MODELING THE EXTREME EVENTS OF THE TOP INDUSTRIAL RETURNS LISTED IN BSE

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Dr.G.S.David Sam Jayakumar and A.Sulthan, “Modeling The Extreme Events of The Top Industrial
Returns Listed In BSE” – (ICAM 2016)
MODELING THE EXTREME EVENTS OF THE TOP INDUSTRIAL
RETURNS LISTED IN BSE
Dr.G.S.David Sam Jayakumar
Assistant professor,
Jamal Institute of management, Jamal Mohamed College, Trichy-20
A.Sulthan
Research scholar,
Jamal Institute of management, Jamal Mohamed College, Trichy-20
ABSTRACT
Extreme price movements in the financial markets are rare, but important the objective of
study was to evaluate the extreme events of major industries in BSE. The study was conducted
for returns of industries and shows the extreme events to which the industries are scattered for
their returns. Many models were undertaken as base for the study, to identify the extreme
events of the industries and same has been incorporated for the analysis too.
Key words: Extreme events, BSE, returns, financial markets
Cite this Article: Dr.G.S.David Sam Jayakumar and A.Sulthan. Modeling The Extreme
Events of The Top Industrial Returns Listed In BSE. International Journal of Management,
7(2), 2016, pp. 341-353.
http://www.iaeme.com/ijm/index.asp
INTRODUCTION AND RELATED WORKS
Extreme price movements in the financial markets are rare, but important. The stock market crash on
Wall Street in October 1987 and other big financial crises such as the Long Term Capital Management
and the bankruptcy of Lehman Brothers have attracted a great deal of attention among investors,
practitioners and researchers. Stock market performance of a large sample of new issues (IPOs and
SEOs) following an extreme price movement during the first three years after the offering. Strong
underperformance follows either a positive or negative one-day return event. Financial position uses
the historical returns of the instruments involved to compute On the other hand, a conditional approach
uses the historical data and explanatory variables to calculate. The overreaction (cum irrational
exuberance-excess volatility) hypothesis (De Bondt and Thaler, 1985, Shiller, 1981), the momentum
strategy as an investment style (Jegadeesh, 1990, Jegadeesh and Titman, 1993), and the extreme or tail
risk phenomenon in financial markets. The bulk of the evidence in the overreaction and momentum
literatures is based on portfolio formation and performance evaluation using typical returns over many
sequential long intervals of a month to more than a year. The extreme risk phenomenon, on the other
hand, refers to market moves that are high in severity, low in frequency and short-term in duration.
Such an episode is most dramatically illustrated by the US market crash of 1987 and the 2008-09
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financial crisis, but may also occur on days of major macroeconomic or leading company information
and geopolitical events, or simply due to technical trading (overbought/oversold).The profitability of a
contrarian strategy (long the past losers, short the past winners) in support of the overreaction
hypothesis relies upon return reversal while that of a momentum style strategy (short the past losers,
long the past winners) rests on return continuation. Whether returns reverse or continue may, however,
depend on the term structure of returns and the investment horizon (De Bondt and Thaler, 1985,
Jegadeesh and Titman, 2001, Novy-Marx, 2012, Goyal and Wahal, 2013). Short term return reversal is
reported by Brown and Harlow (1988), Lehmann (1990) and Atkins and Dyl (1990). To date, however,
there is little published research about continuation versus reversals of extreme market movements, that
is, whether an extreme fall in the market on one day is followed by an extreme fall or rise in the market
in the following days and vice versa.1 As our events are defined using the broader market movements,
microstructure effects (bid-ask bounce, volume, etc.) and group or stock specific issues (e.g., size,
earnings, book to market ratios, analyst coverage, etc.) should have minimal influence on our results.
