Chapter 2

Review of Literature and Research Methodology

2.1 Review of Literature

This chapter reviews relevant studies which make a base for the present study. Then it

outlines the research methodology which is followed in the thesis.

The financial markets in India have gone through various stages of liberalization that

has increased its degree of integration with the world markets. Some instances of new

policy reforms introduced in the Indian stock markets include introduction of trading

in index futures in June 2000, trading in index options in June 200 I, trading in options

on individual securities in July 2001, introduction of VAR (value at risk)-based

margin, and introduction of the T+2 settlement system from April, 2003. After

implementation of such reforms, the Indian securities market has now become

comparable with securities markets of developed and other emerging economies. In

fact, India has a turnover ratio that is comparable with that of other developed markets

and also one of the highest in the emerging markets. These developments in the

Indian securities market have drawn attention of researchers from across the globe to

look at the price behaviour of the Indian securities market. The daily gross activity

(purchase and sales) of the Foreign Institutional Investors (FlI) in the Indian stock

market has increased almost three-fold in three and half years, from Indian Rupees

(Rs.) 6 billion in October 2000 to Rs. 17 billion by the end of January 2004, to Rs. 1.8

trillion in January 2008.

The increasing interests of foreign investors in the Indian market call for greater

research on various properties and mainly the increased volatility of this market. The

present thesis examines the evidence of stylized facts with focus on volatility in the

Indian stock market.

It is hoped that the findings of this study would greatly help fund managers have a

better understanding of the Indian stock market volatility.

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The magnitude offluc/llations in the return of an asset is called its volatility. As a

concept, volatility is simple and intuitive. It measures variability or dispersion about a

central tendency. To be more meaningful, it is a measure of how far the current price

of an asset deviates from its average past prices. Greater this deviation, greater is the

volatility. At a more fundamental level, volatility can indicate the strength or

conviction behind a price move. Despite the clear mental image of it, and the quasi­

standardised status it holds in the field of finance, there are some subtleties that make

volatility challenging to analyse. Since volatility is a standard measure of financial

vulnerability, it plays a key role in assessing the risk/return tradeoffs and forms an

important input in asset allocation decisions].

The relationship between feedback trading and volatility persistence is well­

documented in financial literature with evidence about their significant joint presence.

This is the "open sesame" for manipulation. It suggests that feedback traders are

capable of bearing a destabilizing influence over securities prices, an issue of key

importance especially in the context of emerging markets due to the vulnerable

structure of those markets.

Numerous products are available on NSE following the introduction of derivative

trading. There are futures and options of variable periods. There is a blanket closure at

the expiry on the last Thursday of every month. They have a role in maintaining the

market stability.

We have reviewed studies encompassing many features and factors that would affect

volatility, such as - contribution of derivatives, the presence of rational and irrational

traders, cyclicity of returns and stock prices, review of popular models to estimate

volatility, studies that focus on psychology as behaviour biases etc. Considering the

global influence as well as scarcity of topic related work in India, we reviewed the

vast literature nationally and globally. The various studies try to arrive at a

mathematical model and are discLlssed briefly below:

• Sah and Omkarnath (2003)2 did not tind any contribution of derivatives

trading by comparing the period before and after its introduction. The study

was undertaken by them in year 2003 when the derivative market in India was

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at a very nascent stage. The volumes in derivates have greatly picked up since

2005. Thus the contribution of volatility to intraday and day to day volatility is

now seen in the stock markets.

• Barberis and Thaler 20023 emphasised that there are rational and irrational

traders in the markets. Therefore, prediction based on behaviour finance must

take into account the effects that irrational market players will have on the

markets. Hence, the rational market players are also influenced. This

observation thus obliterates the predictability of markets based on behaviour

finance. Their study leads to an interesting observation that volatility cannot

exist if only the rational investors were investing! They have thus highlighted

market efficiency being dependent upon investor psychology and investor

behaviour in the comparative recent discipline (evolving since 1985), viz.

Behavioural Finance. They argue that some financial phenomena can plausibly

be understood using models in which some agents are not fully rational. The

field has two building blocks: till/its to arbitrage, which argues that it can be

difficult for rational traders to undo the dislocations caused by less rational

traders: and psychology, which catalogues the kinds of deviations from full

rationality we might expect to see. They discuss these two topics, and then

present a number of behavioural finance applications: to the aggregate stock

market, to the cross-section of average returns, to individual trading

behaviour, and to corporate finance.

