proceedings of third international conference on

“Redefining Business Practices to Create
Empathetic Social Change”
11 September 2016
Edited By
2017
427, Palhar Nagar, RAPTC, VIP-Road, Indore-452005 (MP) INDIA
Phone: +91-731-2616100, Mobile: +91-80570-83382
E-mail: [email protected] , Website: www.isca.co.in
Practices to Create Empathetic Social Change"
Editor(s): Dr Ira Bapna and Dr Mandip Gill
Edition: First
Volume: I
© Copyright Reserved
2017
All rights reserved. No part of this publication may be reproduced, stored, in a retrieval
system or transmitted, in any form or by any means, electronic, mechanical,
photocopying, reordering or otherwise, without the prior permission of the publisher.
ISBN: 978-81-934005-9-3
iii
Indices
Bapna
1
from India: An Empirical Study
Ranjana Patel, Neha Godbole,
Manisha Singhai and Satnam
and Sri Lanka
Ranjana Patel, Dr. Manisha
15
Entrepreneurship
Rajeev Jhalani
Rajeev Jhalani
Organisational Transformation in the
Decisions of Individual Investors
Agnus Anthony Meledath 33
National Stock Exchange
Dr.Atul Dubey 38
An Analysis of Factors Influencing
Consumer Attitude Towards Green
Kamran Sultan
Environment
Kumrawat
51
In Logistics Companies
iv
Gengele
Institution in India
National and Store Brands in Retail
Dr. Dhanashree Nagar, Dr.
Kshama Ganjiwale and Ranu
cause not for applause
A Demographic study of Work Life
Balance of IT Employees with Special
Reference Indore region
Dr. Satnam Ubeja
plant - a qualitative review
Kavita Tiwari
Career Couple IT Professionals in
Indore Region
100
Comparative Study between Indore and
Ujjain City
Sharma
105
Madhya Pradesh
Madhya Pradesh
Totala
121
Usages of Mobile Phone Service in
Indore City
Service Sector
132
Impact of Customers’ Gender on their Dr. Krishna Narayan Mishra 142
Proceedings of third international conference on Redefining Business Practices to Create Empathetic Social Change ISBN: 978-81-934005-9-3
v
Developing Brands through Sensory
Marketing: A Contemporary Approach
Ira Bapna
Availing Car Loans from Banks: With
Special Reference to Indore
Dr. Sukhjeet Kaur Matharu,
163
Market Segmentation & Consumer
Singh
173
overview
Determinants of Corporate Leverage in
FMCG Companies of Nifty Index
Dr. Ira Bapna, CA (Dr.)
Sachchidanand Pachori, Dr.
Indore Region
Manisha Singhai, Dr. Ranjana
Patel, Ms. Neha Godbole
1
An Empirical Analysis of Volatility and Leverage: A Study of Various BSE Indices
Dr. Vishal Sood 1*
and Dr. Ira Bapna 2
1MK Ponda College of Business and Management, Bhopal, M.P., India 2Maharaja Ranjit Singh College of Professional Sciences, Indore, M.P., India
[email protected], [email protected]
This study investigates the interrelationship among indices prices, volatility and leverage effect empirically tested
using returns of time series data that consists of daily closing prices of BSE SENSEX, BSE100, BSE200 and BSE
500 for fifteen years period from 4th January 2000 to 31st December 2015. The study identifies and estimates the
mean and variance components of the returns using descriptive statistics and explaining the conditional volatility
structure of the residuals obtained under the best suited mean models for the said series using ARCH lm test. The
analysis has been carried out using both symmetric and asymmetric models of Generalized Autoregressive
Conditional Heteroscedastic (GARCH). As per Akaike Information Criterion (AIC) and Schwarz Information
Criterion (SIC), the study proves that GARCH (1, 1) and TGARCH (1, 1) estimations are found to be most
appropriate model to capture the symmetric and asymmetric volatility prevailing in the studied returns. The study
also provides evidence for the existence of a positive and insignificant risk premium to create hedging as per
GARCH- M (1, 1) model. The asymmetric effect of leverage shows negative shocks have long and significant effect
on conditional variance i.e. volatility using EGARCH (1, 1) and TGARCH (1, 1) models.
Keywords: Conditional Volatility, GARCH Models, Leverage Effect, Akaike Information Criterion, Schwarz
Information Criterion.
Forecasting procedures are widely used in financial markets to evaluate companies and their stocks on continual basis so as
to ascertain returns. Time series modelling plays an important and significant role in describing the structure of economic
variables, investable and financial assets. The towering stocks prices around the globe have taken values of index and stocks
to new peaks scaring economists all around and creating dilemma that these prices can burst in near future (Bandivadekar and
Ghosh, 2003). In finance, “bubble” is an especially warning yet fascinating to investors in terms for pricing. A bubble leads
to over pricing of stocks taking them to undesirable heights and creating speculative expectations of the players in the market.
It creates a cycle of growth and overwhelming growth leading to mass investment fantasy, and ending in disaster of fall in
prices when the bubble bursts. There have been numerous incidents of this in past, which proves the bursting of this
speculative bubble across the globe (Raju and Karmande, 2003).
“Volatility" in stock market terminology leads to sleepless nights to a lot of investors as well as market regulators by
disturbing investments proportions and returns. Volatility of an asset is measure of variability in the price over time measured
as the variance or the standard deviation of the returns on the asset or investable during the course of holding. The more the
standard deviation of the asset more volatile the asset is bound to have in terms of future pricing. This is also a measure of the
riskiness of the asset indicating more ups and downs it has contributed to unpredictability in its returns. Since volatility is a
standard measure of financial vulnerability in pricing of assets, it plays a key role in assessing the risk/return tradeoffs
(Sharma, 1983; Stein. 1987and Gupta and Basu, 2007).
Policy makers depend upon market estimates of volatility as a barometer to evaluate the vulnerability of the financial
markets. The existences of extreme volatility or “noise” also undermine the usefulness of stock prices as a signal to buy or
sell depending on true intrinsic value of firm stock prices. These kinds of time series data can have trends that can be best
modelled by using nonlinear processes. The important types of nonlinear time series includes tests namely, bilinear, threshold
autoregressive, exponential autoregressive, autoregressive conditional heteroscedastic (ARCH), generalized autoregressive
heteroscedastic (GARCH) and stochastic and random coefficient models (Goyal, 1995; Roy and Karmerkar, 1995and
Vijayalaxmi and Gaur, 2013).
Volatility: Volatility clustering is defined as the observation that results in large changes in prices/ returns in stocks. These
prices tend to follow large changes, up or down, and small changes tend to be followed by small changes in prices/ returns.
These fluctuations/ revision might be caused by the continuous effect of the external shocks transmitted by economies across
the globe (Mandelbrot, 1963). The ARCH (Engle, 1982) and GARCH (Bollerslev, 1986) models describe the phenomenon of
volatility clustering to be more accurate and precise measure of risk. ARCH model explained the regularity of the return in
2
the price change observed in the time series. GARCH model explained the heteroscedasticity of the Return Sequence
residuals in studied variables.Leverage Effect: It was discovered by observing returns that the current return and future
volatility have negative correlation among themselves. This indicates that bad news in the market will cause violent
fluctuations as compare to good ones and hence termed as Leverage Effects (Black, 1976). In other words it may be said that,
positive and negative information in the market lead to different level of effect to volatility in stock returns during the course
of holding. EGARCH model analyzes the effect on stock volatility caused by asymmetric conditional heteroskedasticity on
absorbing different information in the market (Nelson, 1991). The use of GJR-GARCH model adds seasonal terms to
distinguish the positive and negative shocks prevailing in the market and have competency to change or revise prices by time
(Glosten, Jagannathan and Runkle, 1993).
This paper attempts to study the uncertainty of the financial assets relating to the daily closing share prices of S&P CNX
SENSEX, BSE100, BSE200 and BSE500 in Indian stock market. The study attempts to calculate volatility and leverage
effect in the various BSE indices. The studied variables estimate mean models and gave the residuals which were white noise
but having the ARCH effect among them in terms of return. The ARCH effect or such influence is evidently persistent for
long time in future. The empirical analysis also tried to capture this effect through different GARCH type models as high
variability and high volatility has been seen in daily indices returns. The GARCH type models are the better models in
describing return series having the property of changing variance level. It has been tested statistically and empirically (Mittal
et. al., 2012). A comparison of Volatility clustering is checked using GARCH (1, 1) and GARCH- M (1, 1) models. The
Leverage in pricing is checked using TGARCH and GJR GARCH model. The efficiency of the volatility and leverage model
is checked on the basis of Akaike Info Criterion (AIC) and Schwarz Info Criterion (SIC).