Further, all individual stocks in this paper share the same event dates. Daniel and Moskowitz (2013)
report that the conventional static momentum strategy “crashes” during periods of high market
volatility following a bear market (cumulative negative return of the CRSP VW Portfolio over the last
24 months). Although not studied directly, this result is indicative of the implication of extreme
movements in the overall market. This paper provides additional evidence in this regard by examining
portfolios that are formed conditional upon the infrequent but extreme daily movements in the broader
market. While not dynamic in the sense of continually updated trading portfolios as in Daniel and
Moskowitz (2013), the experiment is nonetheless more targeted. Brown, Harlow and Tinic (1988)
found positive abnormal returns in the 60 days following an individual stock price change greater than
2.5% in magnitude, for both positive and negative shocks. They advocate that this supports the
Efficient Market Hypothesis (EMH) since the positiveabnormal returns simply reflect the increase in
risk following the event. The authors name this framework as the Uncertain Information Hypothesis
(UIH). Corrado and Jordan (1997) argue that the 2.5% event threshold of Brown, Harlow and Tinic
(1988) is too low, thus generating too many events. For example, assuming a Normal distribution, this
threshold means that one event is expected to occur every ten days. Accordingly, Corrado and Jordan
(1997) employed a much larger event filter of 10% price change and found that, consistent with the
Overreaction Hypothesis (OH) of De Bondt and Thaler (1985), the negative (positive) events are
followed by positive (negative) abnormal returns (AR). Similarly, Bremer and Sweeney (1991)
reported a significant price reversal (above average returns), for the individual stocks of Fortune 500, in
the days after a stock experiences a large price decline such as more than 10%. Also, they did not find
this phenomenon to be related to market movements. Further studies in different markets and for
distinct shock magnitudes led to divergent results. Lasfer, Melnik and Thomas (2003), studying
international markets, found positive (negative) shocks leading to positive (negative) abnormal returns
on a 10 day window, and attributed this result to momentum. They also found that the intensity of the
abnormal returns is proportional to the magnitude of the event, and that this effect is more pronounced
in emerging markets than in developed countries. Employing a ±20% threshold, Himmelmann,
Schiereck, Simpson and Zschoche (2012) reported positive abnormal returns on European stocks after
both negative and positive events, thus supporting Brown, Harlow and Tinic (1988). In contrast,
although adopting the same threshold, Ising, Shciereck, Simpson and Thomas (2006) found
overreaction (underreaction) to positive (negative) events in the German market. Using a qualitative
approach to define favorable and unfavorable events, Mehdian, Nas and Perry (2008) reported positive
abnormal return for both cases in the Turkish market, lending support to the UIH. Recently, Savor
(2012) used analyst reports as a proxy for information and found that the informed events are followed
by drifts (momentum) and the uninformed events are followed by reversals (overreaction). Aside from
the fact that the above studies do not consider events in terms of extreme market movements, there is
also an important methodological issue. With the exception of Corrado and Jordan (1997), most of the
studies do not control their samples for overlapping events, that is, oneor more days in the post-event
period for calculating abnormal returns where the price change is of the magnitude used to define the
event. It is thus not clear whether the reported abnormal returns support a given hypothesis
(overreaction, momentum or the UIH), or simply reflect the influence of another extreme event in the
“post-event” period. The extant evidence becomes even more confounded as many studies measure the
expected (or normal) return from the “pre-event” window that itself contains an event in the case of an
overlap.The study deals with the industrial returns of major industries listed in BSE and shows the
extreme events to which the industries are scattered for their returns. Many models were undertaken as

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base for the study, to identify the extreme events of the industries and same has been incorporated for
the analysis too.
METHODOLOGY
NEED FOR STUDY
In the finance field, it is a common knowledge that money or finance is scarce and that investors try to
maximize their returns. But when the return is higher, the risk is also higher Return and risk go together
and they have a tradeoff. The art of investment is to see that return is maximized with minimum risk. In
the above discussion we concentrated on the word “investment” and to invest we need to analysis
securities. Combination of securities with different extreme events characteristics will constitute the
portfolio of the investor.