• In segmented capital markets, a country's volatility is a critical input in the

cost of capital (Bekaert and Harvey 1995)4.

• Peters (1994)5 noted that stock prices and returns are cyclical, imperfectly

predictable in the short run, and unpredictable in the long run and that they

exhibit nonlinear, and possibly chaotic, behaviour related to time-varying

positive feedback. Asset-return variability can be summarised by statistical

distributions. Typically, the normal distribution is used to characterise a series

of returns. The distribution is centred at the mean and its width is determined

by the standard deviation (volatility). Return series may not be normally

138

distributed and often tend to exhibit excess Kurtosis" so that extreme values

are more likely than the normal distribution would suggest. Such fat-tailed

distributions are common with financial parameters. Skewnessii is also

common, especially with equity returns, where big down moves are typically

more likely than comparable, big up-moves.

• Time-variation in market volatility can often be explained by macroeconomic

and micro-structural factors (Schwert I 989a,b)6/. Volatility in national

markets is determined by world factors and part determined by local market

ejfects. assuming that the nationolmarkets are glubally linked

• It is also consistent that world factors could have an increased influence on

volatility with increased market integration. Bekaert and Harvey (1995)8

showed this using time-varying market integration parameter.

The prediction of volatility in financial markets has been of immense interest among

financial econometricians.

• This interest is further rekindled by Bollerslev et al. (1994)9 when they

established that financial asset return volatilities are highly predictable.

It is true that unlike prices, volatilities are not directly observable in the market, and it

can only be estimated in the context of a model.

• However, Andersen et al. (2001)10 concluded that by sampling intra-day

returns sufficiently frequently, the reali~ed volatility (measured by simply

summing intra-day squared returns) can be treated as the observed volatility.

J In probability theory and statistics. kurtosis (hom the Greek word KUproS, kyrtos or kurtos, meaning bulging) is a measure of the "peakedness" of the probability distribution of a real-valued random variable. lligher kurtosis means morc of the variance is the result of infrequent extreme deviations, as opposed to frequent modestly sized deviations. 11 In probability theory and statistics. skewness is a measure of the asymmetry of the probability distribution of a real-valued random \'ariable.

139

• This observation has profound implication for financial markets (Brooks

1998) 11 in that

a) The realized volatility provides a better measure of total risk (value at

risk) of financial assets, and

b) It can lead to bctter pricing of various traded options,

• It has been observed in early sixties of the last century (Mandelbrot, 1963)12

that stock market volatility exhibits clustering, where periods of large returns

are followed by periods of small returns,

• Latcr popular

(1982)13 and

models of volatility clustering were developed by Engle

Bollerslev (1986) 1", The autoregressive conditional

heteroskedastic (ARCH) models (Engle, 1982) and generalized ARCH

(GARCH) models (Bollerslev, 1986) have been extensively used in capturing

volatility clusters in financial time series (Bollerslev et aI., 1992)15,

• Using data on developed markets. several empirical studies (Akgiray, 1989;

West et aI., 1993)16 havc confirmed the superiority of GARCH-type models in

volatility predictions over models such as the na'ive historical average, moving

average and exponentially weighted moving average (EWMA),

GARCH models can replicate the fat tails observed in many high frequency financial

asset return series, where large changes occur more often than a normal distribution

would imply,

• Financial markets also demonstrate that volatility is higher in a/ailing market

than it is in a rising market, This asymmetry or leverage effect was first

documented by Black (1976)17 and Christie (1982)18,

• Three popular GARCH formulations for describing this asymmetry are Power

GARCII model (Ding et aI., 1993), Threshold GARCH model (Glosten et aI.,

1993)19 and Exponential GARCI I model (Nelson, 1991 )20,

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• Empirical results also show that augmenting GARCH models with

information like market volume or number of trades may lead to modest

improvement in forecasting volatility (Brooks, 1998; Jones et ai, 1994l.

• The association between stock return volatility and trading volume was

analyzed by many researchers (Karpoff, 1987)22.