Review of literature
When volatility smiles the markets soars and when markets roar the volatility fades away. It is well said that volatility hold a
key in deciding investor’s destiny and various studies empirical analysis were carried in measuring it. The study examined
the time variation in volatility in the Indian stock market during 1979-2003.The study identified sudden shifts in the stock
price volatility and nature of events that cause these shifts in volatility. The study revealed that the period around the BOP
crisis and subsequent initiation of the economic reforms in India were the most volatile period in the stock market, it was also
concluded that FII entry in particular does not have any direct implication in the stock return volatility. Level of volatility
does not showed much change pre and post liberalization (Batra, 2004). The study used GARCH class models ranging from
Simple-GARCH (1, 1) to relatively complex GARCH models like EGARCH and TGARCH for modeling the volatility and
forecasting the conditional variance of BSE SENSEX-30 demonstrating negative news has long term volatility than good
news in the market (Srinivasan and Ibrahim, 2010).
Another study used GARCH- class models to two major Stock Exchanges of Indian Stock Market to analyze their
characteristics of volatility and found significant ARCH effects. The study also demonstrated the existence of leverage and
asymmetric effect in Indian Stock Market (Srivastava, 2008). The empirical analysis investigated the Heteroskedastic
behavior of Indian Stock Market by using different GARCH models. The study investigated the asymmetric volatility in
Indian Stock Market by employing EGARCH and concluded that, volatility is an asymmetric function of past innovation
raising proportionately more during market decline and was evidenced that return is not significantly related to risk
(Karmarkar, 2007). The investigation used number of forecasting models like Random Walk, Linear Regression, Moving
Average, Autoregressive models on NSE daily returns to evaluate the forecasting performance of the same the Root Mean
Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) for testing the return characteristics found that the
success or failure of a particular type of forecasting model applied to one type of market carries over to different market are
affected by the quality of volatility forecasts and the markets shows high volatility on the basis of information (Srinivasan et.
al., 2010).
It was identified using GARCH models that the Indian Stock Market Volatility accounts for asymmetry the study revealed
the presence of leverage effect in the stock market and showed the smaller shocks that affect the returns in Indian Stock
Market due to news impact (Krishnan and Mukherjee, 2010). Another study investigated the asymmetric nature of U.S.Stock
Market return and effect of heteroskedasticity on stock return volatility. The research also analysed the relationship between
stock return, conditional volatility and standard residuals. GARCH (1, 1) and TGARCH (1, 1) to test the heteroskedasticity
and asymmetric nature of stock market returns respectively and concluded the presence of non lineariy, heteroskedastic effect
and asymmetric nature of stock returns (Kumar and Dhankar, 2011).
The study used time varying variance based GARCH process to capture change in volatility and study its impact on Indian
Securities Market that compared the change in volatility of Indian Stock Market with U.S.Stock Market (Rastogi and
Srivastava, 2011). It was investigated that, the volatility of Athens Stock excess returns over the period 1990-1999 through
3
the comparison of various conditional Heteroskedasticity models. The empirical results indicated that there was significant
evidence for asymmetry in stock returns which is captured by a quadratic GARCH specification model (Apergis and
Eleptherine, 2001). The outcome used multivariate VAR-EGARCH model to examine the return and volatility dynamics
between their traded adjusted equity returns from Ghana, Kenya, Nigeria and South Africa. The findings suggested reciprocal
return spill over between Ghan and Kenya and between Nigeria and South Africa (Kuttu, 2014).
The research investigated the stock market volatility in emerging stock markets of India and China using daily closing price
and concluded the presence of non-linearity through BDSL test while conditional heteroskedasticity was identified through
ARCH-LM test. The findings revealed that the GARCH (1, 1) MODEL successfully captures the non linearity and volatility
clustering (Joshi, 2010). The research estimated the volatility of BSE-500 stock index and its related stylized facts over 10
periods using ARCH models The study concluded that GARCH(1, 1) MODEL explains the volatility of Indian Stock Market
and its stylized facts including volatility clustering, fat tail and mean reverting satisfactorily (Goudarzi and Ramanarayan,
2010). The researchers examined the integration behavior and volatility spillover transmission across the stock markets of Sri
Lanka, India and Pakistan after liberalization policies initiated in the early 1990’s examined the ways in which two issues
could relate to movement of stock prices and then investigated the impact of this on the corresponding stock markets using
correlation analysis, a multivariate Co-Integration Test and Generalised Impulse Response (GIR) functions based on one
factor model (Gunasinghe, 2005).
An empirical study analyzed the Chinese Stock Market behavior by choosing the data from Shanghai Composite Index and
Shenzen Stock Index and used ARIMAEARCH- M (1, 1) and ARIMA-TARCH (1,1) model to analyze the volatility of
financial time series with the characteristics of clustering, asymmetry, and peak and fat tails (Xing, 2011). The study
examined the ability of rational economic factors to explain stock market volatility and proposed a simple model of the
economy under uncertainty, which identified four determinants of stock market volatility viz. uncertainty about price level,
the riskless rate of interest, the risk premium on the equity and the ratio of expected profits to expected revenues. Their
results were useful in explaining the past behavior of stock market volatility and in forecasting future volatility (Binder and
Merges, 2001).
Objectives: To study Volatility and Leverage effect caused on various BSE Indices.
Research Methodology
Hypothesis
H01: The returns of S&P BSE SENSEX, BSE100, BSE200 and BSE500 are not normally distributed.
H02: The returns of S&P BSE SENSEX, BSE100, BSE200 and BSE500 are non-stationary.
H03: The returns of S&P BSE SENSEX, BSE100, BSE200 and BSE500 are non- heteroscadestic.
H04: There is no volatility caused in the returns of S&P BSE SENSEX, BSE100, BSE200 and BSE500.
H05: There is no leverage effect caused in the returns of S&P BSE SENSEX, BSE100, BSE200 and BSE500.
H06: There is no ARCH effect in the returns of S&P BSE SENSEX, BSE100, BSE200 and BSE500.
The Sample: The daily stocks values of S&P BSE SENSEX (SESNSX), BSE100, BSE200 and BSE500 have been taken
from the period 4th January 2000 to 31st December 2015. There are 3988 observations of the daily closing prices.
The Tools: Descriptive Analysis, Unit Root Test, Test of Hetroscedasticity, ARCH family test i.e. GARCH (1, 1), GARCH-
M (1, 1), EGARCH (1, 1), and TGARCH (1, 1), models are used in the study. The tools are applied using E- views 7
statistical software. The time series data is Heteroscedastic and by applying the tool that is: Returns= ln (Pt- Pt-1), we
convert data into homoscedastic data.
Results and analysis
Table-1: Descriptive statistics.
Mean 0.000401 0.000419 0.000429 0.000396
Median 0.001305 0.001424 0.001606 0.001007
4
Std. Dev. 0.015898 0.015648 0.015442 0.015490
Skewness -0.37856 -0.47865 -0.54803 -0.19733
Kurtosis 9.619600 9.988894 9.923355 10.01336
Jarque-Bera 7374.678 8266.555 8162.415 8197.100
Probability 0.000000 0.000000 0.000000 0.000000
Sum 1.599010 1.671206 1.711710 1.580828
Sum Sq. Dev. 1.007410 0.976068 0.950499 0.956390
Observations 3987 3987 3987 3987
Table-2 above shows the presence of unit root in the series tested using Augmented Dickey Fuller Test (ADF) and the
presence of heteroscedasticity tested using ARCH lm test. The p values of ADF for S&P BSE SENSEX, BSE100, BSE200
and BSE500 are 0.0001< 0.05, which lead to conclude that the data of the time series for the entire study period is stationary.
The ADF test statistics reported in table 2 reject the hypothesis at 5% level with the critical value of -58.767, -57.1459, -
56.6154 and -55.9358 for ADF tests of a Unit Root in the return series. Hence, the hypothesis suggesting that the data is non
stationary is rejected and the data is stationary.
To make the series stationary, the closing price of the S&P BSE SENSEX, BSE100, BSE200 and BSE500 is converted into
daily logarithmic return series. Figure 1, 2, 3 and 4 shows volatility clustering of return series of the S&P BSE SENSEX,
BSE100, BSE200 and BSE500 for the study period from 1st January 2003 to 31st December 2012. From the figures it is
inferred that the period of low volatility tends to be followed by period of low volatility for a prolonged period indicates that,
the volatility is clustering and the return series vary around the constant mean but the variance is changing with time at a
greater pace. The ARCH- lm test is applied to find out the presence of ARCH effect in the residuals of the return series. From
the table 2, it is confirmed that the ARCH- lm test statistics is highly significant. Since p values for 0.8130 S&P BSE
SENSEX, 0.713 BSE100, 0.986 BSE200 and 0.891BSE500 are > 0.05, the null hypothesis of ‘no arch effect’ is rejected at
5% level, confirming the presence of ARCH effects in the residuals of time series models in the returns and hence the results
warrant for the estimation of GARCH family models.