OBJECTIVES
1. To know the industry profile of BSE.
2. To study the extreme events in stock returns of selected industries.
3. To study the extreme events of selected top industries.
4. To study the systematic extreme events involved in the selected industries stock.
5. To offer some suggestions to the investors.
INDUSTRY SELECTION
The monthly data of following industries Automobile, Health care, PSU Capital goods, Bank,
Consumer durables, FMCG, IT, Power, Metal and Oil&Gas are considered.
DATA SAMPLE
The study was conducted for log return of industries from October 2011 to June 2014. The closing
price of companies in the selected industry was collected from historical data available in BSE website.
DATA ANALYSIS
The analysis was conducted at different stages by utilizing selected time series econometric
technique. In Stage-1, the multivariate normality of the data is tested. In Stage-2 industrial returns of
top industries were identified by using multi T-square distance test. While in Stage-3 stepwise
discriminant analysis for extreme event in industries are analyzed.
PROFILE OF SELECTED INDUSTRIES
The Bombay Stock Exchange (BSE) (formerly, The Stock Exchange, Bombay) is a stock exchange
located on Dalal Street, Mumbai and is the oldest stock exchange in Asia. The equity Market
capitalization of the companies listed on the BSE was US $1 trillion as of December 2011, making it
the 6th largest stock exchange In Asia and the 14th
largest in the world. The BSE has the largest
number of listed companies in the world. As of December 2011, there are over 5,112 listed Indian
companies and over 8,196 scrips on the stock exchange, The Bombay Stock Exchange has a significant
trading volume. The BSE SENSEX, also called "BSE30", is a widely used market index in India and
Asia. Though many other exchanges exist, BSE and the National Stock Exchange of India account
forth majority of the equity trading in India. While both have similar total market capitalization
(aboutUSD1.6trillion), share volume in NSE is typically two times that of BSE.
SELECTEDINDUSTRIES LISTEDINBSE
AUTOMOBILEINDUSTRYIN INDIA
The Indian automobile sector is one of its most vibrant industries. The industry accounts for 22 percent
of the country's manufacturing gross domestic product(GDP).It comprises passenger cars, two-
wheelers, three-wheelers and commercial vehicles and is currently the seventh-largest in the world with
an average annual production of 17.5million vehicles, of which 2.3millionare exported. The Indian

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auto market has the potential to dominate the global auto industry, provided a conducive environment
is created for potential innovators to come up with new pilot projects. Then ext few years are projected
to show solid but cautious growth due to improved affordability, rising incomes and untapped markets.
All these open up an opportunity for automobile manufactures in India. In addition, with the
government's backing and a special focus on exports of small cars, multi-utility vehicles (MUVs), two
and three-wheelers and auto components, the automotive sector's contribution to the GDP is expected
to double, reaching a turn over of US$145 billion in 2016, according to the Automotive Mission Plan
(AMP) 2006-2016.
INVESTMENTS
Some of the recent major investments in the automobile industry in India are as follows: BMW Group
has launched the third generation of its sports utility vehicle (SUV), the X5x Drive30d,which will be
Rs1million (US$16,635.94) cheaper than the previous version, as the model will now be assembled at
the company's Chennai plant rather than being imported fully assembled. Japan's Isuzu Motors aims to
sell 50,000 pickup vehicles in India in the next few years to gain market leadership. The company,
which has a fully owned subsidiary in Chennai, has marked Rs.3,000 crore (US$499.07million) for a
120,000 units per year manufacturing facility. Mercedez-Benz India has inaugurated South India's first
AMG Performance Centre at Sundaram Motors in Bengaluru and has also launched the ML 63 AMG
for the Indian market. Mercedes-AM Gains to offer a more personalized service to its customers and
further bolster its powerful luxury SUV product portfolio in India.VE Commercial Vehicle, a joint
venture (JV) between Eicher Ltd and Volvo, is exploring the possibility of entering the small
commercial vehicle segment with arrange of mini trucks. With this move, they plant on the market with
bigger rivals such as Tata Motors, Mahindra and Mahindra and Ashok Leyland. Fiat plans to launch 12
models based on three platforms, double i ts work force to 5,000 and increase capacity by 80 percent its
Ranjanga on plant by 2018. Mahindra & Mahindra (M&M) has inaugurated a factory and a research
center for electric wheelers in Ann Arbor, Michigan, US. With an initial capacity to produce 9,000
vehicles annually, the plant will assemble its first electric two-wheeler later this year.