• The initial research on price-volume relation can be attributed to Osborne

(1959)23 who attempted to model stock price change as a diffusion process

with the variance dependent on the number of transactions.

• Later research on the empirical relationship between daily price volatility and

daily trading volume was based on Clarks (1973)24 mixture of distribution

hypothesis (MOH). The essence of MOH is that if the stock return follow a

random walk and if the number of steps depends positively on the number of

information events, then stock return volatility over a given period should

increase with the number of information events (e.g., trading volume) in that

period.

• [n a recent study on individual stocks in the Chinese stock market, Wang et al.

(2005)25 showed that inclusion of trading volume in the GARCH specification

reduces the persistence of the conditional variance dramatically, and the

volume effect is positive and statistically significant in all the cases for

individual stocks.

• However, another study on the Austrian stock market (Mestel et aI., 2003)26

found that the knowledge of trading volume did not improve short-run return

forecasts. Most of the studies on the relationship between return volatility and

trading volume have used volume levels.

There have been a few attempts to model and forecast stock return volatilities in

emerging markets.

[41

• For example, Gokcan (2000)27 finds that for emerging stock markets the

GARCH (I, I) model performs better in predicting volatility of time series

data. In another market specific study, Yu (2002)28 observes that the stochastic

volatility model provides better volatility measure than ARCH-type models.

• A few studies were conducted (e.g., Vanna, 199929 and 20023°; Kiran Kumar

and Mukhopadhyay", 2002; Raju and Ghosh, 200432; Pandey, 200533;

Karmakar, 200534) on modelling stock return volatility in the worlds largest

democracy, India.

• Vanna (1999)35 showed, using daily data from 1990-1998 of an Indian stock

index (Nifty), that GARCH (I, I) with generalized error distribution performs

better than the EWMA model of volatility.

• [n a later study, Pandey (2005) showed that extreme value estimators perform

better than the conditional volatility models.

• [n another recent study, Karmakar (2005) used conditional volatility models to

estimate volatility of fifty individual stocks and observed that the GARCH (1,

I) model provides reasonably good forecast.

Most recent studies on financial market volatility are placed in the context of

transmission of volatility across economies and the contagion effects of a financial

crisis.

• These include studies by Forbes and Rigobon (2002)36, Bekaert, Harvey and

Lumsdaine (2002a.b )37, Edwards (2000)38 and others.

• Rigobon (2003)39 has focussed on alternative measures of volatility in the

equity and bond markets in the period surrounding the financial crises.

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• Bekaert and Harvey (2000)4<1 analyzed equity returns in a group of emerging

markets before and after financial reforms. The empirical studies investigating

the volatility of returns have yielded mixed conclusions.

• Aggarwal, Inclan and Leal (1999)41 analyze volatility in emerging stock

markets during 1985-95. Using an less algorithm to identify the points of

sudden changes in the variance of returns they examine the nature of events

that cause large shifts in stock return volatility in these economies. Aggarwal

et al find that mostly local events cause jumps in the stock market volatility of

the emerging markets.

• Kim and Singal (1997)42 and De Santis and Imorohoroglu (1994) study the

behaviour of stock prices following the opening of a stock market to

foreigners or large foreign inflolVs. They tind that there is no systematic effect

of liberalization on stock market volatility.

• Richards (1996)43 used three different methodologies and two sets of data to

estimate volatility of emerging markets. A common claim of all these studies

is that. the proposition that liberalization increases volatility is not supported

by empirical evidence.

• However, Levine and Zervos (1995)44 suggest that volatility increases after

liberalization.

• Hamao and Mei (200 I )45 examined the impact of foreign and domestic trading

on market volatility for Japan and tind no systematic evidence that foreign

trading tends to increase market volatility more than trading by domestic

groups. The study however relates to the time period during which the foreign

portfolio investment in Japan was rather small.

Studies analyzing the behaviour of stock prices over financial cycles have been

undertaken in the recent years. They show that stock markets when liberalized tend to

become more stable.

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• Kaminsky and Schumkler (2001,2002)46,47 examine the time varying patterns

of financial cycles before and after financial liberalization in 28 countries.

Their results indicate that while liberalization may trigger financial excesses in

the short-run it also triggers changes in institutions supporting a better

functioning of financial markets. They observe a temporary volatility increase

in the years immediately following liberalization in these countries.