Table-3 and 4: Estimated Result of GARCH (1,1) and GARCH- M (1,1) Model.
Table-3: GARCH (1,1) Model.
Mean Equation
λ (Risk Premium) - - - -
α (ARCH Effect) 0.1125 0.1243 0.1298 0.13305
β (GARCH Effect) 0.8705 0.8587 0.8537 0.8504
α+ β 0.983 0.983 0.9835 0.98345
Log Likelihood 1163.84 11576.42 11633.11 11679.81
Akaike Info Criterion (AIC) -5.834 -5.8041 -5.8235 -5.8559
Schwarz Info. Criterion (SIC) -5.818 -5.7888 -5.8242 -5.840629
Heteroskedasticity Test
5
Note: (0)* indicates the p- value in the model.
After volatility clustering is confirmed with return series; ADF test suggest the data is stationary; and heteroscedasticity
effect using ARCH-lm test focuses on determining the best fitted GARCH (1, 1) model to the return series. Therefore,
GARCH model is used for modeling the volatility of return series in the Indian stock market. The result of GARCH (1, 1)
and GARCH-M (1, 1) models in table 3 reveals the parameter of GARCH is statistically significant. In other words, the
coefficients viz., constant (ω), ARCH term (α), GARCH term (β) is highly significant at 5% level as p- value is less than
0.05. In the conditional variance equation, the estimated β values are 0.8705, 0.8587, 0.8537 and 0.8504 that is considerab ly
greater than α value 0.1125, 0.1243, 0.1298 and 0.13305 indicating that, the market has a memory longer than one period and
that volatility is more sensitive to its lagged values than it is to new surprises in the market values. It shows that, the volatility
is persistent and caries for a long period of time in future. The sizes of the parameters α and β determine the volatility in time
series. The sum of these coefficients (α and β) are 0.983, 0.983, 0.9835 and 0.98345 closer to unity indicating that the shock
will persist too many future periods. Since the risk-return parameter is positive and significant at 5% level, it shows that,
there is a positive relationship between risk and return. Further, ARCH-lm test is employed to check ARCH effect in
residuals and from the results, have p values 0.805, 0.4593, 0.3529 and 0.3813 > 0.05, stating that the null hypothesis of ‘no
arch effect’ is accepted. In other words, the test statistics do not support for any additional ARCH effect remaining in the
residuals of the models, which implies that the variance equation is well specified for the market.
Table-4: GARCH- M (1, 1) Model.
GARCH - M(1,1) Model
Mean Equation
λ (Risk Premium) 0.041737 0.0335 0.0282 0.0206
Variance Equation
ω (Constant) 4.26E (0)* 4.74E (0)* 4.75E (0)* 4.76E (0)*
α (ARCH Effect) 0.1127 (0)* 0.1244 (0)* 0.1299 (0)* 0.1331 (0)*
β (GARCH Effect) 0.8703 (0)* 0.8585 (0)* 0.8536 (0)* 0.8504 (0)*
α+ β 0.983 0.9829 0.9835 0.9835
Log Likelihood 111636.18 11576.66 11633.28 11679.9
The GARCH- M (1, 1) model is estimated by allowing the mean equation of the return series to depend on a function of the
conditional variance. The constant in mean equation is significant at 5% level, indicating that there is an abnormal return for
the market. From the table 4, it is inferred that the coefficient of conditional variance (λ) in the mean equation value is
positive 0.041737, 0.0335, 0.0282 and 0.0206 respectively. However, it is statistically insignificant, which implies that there
is no significant impact of volatility on the expected return, indicating lack of risk-return trade off over time. In the variance
equation of GARCH- M (1, 1), the parameters viz., constant (ω), ARCH term (α), GARCH term (β) are highly significant at
5% level as p values are less than 0.05. The sum of α and β are 0.983, 0.9829, 0.9835 and 0.9835, suggesting that, shocks will
persist in the future period for long time. However, the ARCH-lm test is applied on residuals shows that, the test statistics do
not exhibit additional ARCH effect for the entire study period indicating that the variance equation is well specified.
Table-5 and 6: Estimated Result of EGARCH and TGARCH Model
Table-5: EGARCH Model
Mean Equation
6
α (ARCH Effect) 0.217736(0)* 0.23804(0)* 0.246729(0)* 0.251036(0)*
β (GARCH Effect) 0.969436(0)* 0.967995(0)* 0.965746(0)* 0.964891(0)*
γ (Leverage Effect) -0.083913(0)* -0.07647(0)* -0.07724(0)* -0.076198(0)*
α+ β 1.187172 1.206035 1.212475 1.215927
Log Likelihood 11663.62 11598.1 11653.65 11700.55
ARCH- LM Test 0.2583 0.00602 0.0935 0.0827
Prob. Chi. Square 0.6113 0.9382 0.7597 0.7735
Note: (0)* indicates the p- value in the model. In order to capture the asymmetries in the return series, two models have been used viz., EGARCH- M (1, 1) and TGARCH
(1, 1). γ captures the asymmetric effect in both EGARCH- M (1, 1) and TGARCH (1, 1) models. The asymmetrical
EGARCH (1, 1) model is used to estimate the returns of the S&P BSE SENSEX, BSE100, BSE200 and BSE500 and the
result is presented in table 5. The results reveals that ARCH (α) and GARCH coefficient (β) are greater than one i.e.
1.187172, 1.206035, 1.212475 and 1.215927, reporting that conditional variance is explosive. The estimated coefficients are
statistically significant at 5% level as p value is less than 0.05. γ indicating the leverage coefficient, is negative -0.083913, -
0.07647, -0.07724 and -0.076198 is statistically significant at 5% level. This explains that there is a leverage effect in return
during the study period. The analysis reveals that there is a negative correlation between past return and future return
(leverage effect). Hence, EGARCH (1, 1) model supports the presence of leverage effect on the S&P BSE SENSEX,
BSE100, BSE200 and BSE500 return series. Finally, the ARCH-lm test statistics reveals that the null hypothesis of no
heteroscedasticity in the residuals is accepted as p values are 0.6113, 0.9382, 0.7597 and 0.7735 > 0.05.
Table- 6: TGARCH Model
Mean Equation
Variance Equation
ω (Constant) 4.97E-06 (0)* 5.31E-06 (0)* 5.39E-06 (0)* 5.43E-06 (0)*
α (ARCH Effect) 0.048173 (0)* 0.065072 (0)* 0.071601 (0)* 0.07582 (0)*
β (GARCH Effect) 0.87008 (0)* 0.860147 (0)* 0.854674 (0)* 0.850918 (0)*
γ (Leverage Effect) 0.116214 (0)* 0.101711 (0)* 0.098552 (0)* 0.096294 (0)*
α+ β 0.918253 0.925219 0.926275 0.926738
Log Likelihood 11668.06 11600.81 11654.71 11700.02
An alternate model to test for asymmetric volatility in the Nifty return in table 6 is estimated by result of TGARCH (1, 1)
model. In it, the coefficient of leverage effect (γ) is positive and significant at 5% level as the p values are less than 0.05. The
study implies that negative shocks or bad news have a greater effect on the conditional variance than the positive shocks or
good news because γ values are 0.116214, 0.101711, 0.098552 and 0.096294 is statistically significant at 5% level. The
diagnostic test is performed to test whether the residuals are normally distributed. The arch-lm test statistic for TGARCH (1,
1) model does not show any additional ARCH effect present in the residuals of the model, which implies that the variance
equation is well specified for the Indian stock market.
Interpretation
7
Based on the empirical analysis, the following interpretations can be drawn, positive returns of the mean indicates that there
is a gradual increase in the returns of S&P BSE SENSEX, BSE100, BSE200 and BSE500 by time ranging from 3rd Jan 2000
to 31st Dec 2015. As the data is negatively skewed there is a very high probability of earning returns for investors for a long
period of time. The smaller standard deviation reveals that the prices are tightly bunched around the mean. The lower
standard deviation denotes lesser dispersion and lesser risk of investment. The volatility in the figures show that it prevailed
for a very long period and investors can be benefitted if they are long on S&P BSE SENSEX, BSE100, BSE200 and BSE500.
For arbitragers it is good to observe frequent corrections in the prices and the high volatility can help them frame decision to
long or short the studied indexes. Larger movement in prices creating a high price range result in higher volatility. Whereas it
was observed that, in the present study the peaks and corrections are not too high resulting in low volatility.
The data sets studied are stationary and there is no serial correlation observed. There is an ARCH affect and the prices may
show volatility which can be studied using ARCH family test. There is a strong relation with volatility and market
performance. Volatility tend to decline when stock market rises and increases when market fall. In GARCH (1, 1) model, the
sum of the coefficient (α + β) is 0.98, which implies that the volatility is highly persistent. These remains for a very long
period and the returns have lesser percentage change and low correction in prices but the effect is for a long period of time.