GOVERNMENT INITIATIVES
SIAM and the Automotive Component Manufacturers Association of India (ACMA) are two apex
bodies appointed by the Government of India to work for the development of the automobile industry
in India. India has a well-established Regulatory Framework under the Ministry of Shipping, Road
Transport and Highways in which SIAM plays an important role. Also, ACMA's active involvement in
trade promotion, upgrade in technology, quality enhancement and collection and dissemination of
information has made the body a vital catalyst forth industry's development. The Indian government
encourages foreign investment in the automobile sector and allows 100 percent FDI under the
automatic route. It is a fully delicensed industry and free import so automotive components are
allowed. Moreover, the government has not laid down any minimum investment criteria forth
automobile industry and has formulated the Automotive Mission Plan for the period 2006-2016 which
aims to accelerate and sustain growth in this sector. The plan also aims to double the contribution of the
automotive sector of the country's GDP by taking its turnover to US$145 billion and providing
additional employment to25 million people by2016.
HEALTHCARE INDUSTRY
India has been awarded a Polio Free‘status by way of an official certification presented by the World
Health Organization (WHO). India is among other countries in the South East Asian region which have
been certified as being free of the polio virus. India has been polio free since January 2011, as per
MrGhulam Nabi Azad, Minister for Health and Family Welfare, Government of India. Health care in
India today provides existing and new players with a unique opportunity to achieve innovation,
differentiation and profits. In the next decade, increasing consumer awareness and demand for better
facilities will redefine the country‘s second largest service sector employer. India's primary competitive
advantage over its peers lies in its large pool of well- trained medical professionals. Also, India's cost
advantage compared to peers in Asia and Western countries is significant cost of surgery in India is one
tenth of that in the USor Western Europe. In India, the diagnostics sector has been witnessing immense
progress in innovative competencies and credibility. Technological advancements and higher efficiency
systems are taking the market on heights. The RNCOS report, 'Indian Diagnostic Market Outlook to

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2015', highlights that the IVD equipment market will grow at a compound annual growth rate (CAGR)
of around15 per cent from 2012 to 2015.Healthcare providers in India are expected to spend US $1.08
billion on IT products and services in 2014, a four per cent increaseover2013.
PUBLIC SECTOR UNDERTAKINGSINDUSTRY
Central and state Public Sector Undertakings (PSUs) play a prominent role in India‘s industrialization
and economic development. Since independence, various socio-economic problems needed to be dealt
with in a planned and systematic manner. A predominantly agrarian economy, a weak industrial base,
low savings, inadequate investments and lack of industrial facilities called or state intervention to use
the public sector as an instrument to steer the country‘s underlying potential towards self-reliant
economic growth. The macroeconomic objectives of Central PSUs have been derived from the
Industrial Policy Resolutions and the Five Year Plans. State-level public sectors enterprises (state
PSUs) were established because of the rising need for public utilities in the states. These PSUs
operated in public utilities such as railways, post and telegraph ports, airports and power and
contributed significantly towards infrastructure development in India. Since its inception during the
First Five Year Plan, many public sector undertakings performed exceptionally well in wealth creation
for the country. Many Central PSUs, particularly the Maharatnas, are already global players matching
the best global firms in their field of operations. One of the important reasons for the excellent
performances of Central PSUs during the recent years was the empowerment of the boards of such
profit making Central PSUs by t h e Government leading to greater autonomy.