Studies that focussed on psychology as behavioural biases: representativeness

heuristic, conservatism, overconfidence are as under:

• Barberis and Thaler 200i8 have focussed on the role of rational and irrational

market player's interaction.

• Bikhochandani and Sharma (2001)49 discuss psychological aspects such as:

Herding imitation; Behavioural similarities following interactive

observations for a period, like

Temporary information blockage

Slower information aggregation, and

Cascading

Studies on Mechanism of market manipulation

It was first high-lighted by:

• Allen and Gale (1992)50 have contributed a seminal article, referred and cited

in several studies on manipulation of the markets. It is generally agreed that

speculators can make profits from insider trading or from the release of false

infonnation though both forms of stock-price manipulation have now been

made illegal. They argue that it is not impossible. An uninformed speculator

simply buys lind sells shares. They show that in a rational expectations

framework, where all agents maximize expected utility, it is possible for an

uninformed manipulator to make a profit, provided investors attach a positive

probability to the manipulator being an informed trader.

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• Van Bommel51 (2003) elucidates the feedback trading as conduct based on

historical data. They noted the role of overconfidence and observed temporary

information blockage when such deals are carried out.

• Koutmos and Saidi52 (200 I) observe that positive feedback trading can induce

autocorrelation in stock returns and increase volatility. They emphasize on

rational response as portfolio insurance, stop loss order, margin accounts

liquidation.

• Andergassen53 (2003) also observed that margin trading and trading in the

derivatives are key factors in margin stability: however they do trigger

volatility. They note that herd behaviour may turn out to be rational

speculation since it involves starting / riding the trend.

• Gelos and Wei5., (2002) arguc that Indian markets are prone to trend chasing

behaviour. Interestingly enough, although India constitutes one of the fastest

grOlr ing emerging llIarkets, the issue of feedback trading and its relationship

to volatility has largely been overlooked in its context. This lVas one of the

mainllIotivatingfactorsfor the present study. They also studied fund manager

behaviour internationally and compared it with transparency. Less the

transparency, more herding behaviour was their conclusion.

• John Graham, Harvey Campbell and Hai Huang (2006)55 on studying

behavioral finance, they observed that people are more willing to bet on their

own judgments when they feel skillful or knowledgeable. They investigate

whether this competence effect influences trading frequency and home bias.

They find that investors who feci competent trade more often and have more

internationally diversified portfolios. They also fll1d that male investors, and

investors with larger portfolios or more education, are more likely to perceive

themselves more competent than the female investors, and investors with

smaller portfolios or less education. The paper also contributes to

understanding the thcoretical link between overconfidence and trading

frequency. Existing thcories on trading frequency have focused on one aspect

145

of overconfidence, i,e" miscalibration, The paper offers a potential mechanism

for the better-than-average aspect of overconfidence to influence trading

frequency, In the context of their papers, overconfident investors tend to

perceive themselves to be more competent, and thus are more willing to act on

their beliefs, leading to higher trading frequency,

• David Hirshleifer, Kewei Hou, Siew Hong Teoh and Yinglei Zhang56, (2004)

emphasized the importance of profitable cash flows thus: When cumulative

net operating income (accounting value-added) outstrips cumulative free

cash flow (cash value-added), subsequent earnings growth is weak, If

investors with limited attention focus on accounting profitability, and neglect

information about cash profitability, than net operating assets, the cumulative

difference between operating income and free cash flow, measures the extent

to which reporting outcomes provoke over-optimism during the 1964-2002

sample period,

• De long and James Bredford (1990)57 showed that the issue of the relationship

between feedback trading and volatility bears an interesting connotation in

terms of financial regulation, as the dominance of feedback traders in the

market can well lead to destabilizing phenomena with prices deviating wildly

from their fundamental values when there are incomplete regulatory

environments such as corporate disclosure and information quality, De long

and other showed58 that the presence of sentiment investors in IPOs reduces

the "winners curse" problem, and further that the expected excess return to

sentiment investors may be positive or negative, depending on parameter

values, The possibility of a positive expected return suggests a rational basis

for the presence of sentiment investors in IPOs, They coined the phrase,

"winners curse" when it is based entirely on the sentiments investors herd

together and run to invest without caring for the fundamentals,

This is exactly what happened to Reliance Power IPO, Those who got the allotment

were cursed while those who did not can buy the stock in the open market at a high