In GARCH- M (1, 1) model, the coefficient of conditional variance or risk premium (λ) in the mean equation is positive
however, insignificant, which implies that higher market risk provided by conditional variance will not necessarily lead to
higher returns. Hence investors should have a close watch on volatility to identify bottom and top in terms of price innovation
in the market. Hence it can be said that volatility of the market does not stay substantially below mean for a long period of
time. As volatility increases the market performance will decrease.
The asymmetric effect captured by the parameter (γ) in EGARCH model which is negative and statistically significant at 5%
level providing the presence of leverage effect, which reveals that positive shocks have less effect on the conditional variance
when compared to the negative shocks.
The asymmetric effect captured by TGARCH model infers that the coefficient of leverage effect (γ) is positive and
significant at 5% level, providing the presence of leverage effect during the study period. Hence the market is more reluctant
to change while absorbing the negative news like inflation, interest rates, currency pricing, unemployment etc. innovate price
quickly for long period of time. Leverage effect is a ratio of debt and equity greater risk or volatility or variance of firm when
leverage effect is higher. Hence when negative news hit the market investors park there funds in less riskier asset and the
shift is observed in the market.
The best fitted models both in symmetric as well as in asymmetric effect are selected based on the minimum AIC and SIC
value and the highest log likelihood value. Likewise, the AIC, SIC value between GARCH and GARCH- M model is low in
GARCH (1, 1) model log likelihood value is high in GARCH (1, 1) model. When compared to its alternate symmetric model,
called GARCH- M (1, 1).Hence GARCH (1, 1) model is found to be the best fitted model. On comparing EGARCH and
TGARCH it was concluded that, the AIC, SIC values are low in TGARCH and log likelihood value is also higher than
EGARCH. Hence it conforms the norms and TGARCH (1, 1) model is apparently seems to be an adequate description of
asymmetric volatility process.
Conclusion
In this study, volatility of various BSE Indices returns was tested using the symmetric and asymmetric GARCH models. The
study confirms the unit root rest, volatility clustering and ARCH effect. Higher the volatility that comes with bear market has
direct impact on portfolios. The study prove that, GARCH (1, 1) model has been found to be the best fitted model among all
to capture the symmetric effect as per AIC, SIC criterion and likelihood basis. Further, TGARCH (1,1) model is found to be
the best fitted model to capture the asymmetric volatility based on the highest log likelihood ratios and minimum AIC and
SIC criterion. The overall conclusion of the study supports the findings of previous research studies carried by Zakaria and
Winker (2012) and Zivanayi and Chinzara (2012); and more particularly the study differs in the way of selecting the
appropriate model using diagnostic test. Nevertheless, the results presented in the study (in the said tables) are in contrary to
the research findings of Karmakar (2007) where the risk premium is significant. On a whole, the study concludes that
increased risk did not increase the returns since the coefficient is insignificant for the selected variables for the study period.
Suggestions
It is suggested that volatility add to the level of concern and worry on the part of investor as they watch the value of their
portfolios move more violently. There is decrease in value of asset as the volatility increases as it denotes losses. This causes
8
irritation in the responses which can increase investor’s losses in an asset during the time of its holding. As an investor’s
portfolio of stocks decline it will likely to cause them to rebalance the weight between stocks and bonds by buying more
stocks as prices fall in order to create an ideal combination of debt and equity. Investors can also use volatility as a tool to
decide to buy stocks at lower prices than they might have otherwise purchases. Leverage effect can help investors to create a
combination of debt and equity where by position can be revised depending on the performance of risky and risk free assets.
Finally it can be beneficial to calculate risk premium so as to maintain a balance between market return and risk free rate of
return.
References
1. Apergis, N. and Eleptheriou, S. (2001). Stock Market Volatility: Evidence from Athens Stock Market Index. Journal of
Economics and Finance, 25(1), 50- 61.
2. Bandivadekar, S and Ghosh S. (2003). Derivatives and Volatility on Indian Stock Markets. RBI Occasional Papers, 24,
(3).
3. Batra, A. (2004). Stock Return Volatility Persistence in India: 1973- 2003. Working Paper ICRIER, New Delhi, India.
Sited at www.ssrn.com on 20 th
March 2016.
4. Binder,J and Merges, M. (2001). Stock Market Volatility and Economic Factors. Review of Quantitative Finance and
Accounting, 17(1) 5-26.
5. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-27.
6. Black, F. (1976). Studies of Stock Market Volatility changes. Proceeding of the American Statistical Association,
Business and Economic Statistics Section. Sited at www.ssrn.com on 20 th
March 2016.
Inflation. Econometrica, 50, 987-1007.
8. Glosten, L., Jaganathan, R. and Runkle, D. (1993). On the Relation between the Expected Value and the Volatility of the
Nominal Excess Returns on Stocks. Journal of Finance 48, 1779-1801.
9. Goudarzi, H. and Ramanarayan, C. (2010). Modeling and Estimation of Volatility in the Indian Stock Market.
International Journalof Business and Management, 5(2).
10. Goyal, R (1995). Volatility in Stock Market Returns. Reserve Bank of India Occasional Papers, 16(3), 175-195.
11. Gunasinghe, W, (2005). Behavior of Stock Markets in South Asia: An Econometric Investigation. South Asia Economic
Journal, 6, 165-184.
12. Gupta, R. and Basu, P. (2007). Weak Form Efficiency in Indian Stock Markets. IBER Journal, 6, (3), 57-64.
13. Jingle, X. (2011). The Research on Stock Market Volatility in China Based on the Model of ARIMA-EGARCH-M (1,1)
and ARIMA-TARCH-M(1,1). Education and Management Communication in Computer and Information Science,
(210), 521-527.
14. Joshi, P. (2010). Modeling Volatility in Emerging Stock Markets of India and China. Journal of Quantitative Economics,
8(1), 59-64.
15. Karmarkar M. (2007). Asymmetric Volatility and Risk Return Relationship in the Indian Stock Market. South Asian
Economic Journal, 8(1), 99-116.
16. Krishanan, R. and Mukherjee, C. (2010). Volatility in Indian Stock Market. Journal of Emerging Market Finance, 9 (1),
71-93.
17. Kumar, R. and Dhankar, R. (2011). Non Linearity and Heteroskedasticity Effects on Stock Market Volatility: A Case of
U.S. Stock Market. Global Business Review, 12 (2), 319-329.
18. Kuttu, S. (2014). Return and Volatility Dynamics among Four African Equity Markets: A Multivariate VAR-EGARCH
Analysis. Global Finance Journal, 25, 59-64.
19. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. Journal of Business 36, 394-419.
20. Mittal, A.; Arora, D. and Goyal. N. (2012). Modeling the Volatility of Indian Stock Market. Gitam Journal of
Management 10 (1), 224–43.
21. Nelson, D. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59, 347-370.
22. Raju, M. and Karande, K. (2003). Price Discovery and Volatility on NSE Futures Market. SEBI Bulletin, 1(3), 5-15.
23. Rastogi,S. and Srivastava, V. (2011). A Comparitive Study of Conditional Volatility of Indian and U.S. Stock Markets
using GARCH (1, 1) Models. Asia Pacific Journal of Management Research and Innovation,7 (1), 92-101.
24. Roy, M. and Karmakar, M. (1995). Stock Market Volatility: Roots and Results. Vikalpa, 20(1), 37-48.
25. Sharma, J. (1983). Efficient Capital Markets and Random Character of Stock Prices Behavior in a Developing Economy.
Indian Journal of Economics, 63, (251), 395.
26. Srinivasan, P. and Ibrahim, P. (2010). Forecasting Stock Market Volatility of BSE-30 Index Using GARCH Models.
Asia Pacific Journal of Management Research and Innovation, 6, (1), 47-60.
9
27. Srivastava, A. (2008). Volatility of Indian Stock Market: An Empirical Evidence. Asia Pacific Journal of Management
Research and Innovation, 4 (4), 53-61.
28. Srinivasan, K.; Deo, M. and Murugesan, B. (2010). Testin Alternative Models for Forecasting Volatility in Stock
Markets. Asia Pacific Journal of Management Research and Innovation, 6 (4), 95-104.
29. Stein, J. (1987). Information Externalities and Welfare Reducing Speculation. Journal of Political Economy, 95, 1123-
1145.
30. Vijayalakshmi, S., and Gaur, S. (2013). Modeling Volatility: Indian Stock and Foreign Exchange Markets. Journal of
Emerging Issues in Economics, Finance and Banking 2 (1), 583–98.