CAPITAL GOODS INDUSTRY
The development of a strong and vibrant engineering and capital goods sector has been at the core of
the industrial strategy in India since the planning process w a s initiated in 1951. The emphasis that
this sector received was primarily influenced by the rest while Soviet Union model, which made
impressive progress by rapid state-led industrialization through the development of the core
engineering and capital goods sector. The ‗Mahalanobis Model‘, which was a ‗supply oriented model
with a basic emphasis on increasing the rate of capital accumulation and saving, gave the engineering
and capital good sector a central place. Super imposed over this were the other objectives of balanced
regional development, prevention of the concentration of economic power and the development of
small-scale industries. One of the primary objectives was import substitution, which was persuades a
priority. A capital good is a durable good (one that does not quickly wear out) that is used in the
production of goods or services. Capital goods are one of the three types of producer goods, the other
two being land and labor, which are also known collectively as primary factors of production. This
classification originated during the classical economic period and has remained the dominant method
for classification.
BANKING SECTOR
India is considered among the top economies in the world, with tremendous potential for its banking
sector to flourish. The last decade witnessed a significant up surgein transactions through ATMs, as
well as internet and mobile banking. The country's banking industry looks set for greater
transformation. With the Indian Parliament passing the Banking Laws (Amendment) Bill in 2012, the
landscape of the sector has duly changed. The bill allows the Reserve Bank of India (RBI) to make
final guide lines on issuing new licenses, which could lead to a greater number of banks in the country.
The style of operation is also slowly evolving with the integration of modern technology in to the
banking industry.In the next 5-10years, the sector is expected to create up to two million new jobs
driven by the efforts of the RBI and the Government of India to expand financial services into rural
areas. Two new banks have already received licenses from the government, and the RBI's new norms
will offer incentives to banks to spot bad loans and take necessary recourse to curb the practices of
rogue borrowers. The size of banking assets in India totaled US$ 1.8 trillion in FY13 and is
expected to touch US $28.5 trillion in FY 25. Bank deposits have grown at a compound annual growth
rate (CAGR) of 21.2 percent over FY06-13. In FY13, total deposits were US$1,274.3 billion. The
revenue of Indian banks in creased fromUS$11.8 billion to US$46.9 billion over the period 2001-2010.
Profit after tax also reached US$12 billion from US$1.4 billion in the period.Credit to housing sector
grew at a CAGR of 11.1 percent during the period FY08-13. Total banking sector credit is anticipated
to grow at a CAGR of18.1percent (in terms of INR) to reach US$ 2.4 trillion by 2017. In FY14, private

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sector lenders experienced significant growth in credit cards and personal loan businesses. ICICI Bank
saw 141.6 per cent growth in personal loan disbursement in FY14, as per are port by Emkay Global
Financial Services. The bank also experienced healthy growth of 20.8 percent in credit card dues,
according to the report. Axis Bank's personal loan business also grew 49.8 percent, with its credit card
business expanding by 31.1 per cent.
CONSUMER DURABLES
India has been a consumption-driven economy forth last many decades. Consumer spending in the
country is expected to increase about 2.5times by 2025. Broadly categorized into urban and rural
markets, the Indian consumer segment is gaining high attention and pampering from marketers across
the globe. Global corporations view India as one of the key markets from here future growth will
emerge. The growth in India‘s consumer market will be primarily driven by a favorable population
composition and rising disposable incomes. A recent study by the McKinsey Global Institute (MGI)
suggests that if India continues to grow at the current pace, average household incomes will triple over
the next two decades and the country will be come the world‘s fifth largest consumer economy by
2025, up from 12th at present. The Government of India plays a catalytic role in the growth of Indian
consumer segments and their welfare. Itha seased key rules on foreign direct investment (FDI) in an
attempt to attract foreign firms to boost economic growth. As people are demonstrating an increasing
online shopping, future prospects pose a tremendous growth opportunity for retail and FMCG players
alike. India is likely to emerge as the world‘s largest middle class consumer market with an aggregated
consumer spend of nearly US $13 trillion by 2030, as per are port by Deloitte titled 'India matters:
Winning in growth markets'. Fuel led by rising incomes and growing affordability, the consumer
durables market is expected to expand at a compound annual growth rate (CAGR) of 14.8 percent to
US $12.5 billion in FY 2015 from US $7.3 billion in FY 2012. Urban markets account for the major
share (65percent) of total revenues in the Indian consumer durables sector. In rural markets, durables,
such as refrigerators, and consumer electronic goods are likely to witness growing demand in the
coming years.FromUS$2.1 billion in FY2010, the rural market is expected to grow at a CAGR of 25
per cent to touch US$ 6.4 billion in FY 2015. The growth of internet retail is going to complement the
growth of offline retail stores. Online retailing, both direct and through market places such as eBay,
will triple to become a Rs50,000 crore (US$8.34billion) industry by 2016, growing at a whopping 50–
55 percent per year over the next three years, according to rating agency Crisil. With growing
consumerism and disposable income, India's used goods market is likely to touch Rs.115,000 crore
(US$19.18 billion) by 2015 from Rs 80,000 crore (US$13.34billion) at present, according to a study by
an industrial body. Whether consumer goods like electronics, durables, automobiles, etc., or industrial
machinery in the capital goods sector, the options of re usage are being considered more actively than
ever before coming up at Nilakottai near Madurai. It is expected to start commercial production by the
end of 2014, according to Mr Anshu Budhraja, Chief Operating Officer, Amway India.