discount

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• Aggarwal and W U59 explain it thus: what happens when a manipulator can trade

in the presence of other traders who seek out information about the stocks true

value. In a market without manipulators, these information seekers

unambiguously improve market efficiency by pushing prices up to the level

indicated by the informed party's information. In a market with manipulators,

the information seekers playa different role. More information seekers imply

greater competition for shares, making it easier for a manipulator to enter the

market and potentially worsening market efficiency. This suggests a strong role

for government regulation to discourage manipUlation while encouraging

greater competition for information. Their research of US markets provided

them with concluding evidence that potentially informed parties such as

corporate insiders, brokers, underwriters, large shareholders and market makers

are likely to be manipulators. More illiquid stocks are more likely to be

manipulated and manipUlation increases stock volatility. They showed that

stock prices rise throughout the manipUlation period and then fall in the post

manipUlation period. Prices and liquidity are higher when the manipulator sells

than when the manipulator buys. In addition, at the time the manipulator sells,

prices are higher when liquidity is greater and when volatility is greater. These

results suggest that stock market manipulation may have important impacts on

k ffi ' 60 mar et e lClency .

Feedback trading, as it has been now come to be called describes how the traders - and

the daily traders in particular trade. There are three main types of operations that are

applied:

• Contrarian tradillg

It simply applies that one acts contrary to the logics of the market at a given time.

This is so constant a happening that some fund managers"l manage their portfolios

going against the logical parameters. They argue on the basis of market data that

contrary to the standard belief the highest risk stocks can be expected to produce the

lowest returns and vice a versa - and label themselves as Contrarians!

147

• Werner, De Bondt and Thaler62•63

, economists themselves, applied

psychological principles64 governing behaviour psychology to the market.

They were struck by the similarity between two sets of empirical findings

pertaining to the market and individual decision making. Both of them are

characterised by overreaction. This was further confirmed by Hong and

Stein65

• This is common knowledge to the market players. What is left out by

the acadcmicians is the practical aspect of application in the market at the

appropriate time- moment. George Soros"', who has made his mark as an

enormously successful speculator, was wise enough to largely withdraw when

still way ahead of the game, cmphasized this as "". prevailing view of how

financial markets operate tends to leave the participating function out of

account66

,67. Thus, the importance of participation at the right time by the right

person is the key; there are several stock market rules that can be well argued

out: they seldom make success68 Is it not said that success goes to those that

dare and act?

Academics based on sound principlcs of behaviour psychology help those who

understand them and apply appropriately at the correct time. What does this mean

actually when one is facing the screen? Jack Welsh, a successful manager and player

of the market, called it acting Straight from the Gut69•

• ~lomentum trading

Jegadeesh and Titman 70 suggests that there is substantial evidence that indicates that

that the performance of stocks over a three- to 12-month period tend to continue in

similar direction over the subsequent three to 12 months. This phenomenon is

ascribed to momentum trading; acceleration and deceleration both take time to

unwind the steam. The strategies that exploit this phenomenon have been consistently

protitable in the United States and in most developed markets. Similarly, stocks with

high earnings momentum outperform. This is the basis of classifying certain high

III Soros made millions that he has donated in philanthropic causes too; as v.:ell as in unsuccessful attempts to defeat George Bush in his second term campaign in 2004. Participating function is well illustrated by the fact that Soros is said to have lost 80% of his earnings in 2008 crash, hoping to make some good by writing a book about it.

148

dividends yielding stock as beta stocks; they continue to maintain/improve over their

dividend records: thereby their prices too do not fluctuate widely.

• TecftlliclII1I1I1Ilysis 71

Technical analysts seek to identity price patterns and trends in financial markets and

attempt to exploit those patterns. While technicians use various methods and tools, the

study of price charts is primary.

Technicians especially search for archetypal patterns, such as the well-known head

and shoulders or double top reversal patterns, study indicators such as moving

averages, and look for forms such as lines of support, resistance, channels, and more

obscure formations such as tlags, pennants or balance days.