10
Export Potential of Hides and Leather From India: An Empirical Study
Ranjana Patel, Neha Godbole, Manisha Singhai and Satnam Ubeja Prestige Institute of Management and Research (M.P), India
[email protected]
Abstract
The leather industry has undergone a phase of transformation from a mere exporter of raw materials in the sixties to that of
value added finished products in the nineties. Policy initiatives taken by the Government of India since 1973 have been
instrumental to such a transformation. At the time of globalization, Indian economy supported with liberalized economic and
trade policies since 1991, the industry is poised for further growth to achieve greater share in the global trade. India is the
world’s second largest producer of footwear; its production estimated is over 700 million pairs per annum. Footwear
accounts for 18 percent share of total leather exports. Increase in the forex impact and global competition implies
opportunity that Footwear companies cannot sustain their growth from only exports front. It has to cater to the domestic
market with a blend of traditional, western fashion which will bring in huge market in the footwear segment in India.
Keywords: Export potential, leather industry, trend of exports.
Introduction
The leather industry occupies a place of prestige in the Indian economy because of its massive potential for employment,
growth and exports. There has been an increasing emphasis on its planned development, aimed at optimum utilization of
available raw materials for maximizing the returns, particularly from exports. The exports of leather and leather products
gained momentum during last twenty years. There has been a phenomenal growth in exports from Rs.320 million in the year
1965-66 to Rs.69558 million in 1996-97. Indian leather industry today has attained well merited recognition in international
markets besides occupying a prominent place among the top seven foreign exchange earners of the country. Apart from a
significant foreign exchange earner, leather industry has tremendous potential for employment generation. Direct and indirect
employment of the industry is around two million. The skilled and semi-skilled workers constitute nearly 50% of the total
work force.
Structure of the industry: The leather industry is spread in different segments namely, training & finishing, footwear & its
component, leather garments, leather goods including saddle, harness etc. The estimated production capacity in these
segments is 64 million pieces Hides and 166 million pieces skins.,The major production centers for leather and leather
products are located at Chennai, Ambur, Ranipet, Vaniyambadi, Trichi, Dindigul in Tamil Nadu, Calcutta in West Bengal,
Kanpur in Uttar Pradesh, Jalandhar in Punjab, Bangalore in Karnataka, Delhi and Hyderabad in Andhra Pradesh.
Raw materials supplies: There exists a huge raw material base. This is on account of population of 194 million cattle, 70
million buffaloes, 95 million goats. According to the latest census, India ranks first among the major livestock holding
countries in the world. In respect of sheep, we claim the sixth position. These species provide the basic raw material to the
leather industry.
The annual availability of 166 million pieces of hides and skins is the main strength of the industry. Some of the
goat/calf/sheep skins available in India are regarded as specialty products commanding a good market. Abundance of
traditional skills in training, finishing and manufacturing downstream products and relatively low wage rates are the two
other factors of comparative advantage for India.
Tanning and finishing capacity: With tanning and finishing capacity for processing 1192 million pieces of hides and skins
per annum, the capability of India to sustain a much larger industry with its raw material resource is evident. In order to
expand the domestic raw material availability, the Government of India has allowed duty free import of hides and skins from
anywhere in the world. It attracts the foreign manufacturers who intend to shift the production base from a high cost location
to low cost location.
Export Potential: The leather industry, one of the major foreign exchange earner of the country recorded significant growth
since the beginning of the decade. Today the share of the value added finished products in the total exports from leather
sector is 80% as against 20% in seventies.
Indian Leather Footwear Industry: India is the world’s second largest producer of footwear; its production estimated over
11
700 million pairs per annum. At about US $ 300 million per annum, footwear accounts for 18 percent share of total exports of
leather exports.
Various types of shoes produced and exported from India include dress shoes, casuals, moccasins, sports shoes, horacchis,
sandals, ballerinas, and booties. Major production centers are Chennai (Madras), Delhi, Agra, Kanpur, Mumbai (Bombay),
Kolkata and Jalandhar.
Most of the modern footwear manufacturers in India are already supplying to well establish brands in Europe and USA. The
large domestic market and the opportunity to cater to world markets makes India an attractive destination for technology and
investments. Equally relevant is it for the footwear components industry, at this juncture, it is posed for real growth and
diversification.
Indian Leather Goods Industry: Items produced by this sector includes, in addition to the bags, handbags, handgloves and
industrial gloves, wallets, ruck sacks, folios, brief cases, travelware, belts, sports goods, upholstery and saddlery goods.
A surfeit of modern units in Chennai, Kanpur and Calcutta employing skilled human resources and equipped with modern
and sophisticated machinery account for a diversified range of superlative small leather goods including bags, purses, wallets,
industrial gloves etc. made of quality leathers of cows, sheep, goats and buffaloes. The products meet the requirement of bulk
buyers and consumers in Europe, USA and Australia.
The major market for Indian leather goods is Germany, with an offtake of about 25 per cent of the leather goods produced in
India followed by USA, UK, France and Italy. With products ranging from designer collections to personal leather
accessories, this sector has a share of 20.53 per cent in the leather industry, while maintaining an average growth rate of 11
per cent recorded in the last five years.
Indian Saddlery Industry: India is one of the largest producers of saddler and harness goods in the world. The saddler
industry was established in the 19 th
century primarily to cater to the needs of military and police. From then on initiatives
were taken to develop the industry and today there are over 150 units in the organized sector, out of which approximately 105
are 100% export oriented units.
Kanpur is a major production centre for dsaddlery goods in India accounting for more than 95% of the total exports of
saddler items from India. Kanpur, because of its specialisation in tanning and finishing of buffalo hides is the only centre in
the country where harness leather, which is major input for saddlery industry, is manufactured.
The export of saddler and harness items have showed an annual growth rate of about 40% reaching DM 64 million during
1998-99. The major importers of India saddler are Germany, USA, UK, France, Scandivinia, Netherlands, Japan, Australia,
and New Zealand.
Indian Leather Garments Industry: The Leather Garment Industry occupies a place of prominence in the Indian leather
sector. The product classification of leather garments comprise of jackets, long coats, waist coats, shirts, pant/short, children
garments, motorbike jackets, aprons and industrial leather garments.
Indian leather garments, which entered the world market only in the mid-eighties with exports of Rs. 15 crores in 1997-98,
account for about Rs. 1530 crore in 1997-98. The major export destination of leather garments from India is Germany. In
1997, German imports of leather garments aggregated DM 1786 million of which DM 304 million worth of imports went
from India. India, China and Turkey were the major suppliers of leather garments for the German market, as they accounted
for about 78% of the market share.
Among the three major exporting nations of leather garments, India maintains a similar level of market share of about 20%,
in both German and EU markets.
The main reasons reported for under utilisation of capacity are raw material shortage, high price of raw materials, lack of
modernisation, financial constraints, power constraints and stringent environmental regulations.
Rationale of the Study: Exports and Imports play a vital role in a country’s economy and it has its effect on a country’s
Balance of Payment position. India has been trading a number of products to various countries not only to meet its own
domestic demand. The main rationale behind this study is to understand the Indian Footwear Industry scenario in the present
12
world; and to know the position of Indian Industry in the world and also the export scenario of leather from India.
Objective of the Study: i. To explore Indian hides and leather market. ii. To study the growth of leather industry in India. iii.
To study the export potential of Indian hides and leather to other countires.
Methodology
The basic knowledge about the procedures of leather Industry has to be gathered through the secondary data available on the
internet and the documents available in the institute library. The quantum of information on this topic is updated. The
secondary data sources such as internet and news articles cover almost all major players. Although, the project does not
entitle for a primary research but for the validity and reliability of the research and better perception of the subject, a primary
research will be undertaken in some of the companies.
The Study: This study is exploratory in nature.
The sample: The sample used is the export data for the period of 2003-2015.
The tools: Data Collection: Secondary Source: those, which have already been collected by someone else and which have
already been through the statistical process and thus are available on internet sites and any other media for that matter. The
secondary data was collected from internet and references from Library.
Data Analysis: We will use trend analysis for the purpose of data analysis.
Result and Discussion
Year Value Trend Value
13
Discussion
Leather industry in India occupies a famous place in the Indian economy. Leather creates a huge potential for employment
opportunities, growth and exports in India. Leather industry in India recorded the major foreign exchange earnings and
significant growth for the past ten years. Exporters have been increasing emphasis on technological development and
optimum utilization of raw materials for high returns especially from exports.
According to the report, the CLE officials said that French leather industry, the 10th largest importer of leather products with
2.3% share in worldwide imports and fourth largest exporter with more than 4.5% share in worldwide exports, is the main
player in the global leather market. France exports mainly to Italy, China, Turkey, US and Germany. France imports
leather and leather products from the US, Spain, Australia, Zimbabwe and Germany.
France accounts for nearly 6.9% share of India’s total leather products export while other major leather markets for the
country are Germany (12.73%), UK (12.52%), Italy (11.74%), US (9.06%), Hong Kong (10.99%), Spain (6.48%),
Netherlands (3.31%), Belgium (1.78%), UAE (1.79%), & Australia 1.20%. The report also quoted that France has to be
viewed as a potentially interesting market by exporters from developing countries, especially India.