FAST-MOVINGCONSUMER GOODS (FMCG) INDUSTRY
Fast-Moving Consumer Goods (FMCG) or Consumer Packaged Goods (CPG) are products that are
sold quickly and at relatively low cost. Examples include non-durable goods such as soft drinks,
toiletries, Over the counter drugs, toys, processed food sand many other consumables. Though the
profit margin made on FMCG products is relatively small (more so for retailers than the
producers/suppliers), they are generally sold in large quantities; thus, the cumulative profit on such
products can be substantial. FMCG is probably the most classic case of low margin and high volume
business. Fast-moving consumer electronics are a type of FMCG and are typically low priced easily
substitutable consumer electronics, including and digital cameras which are of disposable nature.
THEORETICALFRAMEWORK OFEXTREME VALUE THEORY
Extreme value theory or extreme value analysis (EVA) is a branch of statistics dealing with the
extreme deviations from the median of probability distributions. It seeks to assess, from a given
ordered sample of a given random variable, the probability of events that are more extreme than any
previously observed. Extreme value analysis is widely used in many disciplines, such as structural
engineering, finance, earth sciences, traffic prediction, and geological engineering. For example, EVA
might be used in the field of hydrology to estimate the probability of an unusually large flooding event,

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such as the 100-year flood. Similarly, for the design of a breakwater, a coastal engineer would seek to
estimate the 50-year wave and design the structure accordingly.
DATA ANALYSIS
Two approaches exist for practical extreme value analysis. The first method relies on deriving block
maxima (minima) series as a preliminary step. In many situations it is customary and convenient to
extract the annual maxima (minima), generating an "Annual Maxima Series" (AMS). The second
method relies on extracting, from a continuous record, the peak values reached for any period during
which values exceed a certain threshold (falls below a certain threshold). This method is generally
referred to as the "Peak Over Threshold" method (POT) and can lead to several or no values being
extracted in any given year.
For AMS data, the analysis may partly rely on the results of the Fisher–Tippett–Gnedenko
theorem, leading to the generalized extreme value distribution being selected for fitting. However, in
practice, various procedures are applied to select between a wider range of distributions. The theorem
here relates to the limiting distributions for the minimum or the maximum of a very large collection of
independent random variables from the same arbitrary distribution. Given that the number of relevant
random events within a year may be rather limited, it is unsurprising that analyses of observed AMS
data often lead to distributions other than the generalized extreme value distribution being selected.
For POT data, the analysis involves fitting two distributions: one for the number of events in a
basic time period and a second for the size of the exceeders. A common assumption for the first is the
Poisson distribution, with the generalized Pareto distribution being used for the exceeders. Some
further theory needs to be applied in order to derive the distribution of the most extreme value that may
be observed in a given period, which may be a target of the analysis. An alternative target may be to
estimate the expected costs associated with events occurring in a given period. For POT analyses, a
tail-fitting can be based on the Pickands–Balkemade Haan theorem.