Technical analysts also extensively use indicators that are typically mathematical

transformations of price or volume. These indicators are used to help determine

whether an asset is trending, and if it is, its price direction. Technicians also look for

relationships betwecn price, volume and, in the case of futures, open interest.

Examples include the Relative Strength Index (RSI), and Moving Averages

Convergence Divergence (MACD). Other avenues of study include correlations

between changes in options (implied volatility) and put/call ratios with price. Other

technicians include sentiment indicators, such as Put/Call ratios and Implied Volatility

in their analysis.

Technicians seek to forecast price movements such that large gains from successful

trades exceed more numerous but smaller losing trades, producing positive returns in

the long run through proper risk control and money management.

There are several schools of technical analysis. Adherents of different schools (for

example. candlestick charting, Dow Theory, and Elliott wave theory) may ignore the

other approaches, yet many traders combine elements from more than one schoo!.

Some technical analysts use subjective judgment to decide which pattern a particular

instrument reflects at a given time, and what the interpretation of that pattern should

149

be. Some technical analysts also employ a strictly mechanical or systematic approach

to pattern identification and interpretation.

Technical analysis is frequently contrasted with jilndamental analysis, the study of

economic factors that influence prices in financial markets. Technical analysis holds

that prices already reflect all such influences before investors are aware of them,

hence the study of price action alone. Some traders use technical or fundamental

analysis exclusively, while others use both types to make trading decisions.

Thira and Enke72 presents the use of an intelligent hybrid stock trading system that

integrates neural networks, fuzzy logic, and genetic algorithms techniques to increase

the efficiency of stock trading when using a Volume Adjusted Moving Average

(VAMA), a technical indicator developed from equivolume charting. For this

research, a Neuro-Fuzzy-based Genetic Algorithm (NF-GA) system utilizing a

VAMA membership function is introduced. The results show that the intelligent

hybrid system takes advantage of the synergy among these different techniques to

intelligently generate more optimal trading decisions for the VAMA, allowing

investors to make better stock trading decisions.

Much of econom ic theory is currently presented in terms of mathematical economic

models, a set of stylized and simplitied mathematical relationships that clarify

assumptions and implications. Formal economic modelling began in the late 19th

century with the use of differential calculus to describe and predict economic

behaviour. Economics became more mathematical as a discipline throughout the first

half of the 20th century, but it was not until the Second World War that new

techniques would allow the usc of mathematical formulations in almost all of

economics. This rapid systematizing of economics alarmed critics of the discipline as

well as some esteemed economists. John Maynard Keynes, Robert Heilbroner,

Friedrich Hayek and others have criticized the broad use of mathematical models for

human behaviour, arguing that some human choices are irreducible to arbitrary

quantities or probabilities.

Yet, Behavioural economics and behavioural finance are closely related fields that

have evolved to be a separate branch of economic and financial analysis which

150

applies scientific research on human and social, cognitive and emotional factors to

better understand economic and market decisions by consumers, borrowers, investors,

and how they affect market prices, and the returns.

The field is primarily concerned with the bounds of rationality (selfishness, self­

control) of economic agents. Behavioural models typically integrate insights from

psychology with nco-classical economical theory. Psychologists have been

investigating why would people speculate - gamble - or, why would they insure,

when the chances are heavily weighted against them. We could not find any

psychology literature from India on the subject. The discipline is comparatively new

but there has been basic psychology research on the topic for long in the West. Since

the psychology of risk taking and risk aversion - insurance - is globally the same, we

have referred to a couple of basic books in psychology to understand the

fundamentals of behaviour psychology73,'4.

Jerome Bruner's'5 contribution to psychology can be compared to that of Pavlov and

Watson, classical behaviourists of earlier times. Rather than attributing behaviour to

the conditioned reflexes (herding and imitation are examples), he was a pioneer who

talked about insights guiding the bchaviour'6. Present day behaviour finance and its

further evolution to various kinds of feedback trading rely on the basic fundamentals

of both; behaviour and cognitive psychology so much so that the research protocols

are drawn basically from the work of Bruner (Appendix 3).