Major Markets: i. During 2009-10, the main markets for Indian leather industry are UK with a share of 19.66%, Germany
14.88%, Italy 13.93%, USA 8.20%, France 9.58%, Spain 6.37%, Netherlands 4.32%, Portugal 1.50%, U.A.E 2.63% and
Denmark 1.13.%. These 10 countries together accounts for 82.20% share in India’s total footwear export. ii. Nearly 90% of
India’s export of leather goes to European Countries and the USA. Future growth of Indian leather in India will continue to
be market driven. The European countries and the US are major consumers for the Indian leather.
Conclusion
The leather industry of India, which is amongst the leading leather industries of the world, following China, shows ten
percent growth every year As the global economy witnesses recovery from the financial meltdown, overseas sales of
footwear is likely to be more than two-fold times amounting to US $3.37 billion within the next two years. India has cutting
edge in the manufacturing plants. The footwear sector of India has matured from the level of the manual footwear
manufacturing method to the automated footwear manufacturing systems. World class machines are being used for the
production of footwear. Skilled technicians are responsible to operate on these machines to convert imagination into reality.
Support systems created for the sector have indeed served the footwear industry well.
Future growth of leather industry of India will continue to be market driven towards the European and the US markets.
Technology partnerships with major merchandising houses in USA and market leaders in Europe are decided advantages in
Year
2014-15
2013-14
2012-13
2011-12
2010-11
2009-10
2008-09
2007-08
2006-07
2005-06
2004-05
2003-04
14
the integrated developmental plan of India. Investment backed technology support for footwear component industry is being
sought to be outsourced.
The Government of India had identified the Leather Sector as a Focus Sector in the Foreign Trade Policy 2010-2014.
Considering the export potential and increasing demand, the Government of India has fixed an export target of US $ 8.25
billion to be achieved by the year 2013-14, from the current level of US $ 3.40 billion. This requires the industry to increase
its export by CAGR of 24.79%. The Transaction cost in export trade is one of the critical elements to increase the
competitiveness of our export, and help the industry to attain the export growth.
Suggestions
The Indian leather market is set to experience the phenomenal growth in coming years. There are few suggestions that can
further enhance the growth of Indian Footwear Industry - i. We are lagging behind China in leather exports; the major reason
behind this is that India is not able to provide leather at economical prices. Proper measure should be taken by Indian
Government in this regard so that Indian leather can also provide hides and leather at competitive prices. ii. The Industry
should focus on improving their methods of tanning and provide healthy and ethical methods of tanning. iii. We should
exploit fully the immense potential of leather industry in India so that more employment can be provided. iv. The best
possible utilization of available raw materials, proper planning and providing quality products to its customers will make
leather industry flourish in the future. v. Working conditions in Indian leather Industry should be improved i.e. workers
should be provided better working environment and facilities so that their productivity can increase.
Implications of the Study
This study will help all the individuals who are associated with the Footwear Industry in India.
For Tannery and Footwear Industry: This study will help the India leather Industry as it will help to know the demand of
leather in European & US market and then target them with the strategies suggested.
For the Exporters: This study will be helpful to the exporters to know what the export trends are. It gives current
information about the Indian leather Industry potential for exports.
For the Government: This study has generated useful suggestions for the government. If government implements the
suggestions generated on this basis it will help to promote exports of leather in the above mentioned markets. The
government will also be benefitted from this research in respect of policy they should adopt & the changes they should make
in the existing policy.
For the Researchers: This study will be helpful to the students who are interested in the research in this field. This study has
focused on European and US market but the methodology can be used to explore other markets also. The same method can
also be adopted to explore products in different countries.
References
2. Council of Leather Exports.
Webliography
15
Export Potential of Pharmaceutical Products from India to USA, Germany and Sri
Lanka
Dr. Satnam Kour Ubeja, Dr. Ranjana Patel, Dr. Manisha Singhai and Dr. Anukool Hyde Prestige Institute of Management and Research, Indore, M.P. India
[email protected]
Abstract
Indian Pharmaceutical products are very common now a days in foreign countries as a medicine. We use in export both
traditional as well as modern medicine. These medicines include all types of diseases simple and complex. Export industry of
pharmacy products are increasing in different manner. The objective of this study was to find the export potential of
pharmaceutical products from India to three countries. For the target objective; applied trend analysis and found the
different results that all three countries have significant growth in coming five years but one country has some constant
growth with respect to other two countries. With the help of this study businessman could able to make their strategies of
export of pharmaceutical products from Indian to any of these three countries.
Keywords: Export, Pharmaceutical, India, USA, Germany, Srilanka, Potential.
Introduction
Pharmaceutical Products are utilized to now days in like manner specialized terms i.e. solution or medication; it incorporate
old and new both the configuration. Employments of these items are more essential instead of offering angle. Item ought to
be great and up to the imprint as a quality standard perspective. A pharmaceutical prescription characterize as a drug which
might be utilized as recommended organization; it is likewise as substance which individuals use according to the need.
Individuals are utilizing drugs as prescription that may for curing the ailments. In the Indian business sector we have heaps of
abilities and chances to plan and offer extensive variety of uncommon medications for curing the unpredictable illnesses.
Indian business sector need present day advances for assembling the current arrangement drugs; we have science based
proficient experience individual of this range. Indian business sector is extremely sorted out structure in pharmaceutical zone
be worth of close $ 21.07 million dollar, developing at around 16 to 17 percent every year. After the third It positions
excessive within the 1/3 international, as some distance as innovation, pleasant and scope of drugs fabricated. From basic
cerebral ache drugs to modern anti-infection dealers and complex cardiovascular mixes, verging on every type medication is
currently made indigenously.
India in Pharmaceutical Patents Regime: journeys, the included innovation phase of the Uruguay spherical of the GATT
Treaty ,has provided ascend to a rancorous verbal confrontation among the created nations and much less created
countries(LDCs).commercial enterprise pastimes in the created global guaranteed vast misfortunes from the impersonation
and usage in their traits in LDCs. They additionally affirmed that IPRs might benefit the growing nations by empowering
outdoor task, by way of empowering alternate of innovation and more noteworthy residential progressive work (R&D). on
the other aspect ,LDC authorities had been agonized over the higher prices that more grounded IPRs could contain and about
the harm that their acquaintance may also bring about with new child baby slicing area businesses .India become successfully
required in proscribing the trips issue of the GATT assention ,especially the proposition for item licenses on pharmaceuticals
developments. Indira Gandhi compactly summed up the national assessment at the arena health assembly In 1982.'The
concept of a superior –ordered global is one wherein medicinal revelations may be free of licenses and there could be no
profiteering from life and death. "seeing that India has marked the bargain, but maximum unwillingly , it's miles centered on
providing pharmaceutical item licenses 2004 ,a really worth exam i.e. taken a toll –gain examination of this flow is
necessities for India .first some history. Profile of Pharmaceutical Industry in USA, GERMANY and SRILANKA.
With an expected offer of around 40% of the world pharmaceutical yield, the US is at present the significant assembling
center for pharmaceuticals on the planet. USA is the starting point of significant pharmaceutical organizations, who are into
improvement of new substance and natural elements.
The compound and pharmaceutical industry is Germany's third-biggest mechanical area regarding incomes. It delivers an
extensive variety of items, including fine chemicals, claim to fame chemicals, petrochemicals, inorganic chemicals,
engineered materials, agrochemicals and composts, clothing and cleaning specialists, beauty care products, pharmaceuticals,
paints and glues.
16
We at present hold 22% of the piece of the overall industry, prevailing as Sri Lanka's biggest, and steadily developing,
pharmaceutical merchant. In every one of our years, we've produced some intense associations with top industry
accomplices. By utilizing these connections and executing our reality class frameworks, we have possessed the capacity to
reliably convey superb pharmaceuticals to healing facilities and drug stores in all aspects of the nation. In all our work, we
hold fast to the most elevated codes of morals. We make top level augmentations to guarantee that honesty is safeguarded at
each progression of our procedures. Our specific offices are going by expert directors who have logistics down to a science.
Their superb work strategies joined with our proficient conventions guarantee that all parts of the store network stream
directly into the most optimized plan of attack, including enlistment, stock administration, importation and clearing,
warehousing, offering and dispersion, and offering.
Review of Literature
Macmillan. P (1998): In this study creator learn about the paper surveys profitability overflows from R&D, trades and the
very nearness of outside direct speculation (FDI) in China's assembling division, taking into account a board of more than
10,000 indigenous and remote contributed firms for 1998–2001. There are certain between industry profitability overflows
from R&D and fares, and positive intra-and between industry efficiency overflows from outside nearness to indigenous
Chinese firms inside areas. OECD-contributed firms appear to assume a much more prominent part in between industry
overflows than abroad Chinese firms from Hong Kong, Macao and Taiwan inside areas.