RESULTS AND DISCUSSION
Table 1 Univariate test of normality
Industries SW statistic AD statistic p-value
Auto 0.991 1.978 <0.001*
Health care 0.992 2.12 <0.01*
PSU 0.995 1.441 <0.01*
Capital goods 0.991 2.42 <0.01*
Bank 0.984 3.491 <0.01*
Consumer durables 0.97 5.556 <0.01*
FMCG 0.982 4.206 <0.01*
IT 0.924 11.012 <0.01*
Power 0.989 2.848 <0.01*
Metal 0.989 2.295 <0.01*
Oil& gas 0.997 1.114 <0.01*

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Table 2 Multivariate test of Normality
Test name Coefficient statistic p-value
Mardia's Skewness 5.977 998.678 <0.01
Mardia'sKurtosis 207.459 60.235 <0.01
HenzeZirkler - 1.557 <0.01
Table 3 Descriptive statistics of industry returns
Industries Minimum Maximum Mean SD Variance CV Skewness Kurtosis
Auto -4.779 5.983 0.057 1.297 1.683 22.927 0.176 1.095
Health care -3.529 3.111 0.072 0.861 0.742 11.908 -0.186 0.804
PSU -4.669 4.503 -0.042 1.141 1.301 -26.947 -0.088 0.701
Capital goods -5.57 5.498 -0.021 1.555 2.418 -75.735 -0.035 0.857
Bank -5.552 9.305 0.038 1.602 2.568 42.164 0.197 1.729
Consumer durables -8.384 5.701 0.053 1.524 2.323 28.788 -0.352 2.758
FMCG -3.89 5.303 0.09 1.066 1.135 11.81 0.059 1.778
IT -11.094 9.339 0.065 1.413 1.996 21.593 -0.462 8.449
Power -4.514 4.351 -0.056 1.257 1.579 -22.64 -0.24 0.941
Metal -5.819 8.226 -0.043 1.696 2.876 -39.216 0.218 1.194
Oil& gas -4.787 3.827 -0.008 1.294 1.675 -153.481 0.029 0.284
Control Chart showing the Extreme variation of industry return at 5%and 1%significancelevel
Figure 1

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Figure 2
Table-1 to3 visualizes the result of Mardia’s Multivariate test of normality such as Mardia’s
Skewness test, Mardia’s Kurtosis test and Henze Zirkler test. The test was applied for the returns of top
securities listed in BSE. The result of the test confirms that the security returns of securities are
departed from Multivariate normality and the returns are non-normally distributed. Hence, the
researcher assumed that the returns of securities are non-normally distributed. Among the top10
industries the Mean Returns of FMCG industry is high followed by Healthcare, IT, Automobile
respectively. As for as Health Care industry is concerned standard deviation of returns are less
compared to remaining industries which are highly consistent. Finally the univariate skewness,
kurtosis, Shapiro Wilk test statistics and Anderson darling statistics confirms that the returns of FMCG
industry are departed from Univariate normality and it follows the non-normal distribution. Control
chart fig.1 and fig.2 visualize the extreme variation of industry return during the 999days’ time period.
The upper control limit for the control chart 4.1 and 5% significance level 24.56 nearly out of 999days
the mainly of 64 days are having extreme volatility in the industrial returns at 5% significance level. As
for as 1% significance level the upper T square distance is 19.59 out of 999days 113days having
extreme volatility in the industry returns the shows the T-square distance and industry distance normal
static it may have extreme volatility in the coming days.
Results of stepwise Multiple Descriptive Analysis
Table 4 Eigen value
Function Eigen value %of Variance Cumulative % Canonical Correlation
1 .043
A 100.0 100.0 .204
Table 5 Wilk’s Lambda
Test of function(s)
Wilks'
Lambda
Chi-square df Sig.
