macro Economy:The Long Run

and Short Run Dynamics”, Advances in Investment Analysis and Portfolio Management, Vol. 8.

Davis N and Kutan A (2003), “Inflation and Output as Predictors of Stock Returns andVolatility:

International Evidence”, Applied Financial Economics, Vol. 13, pp. 693-700.

Dickey D A and Fuller W A (1976), “Distribution of the Estimations for AutoregressiveTime

Series with a Unit Root”, Journal of American Statistics Association, Vol. 74,pp. 427- 431.

Dufour J M and Tessier D (2006), “Short Run and Long Run Causality between MonetaryPolicy

Variables and Stock Prices”, Working Paper, Bank of Canada.

Durai S R S and Bhaduri S N (2009), “Stock Prices, Inflation and Output: Evidencefrom Wavelet

Analysis”, Economic Modelling, Vol. 26, pp. 1089-1092.

Engle R F and Granger C W J (1987), “Co-integration and Error Correction:Representation,

Estimation and Testing”, Econometrica, Vol. 55, pp. 251-276.

Fama E (1980), “Inflation, Output and Money”, Journal of Business, Vol. 2,pp. 201-231.

Fama E (1981), “Stock Returns, Real Activity, Inflation, and Money”, AmericanEconomic Review,

Vol. 71, pp. 545-565.

Fama E (1990), “Stock Returns, Expected Returns, and Real Activity”, the Journal ofFinance, Vol.

45, pp. 1089-1108.

Fama E and Schwert W (1997), “Asset Returns and Inflation”, Journal of FinancialEconomics,

Vol. 5, pp. 115-146.

Fisher I (1930), The Theory of Interest Macmillan, New York.

Geske R and Roll R (1983), “The Fiscal and Monetary Linkages Between Stock Returnsand

Inflation”, The Journal of Finance, Vol. 38, pp. 1-33.

Granger C W J (1969), “Investigating Causal Relations by Econometric Models and CrossSpectral

Methods”, Econometrica, Vol. 37, pp. 424-438.

Hagmann M and Lenz C (2005), “Real Assets Returns and Components of Inflation:A Structural

VAR Analysis”, SFI & NCCR Working Paper.

Jaffe F and Mandelker G (1976), “The „Fisher Effect‟ for Risky Assets: An

EmpiricalInvestigation”, Journal of Finance, Vol. 31, pp. 447-458.

Johansen S (1992), “Determination of Co-integration Rank in the Presence of a linearTrend”,

Oxford Bulletin of Economics and Statistics, Vol. 54, pp. 383-397.

Johansen S (1995), “A Statistical Analysis of Co-integration for I (2) Variables”,Econometric

Theory, Vol. 11, pp. 25-59.

Johansen S and Juselius K (1990), “Maximum Likelihood Estimation and Inference onCo-

integration: With Application to Demand for Money”, Oxford Bulletin of Economicsand Statistics,

Vol. 52, No. 2, pp. 169-210.

Johansen S and Juselius K (1992), “Testing Structural Hypotheses in a MultivariateCo-integration

Analysis of the PPP and the UIP for UK”, Journal of Econometrics,Vol. 53, pp. 211-224.

Kim Jeong-Ryeol (2003), “Stock Returns Inflation Puzzle and the Asymmetric Causalityin Stock

Returns, Inflation and Real Activity”, Economic Letters, Vol. 80, pp. 155-160.

Kim S and Francis I (2005), “The Relationship Between Stock Returns and Inflation:New

Evidence from Wavelet Analysis”, Journal of Empirical Finance, Vol. 12,pp. 435-444.

Lee KeunYeong (2008), “Causal Relationship Between Stock Returns and Inflation”,Applied

Economic Letters, Vol. 15, pp. 125-129.

Linter J (1975), “Inflation and Security Returns”, Journal of Finance, Vol. 30,pp. 259-280.

Modigliani F and Cohn R (1979), “Inflation, Rational Valuation, and the Market”,Financial

Analysts Journal, Vol. 35, pp. 24-44.


Nelson C (1976), “Inflation and Rate of Returns on Common Stocks”, Journal of Finance,Vol. 31,

pp. 471-483.

Samarakoon P L (1996), “Stock Market Returns and Inflation: Sri Lankan Evidence”,Sri Lankan

Journal of Management, Vol. 1, pp. 292-311.

Sari R and Soytas U (2005), “Inflation, Stock Returns, and Real Activity in Turkey”,The Empirical

Letters, Vol. 4, pp. 181-192.

Shanmugam K R and Misra S B (2008), “Stock Returns Inflation Relations in India”,Working

Paper, Madras School of Economics, Chennai.

Sharpe S (2002), “Re-Examining Stock Valuation and Inflation: The Implications ofAnalysts‟

Earnings Forecasts”, The Review of Economics and Statistics, Vol. 84,pp. 632-648.

Spyrou S I (2001),”Stock Returns and Inflation: Evidence from an Emerging Market”,Applied

Economics Letters, Vol. 8, pp. 447-450.

Spyrou S I (2004), “Are Stocks a Good Hedge Against Inflation?”Applied EconomicsLetters, Vol.

36, pp. 41-48.

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