Blagden. N (2007): In these study the creator learn about the expanding predominance of ineffectively dissolvable
medications being developed gives striking danger of new items exhibiting low and inconsistent bioavailabilty with outcomes
for security and adequacy, especially for medications conveyed by the oral course of organization. Albeit various
methodologies exist for improving the bioavailability of medications with low watery solvency, the accomplishment of these
methodologies is not yet ready to be ensured and is incredibly reliant on the physical and compound nature of the particles
being produced. Precious stone building offers various courses to enhanced dissolvability and disintegration rate, which can
be received through a top to bottom learning of crystallization procedures and the sub-atomic properties of dynamic
pharmaceutical fixings.
Miesenbock. K (1994): In this study the creator learn around a speaker at the University of Economics, Vienna, Austria. The
present writing on worldwide business falls into two fundamental classifications: the primary spreads multinational
enterprises, their advancement, authoritative and advertising issues, and methodologies; the second includes the
internationalization of little and medium-sized organizations. Early productions on this issue showed up in the mid 1 960s
and from that point forward the issue has increased increasingly significance. Be that as it may, the broad writing in light of
experimental studies is loaded with irregularities and a decisive hypothesis of little business internationalization is a long way
from accessible.
Objectives of the Study: i. To determine the exports potential of pharmaceutical to USA, GERMANY & SRILANKA from
INDIA. ii. To study the growth in demand of Indian pharmaceutical products in USA, GERMANY & SRILANKA.
The Study: The study is totally based on the secondary data which shows the export potential of pharmaceutical products
from India to USA, GERMANY & SRI LANKA.
The Sample: Last 5 year (2010 – 2015) export historical data of pharmaceutical products will be taken as sample.
Tool for data collection: i. Website for Secondary Data www.ministryofcommerce.in, ii. Trade journals.
Tool for data analysis: For the purpose of data analysis trend analysis has been conducted using excel. Trend analysis is a
component of time series. Time series analysis is used to detect the pattern of change in statistical information over regular
interval of time, which is used to project the future trend. Thus time series analysis helps to cope up with uncertainty about
the future. Trend analysis shows the long-term direction of the series. We took year as an independent trend analysis is done
on linear regression. The formula is,
Y = a + b (x)
Where; a = constant, b = variable, x = number of year, y = result.
17
1 2010 8,34,485.29 896207.6
2 2011 12,20,373.01 1253052
3 2012 16,83,611.87 1609896
4 2013 20,86,619.45 1966740
5 2014 23,02,004.89 2323584
6 2015 26,02,814.21 2680429
Chart-1
With the help of trend analysis we can conclude that export potential of pharmaceutical product from India to USA having
continuity growth and it will also be increasing at least 2020.
Department of Commerce
1 2010 72,604.58 98609.99
2 2011 1,01,741.73 101053.5
3 2012 1,29,013.09 103497.1
4 2013 1,33,837.76 105940.6
5 2014 1,03,316.00 108384.1
6 2015 87,799.83 110827.7
value in lac trend
18
Chart-2
With the help of trend analysis we can conclude that export potential of pharmaceutical product from India to Germany had
been no growth earlier but now in the upcoming next five years having constant growth at least 2020.
Department of Commerce
144666.6
0
50000
100000
150000
200000
250000
300000
1 2 3 4 5 6 7 8 9 10 11
year value in lacs trend
19
Chart-3
With the help of trend analysis we can conclude that export potential of pharmaceutical product from India to Srilanka having
continuity growth and with the help of present date we can say that it will also be increasing in the linear form till 2020.
Conclusion
With this study, we can presume that the requests for the pharmaceutical item from the India are expanding with coming
patterns of globalization. With expansion in pharmaceutical mechanical India has propelled them self with the development
component of fare which they have enhanced over couple of years. India can possibly get to be center for pharmaceutical and
biotechnology disclosure; with its high favorable circumstances including substantial and instructed, talented, workforce, low
operational cost and enhancing administrative base. With this study we can presume that fare situation will increment
between India to USA, GERMANY and SRILANKA in coming one decade from now.
Reference
Website
value in lac trend
20
Ms. Ritu Talreja 1 and Dr. Rajeev Jhalani
2
1School of Commerce, DAVV, Indore, MP, India 2R.P.L. Maheshwari College, Indore, MP, India
[email protected]
Abstract
Women plays prominent role for the development of economy. In India the situation is different certain controls are put on
women. Women can manage the home efficiently, then why can’t she manage the business in an efficient manner. The success
of women is not equal in all countries based on social, cultural, demographical, geographical environments. It is different
from one country to another country. Women required certain unique motivational factors apart from economical support
and government support. The following paper is an attempt to find out the studies done so far in the field of women
entrepreneurship. For this we have considered the research paper from 60’s to latest. The motive behind that is to know the
view of people in each era.
Keywords: Entrepreneurs, women entrepreneurs.
Introduction
Woman constitutes the family, which leads to society and Nation. Social and economic development of women is necessary
for overall economic development of any country. In past times, only the males were considered capable of being an
entrepreneur but now the time is changed, the women is also coming forward in entrepreneurship as like other sectors as
compared to male. Due to developmental changes in environment, now people are more comfortable to accept leading role
of women in our society, though there are some exceptions of non-acceptance. Government should introduce such schemes
which help in progress of Women as an entrepreneur. Not only introducing schemes is sufficient but it should implement
systematically for the development of women entrepreneurs .The main motivators to women for success as an entrepreneur
should not be only in the form of financial assistance and government schemes, there should be psychological support from
family members also. Even though, government supporting in financial aspects, but without having moral support from
family, friends and relatives women may not get success as an entrepreneur. When it is a business someone should support
women at all times to avoid problems. This support will matter a lot to women and help her build up high morale with the out
comings of her leadership in the society.
Previously women were doing business in service sectors only like beauty parlours, dry cleaning etc. but now women are
entering in non-traditional areas like electronics, consultancy, construction firms etc. they have shown courage, willpower in
doing business, the role of women entrepreneur are limited up to urban areas but they have establish business and generate
employment in rural areas also.
Women have to face so much criticism both at family as well as at society level. Despite of all this women entrepreneur are
moving forward, just because of their confidence, hard work and patience. Now women are becoming carrier minded and
economically Independent.
Objective
The objective of the study is to present the compact review data for the researcher and to search the different views of
different researchers on relevant topic.
The second objective behind the paper is to present characteristics of women entrepreneur.
The study is purely based on secondary data. It’s an exploratory & descriptive in nature. The secondary data were collected
from various research papers published in different reports and journals. Data were also collected by surfing the net and from
relevant websites.
Literature Review
Singh, 2008, identifies the reasons & influencing factors behind entry of women in entrepreneurship. He explained the
characteristics of their businesses in Indian context and also obstacles & challenges. He mentioned the obstacles in the
growth of women entrepreneurship are mainly lack of interaction with successful entrepreneurs, social un-acceptance as
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women entrepreneurs, family responsibility, gender discrimination, missing network, low priority given by bankers to
provide loan to women entrepreneurs. He suggested the remedial measures like promoting micro enterprises, unlocking
institutional frame work, projecting & pulling to grow & support the winners etc. The study advocates for ensuring synergy
among women related ministry, economic ministry & social & welfare development ministry of the Government of India.
Lall & Sahai, (2008), conduct a comparative assessment of multi-dimensional issues & challenges of women entral problem
for future plans for growth & expansion of entrepreneurship, & family business. The study identified Psychographic variables
like, degree of commitment, entrepreneurial challenges & future plan for expansion, based on demographic variables.
Through stratified random sampling & convenience sampling the data have been collected from women entrepreneurs
working in urban area of Lucknow. The study identified business owner’s characteristics as self-perception self-esteem,
Entrepreneurial intensity & plans for growth & expansion. The study suggested that though, there has been considerable
growth in number of women opting to work in family owned business but they still have lower status and face more
operational challenges in running business.
Das, 2000 performed a study on women entrepreneurs of SMEs in two states of India, viz, Tamilnadu and Kerala. The initial
problems faced by women entrepreneurs are quite similar to those faced by women in western countries. However, Indian
women entrepreneurs faced lower level of work-family conflict and are also found to differ from their counterparts in western
countries on the basis of reasons for starting and succeeding in business. Similar trends are also found in other Asian
countries such as Indonesia and Singapore. Again the statistics showed that the proportion of business setup and operated by
women is much lower than the figures found in western countries.
Pramila Kapoor in her study (1974) has concentrated on women office workers and women in unusual professional and
occupation. The study discusses the impact of a married women’s employment on their marital and family relationships and
identifies the factors affecting marital harmony. It is an action oriented study which suggested measure to improve the status
of women and to achieve marital harmony.