1 .958 42.318 6 .000

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Table 6 Standardized Canonical Discriminant Function Coefficient
Industries Function
Auto -.636
Healthcare .758
Consumer Durables .487
Power .815
Metal -.533
Oil&Gas -.673
Table 7 Classification results
Out of control Points
Predicted Group Membership
Total
Outlier Inlier
Outlier
Count
Inlier
1 63 64
0 935 935
Based on the previous analysis, the result of multivariate outlier detection technique and control
chart shows, out of 999days, returns of the industry was extremely erratic for 64days and this
confirms the industry return has an extreme behavior. Moreover from table4 to7 describes the results
of the stepwise multiple discriminant analysis .The calculated value close to 0 the chi-square test the
also significant at 5% level. More over table 6 reviews, out of 10 industries, the returns of the 6
industries namely auto, h e a l t h c a r e , consumer durable, power, metal, oil and gas all most
dominate industry which mate industries behave extremely. Hence any events upon in the industries
will leads to extreme events return of the industries.
RESULTOFSTEPWISE RETURN ANALYSIS
Table 8 Eigen values
Table 9 Wilk’s Lambda
Test Of Function(S) Wilks' Lambda Chi- Square DF SIG.
1 .960 40.152 4 .000
Function Eigenvalue %Of Variance
Cumulative
%
Canonical Correlation
1 .041 100.0 100.0 .199

351
Table 10 Standardized Canonical Discriminant Function Coefficient
Table 11 Classification of result
Out of control Points
Predicted Group Membership
Total
Outlier Inlier
Outlier
CountInlier
2 111 113
0 886 886
Based on the previous analysis, the result of multivariate outlier detection technique and control
chart shows, out of 999days, the returns of industry was extremely erratic for 64days and this
confirms the industry return have an extreme behavior. More over from table 8 to 11 describes the
result of the step wise multiple discriminant analysis. The calculated value close to 0 the chi-square is
also significant at 1% level. More over table 10 reviews out of 10 industries, the returns of the 4
industries namely auto, healthcare, consumer durable, oil and gas almost dominate industry which
behave extremely. Hence any events upon in the industries will leads to extreme events
return1oftheindustries.
SUGGESTIONS
From the study conducted on evaluation of extreme events of returns of top industries listed in BSE,
the following suggestions were given to the investors. The returns of Auto mobile industry is high in all
the years which reveals that accordingly to the Mardia’s skewness Model, if the invest or choose to
invest their funds in automobile industry, they can achieve a maximum possible returns while
compared to the other industries. Moreover, the returns of Banking, Health care and ITindustry are
greater when compared to the other industries in overall period basis. The returns earned from
Automobiles, Healthcare, OilandGas, Metal and consumer durables industries follows the above
maximum yielding industries. So, the researcher by having Mardia’s kurtosis model as the base
suggests the investors to invest their funds in automobile industry followed by banking, Healthcare and
power industries which has a higher positive returns with lower risk.
CONCLUSION
Based on the analysis, the researcher comes to a concrete conclusion. This study deals with the risk of
extreme events of returns for the selected industries listed in BSE. At first the researcher observes that
the returns of the industries are non normally distributed and it’s having a different pattern. Moreover,
the researcher emphasis the investors to look in to the average amount of returns of the security and
also the amount of risk involved before investing their funds. If, the investors observe the industries
they can see the returns of in the automobile industry plays a vital role followed by banking industry,
Power industry, Oil and gas industry, IT industry, Metal and steel industry, Health care industry and
FMCG industry so on. Finally, the selection and ARCH model of extreme events is the most
important aspect to be considered by an investor whether he or she may be an individual or
institutional investor. According to the results of the analysis the researcher recommends the investors
to invest their funds in Automobile, Banking and Power industries, and then only they can earn a
maximum return with the nominal risk. This research is very helpful to make an investment to the best
companies and also they have an idea about the extreme events of stock and market return and the
market risk. The data analysis, findings and the suitable suggestion.
Industries Function
Auto 0.985
Healthcare -0.956
Consumer Durables -0.460
Oil&Gas -0.441

352
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