A.S. Seetharam in his study (1981) cleared that women are not sufficiently motivated to participate in organized
movements.
Lalita Devi in her study (1982) tried to show that employed women have more power and influence in the family and
outside than unemployed women.
Sunita Kohli Chandra in her study has discussed Public Policies and Programmes affecting women entrepreneurs. She has
suggested in her study that government must analysis the current status role of women to bring change.
Dr. N.C. Pallai and Anna examine some reason like independent economic status, desire to earn money, engage herself
fully are stimulating factors which led women to industry.
B.B. lal Das in his paper named “Requirements of women Entrepreneurship Development in India with Special Reference to
Bihar” said that women have all qualities and abilities for successful entrepreneur. The section requires favourable climate
which motivated and encourage them.
Dr. Hanumant Yadav in his research paper entitled “Problems of women entrepreneurship in eastern Madhya Pradesh”,
reveals that shortage of funds is base of all the problem, if it is solved half of the major problems are solved.
Knight (1967) claims that an entrepreneur has an inherent risk bearing capacity through, the risk-bearer theory alone cannot
explain why some individuals become entrepreneurs while others do not.
Leibenstein (1968) clams that the, necessary characteristics of entrepreneurs is that they are gap-fillers i.e. they have the
better ability to connect different market and make up for market failures and deficiencies.
P. Babu (1978) stated that community and family background, formal education, infrastructure facilities and association of
small scale industries mainly contribute to the development of small enterprise.
Sharma R.A. (1985) opined that strong desire to do something independent in life technical knowledge and/or
manufacturing experience, financial assistance from institutional sources business experience in the same or related lime
accommodation in industrial estates and heavy demand emergence of new entrepreneurial class.
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Hisrich and Brush (1986) stated that the women entrepreneur is an individualist, creative, enthusiastic, instinctive and
adaptable person. Her concern is for growth and creating assets.
McKee (1989) indicated that poor women prefer to expand only to limit of their own labour and management capabilities on
the assumption that their basic needs have already been met.
Downing (1991) pointed out that most female entrepreneurs chose the path of lateral growth pattern, meaning increase in the
number of the enterprises in which they are engaged rather than the size because the expansion of enterprises increases the
risk.
The role of the entrepreneur is that of an innovator and Economist claims that entrepreneurs have special skills that enable
them to participate in the process of innovation (spontaneous processes); however, the definition of innovation is still widely
debatable Kirzner (1997).
Mrs. Thamaraiselvi (2007) opined that in spite of the mushrooming growth of associations, institutions, and the schemes
from the government, women are not as much enterprising and dynamic to optimize the resources in the form of reserves and
assets.
Noteworth contributions: According to some school of thought they conclude that, now India is in better position because
of introduction of women as an entrepreneur. This is mainly because of change of attitude of people towards women,
courageous and risk-taking Capabilities of women, support from society people, changes and relaxations in government
policies, granting various upliftment schemes to women entrepreneurs etc. extension to these findings there are certain other
factors which facilitates to get success as an entrepreneur as explained in this paper.
According to some analytical frame work, women Entrepreneurs are essential for achieving for the economic growth of the
nation. There are certain obstacles which hinder the growth of the nation should be avoided. Encouragement should be in
such a manner which allows women to participate. And to take up all kinds of business as an entrepreneur. Government
should provide proper training to women entrepreneurs. Government should use sophisticated methods to impart knowledge
in all functional areas. Promoting Women entrepreneurship is surely a successful path to develop Indian economy. Apart
from these women required psychological motivation also.
According to some analytical frame work, women family obligations and certain responsibilities lacking them to become a
successful entrepreneur. The financial institutions are having wrong opinion about women entrepreneurs, because at any time
they might become again housewife. Indian women give more importance to family members. They should handle dual role
as a housewife and businessmen. If there is no support from family members it is difficult to women to succeed as an
entrepreneur.
There is a study which explained that 51% women are working for 5-7 hrs.19% women have only 2-4 hrs for their business.
They have their house work and burden of the family work. They have less support from their family. May be they lives in a
separate family. Only 27% women gave 8-9 hrs to their business and they have huge family support and husband support. In
the study we find that there are only 3% women who are totally devoted to their business. For the women it is impossible to
give more hrs to the business in the serrate family and unsupported family.
Conclusion
The necessity of entrepreneurship for production was first formally recognized by Alfred Marshall in 1890. He believed that
entrepreneurship is driving element behind organization. Entrepreneurs by his default leadership qualities create new
commodities or improve “the plan of producing an old commodity” and further he/she must have the ability to foresee
changes in supply and demand and hence act accordingly. He claims that the abilities of entrepreneur are inherent and so
numerous that very few people can exhibit them in all in a very high degree although such abilities can also be required.
In India the past image of restricted and home maker women is changing slowly. The Indian women even after facing many
obstacles is now becoming an educated and economically independent entrepreneur. Govt. has come forward with many
facilities and incentives exclusively for women entrepreneur. But Women entrepreneurs not only required motivation in the
form of financial assistance, and government permissions and sanctions; they also require support from family members and
life partners and society as a whole. The progress of the nation not only depends on men’s performance but also on females.
In India there is a possibility to grow our economy by supporting women entrepreneurs. Family members and life partners
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should support to avoid stress in spite of these women have to go a long way for fulfilling their multiple roles of a mother,
wife and a businesswomen as well.
The review of the study in area of women entrepreneur shows that there is tremendous scope for women to grow. The only
requirement for their success is to provide support to them through government schemes, institute and by family support.
References
1. Knight, K.(1967) “A Descriptive Model Of The Intra- Firm Innovation Process” Journal Of Business Of The University
Of Chicago, Vol 40, 1967.
2. PramilaKapur: changing status of the women working in India,1974.
3. Babu, P. (1978), “A Study on Sociological Characteristics of small Scale Industries “Ph.D. Thesis, Dept. Of Sociology,
Kerela.
4. A.S Seetaram: women in organised movements,1979.
5. Marshall,A.(1980) Principles of Economics. Vol.1. London:Macmillan.
6. Lalita Devi: “status and employment of women in India”, B.R Publishing corporation Delhi,1982, p.186.
7. Sharma, R.A. (1985) “Entrepreneurial Change In Industry” Sterling Publisher, Jalandhar.
8. Hisrich, R., and Brush,C. (1986) The Women Entrepreneurs: Starting, Financing and Managing New Business.
Lexington, USA: Lexington Books.
9. Mc. Kee, K. (1989) Micro Level Strategies for Supporting Livelihoods Employment and Income Generation of Poor
Women in the Third World the Challenge of Significance World Development Vol.17. No.7, pp. 993-1006.
10. Dr. N.C. Pillai and Anna V: An article on “The entrepreneurial spirit among women- A study of Kerala” published in
Indian management, Nov- Dec 1990, p.93 to 98.
11. Downing, J. (1991) Gender and the Growth of Micro Enterprises Small Enterprise Development Vol. 2, No. 1, Pp.4-13.
12. B.B LalDas: “Requirements of women entrepreneurship development in India: problems and prospectus”, Jayanti
publication House 1994. P. 216-226.
13. HanumantYadav: “Problems in women entrepreneurship in Eastern Madhya Pradesh”,Vikas Publishing House, Jabalpur,
1996, p.114.
Economic Literature, 35:60-85.
16. Thamaraiselvi (12-09-2007) Women as Entrepreneurs In India; Http:// WWW. Articlebase.Com/ Entrepreneurship-
Articles/ Women-As- Entrepreneurs-In- India- 212759. Html.
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Studies. ISSN 2248-9878 Volume 3, November 4 (2013), pp. 371-376.
19. A.M. MahaboobBasha, K. SaiPranav, R.V.S.S NagabhushanaRao, K. Madhavi and P. Sri Sudha “A study on the
Development of Women Entrepreneurship in Nellore, AP, India” Research journal of management Science ISSN
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20. Leibenstein, H., ‘Entrepreneurship and Development’ lbid.5. op.cit.,p.156
21. Web Sites- www. Googlee.co. in, www. Wikipedia.com.
22. ShantaKohli Chandra: op.cit. ,p.70
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1School of Commerce, DAVV, Indore, MP, India 2R.P.L. Maheshwari College, Indore, MP, India
[email protected]
Abstract
Women entrepreneur is a person who accepts challenging role to meet her personal needs and become economically
independent. The problems, challenges and conditions are same for both men and women while doing business. In practice
most of women face problem that are of different dimensions as compared to male counterparts. These problems generally
prevent the women entrepreneur from realizing their potential as entrepreneur. The purpose behind the study is to identify
the factors which influence the women entrepreneur. The study is based on data collected from women entrepreneur of the
Indore city on the basis of random sampling technique through self-structured questionnaire.
Keywords: Entrepreneurship, Women Entrepreneur.
Introduction
There is a wor

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