We found some publications of seminal importance for (i) India and (ii) global

scenario. It is pertinent to mention the Indian works here, in spite of their referencing

elsewhere too. They are:

Roy M. K. and Kannarkar M (1995).77

Based on measurement of stock market volatility for the period 1935 to 1992, they

focus on two key issues:

a) What is the average level of volatility and whether it has increased in the

subsequent period,

151

b) Whether the present trend of share price movement IS likely to impair the

development process of our economy.

Madhusudan Kannakar (2005),78

This is an extension of the paper mentioned earlier. The aim of this paper is to

estimate conditional volatility models in an effort to capture the salient features of

stock market volatility in India and evaluate the models in terms of out-of sample

forecast accuracy. The estimation of volatility is made at the macro level on two

major market indices, namely, S&P CNX Nifty and BSE Sensex. The fitted model is

then evaluated in terms of its forecasting accuracy on these two indices.

The paper relies heavily on econometric studies to arrive at a simple conclusion that

positive return stocks generate less volatility then negative return stocks, all else being

equal. The importance of the finding is great for individual investors. (This has lead to

the concept of ~ stocks". (Ex. lTC, GSK Phanna).

Harvinder Kaur (2004),79 (vide infra)

Other psychological aspects that are mentioned for the sake of completion are:

I. Gambling80

2. The Hallow EffectBl

3. Rumours as forecasts (Appendix 4)

4. Circadian Rhythm82

5. Freak factors (Appendix 5)

Behavioural finance has become the theoretical basis for technical analysis. Though

lot of mathematics is involved, Caginalp and Balenovich83, both high profile

1V B, Beta indicates the sensitivity of a stock's returns to the changes in the benchmark Index. For instance, a beta-one stock wil! change by the same percentage as the change in the benchmark. A beta lower than one, indicates a lower sensitivity to tht: benchmark. Thus, a stock with a beta 0[0.6 will fall by 6% if the index falls [0%. However, this also means that low-beta stocks are laggards during a bull run, as they are less, sensitive to market movements. So.lo\V~bcta scripts lose their charm during a bull stampede, but they provide the much-needed solace during times of sharp declines.

152

professors of mathematics initially, did not find any sound basis to principles of

mathematics. They described such analysis as philosophical! Paradoxically, finding

that there is some relevance in the patterns using a dynamical microeconomie model

which generalizes the classical theory of adjustment to include finite asset base and

trend-based investment preference, they themselves evolved a charting pattern. The

mathematically complete system of (deterministic) ordinary differential equations that

has provided a quantitative explanation of the laboratory bubbles experiments

generate a broad spectrwll of patterns that are useful as they found in their studl4.

The origins of many of these charting patterns are classified as 0) those that can be

generated by the activities of a singlc group. and (iil those that can be generated by

the presence of two or more groups with asymmetric information. Examples of (il

include the head and shoulders. double tops. rising wedge while of (ii) includes

pennants, symmetric triangles and Fibonacci charts predictions.

The system of dilTerential equations is easily generalized to Stochastics85• Application

is also made to Japanese candlestick analysis86• Chart I Page No 185

As is evident from the survey of literature discussed above, the issue of changes in

volatility of stock returns on account of stock market liberalization in emerging

markets has received considerable attention in recent years.

• However almost all the studies undertaken thus far analyze the changes in

volatility across selected emerging markets in Latin America and East Asia. In

most studies India is not included in the sample of countries for which

liberalization and volatility is analyzed.

• In addition, in most studies a narrowly defined concept of financial

liberalization is adopted.

• Research has also shown that capital market liberalisation policies too, are

likely to affect volatility. It would be of interest to policy makers that the

153

correlation between the two has been found to be positive in the c"se of some

countries.

The review of literature has provided us the findings and contribution of eminent

researchers at national and international level on volatility and its estimation. Though

prominent research with significant contribution have surfaced, it is found that the

review of most of the studies do not throw light on the causative factors of intraday

volatility. Trading days where there are huge intraday swings in the frontline index

are not addressed by any of these studies reviewed.

The increasing interests of foreign investors in the Indian market call for greater

research on various properties of this market and to examine the evidence of stylized

facts in the Indian stock market. They are:

o Stock markets are characterised by bursts of price volatility.

o Price changes are less volatile in bull markets and more volatile in bear

markets.

o Price change correlations are stronger with higher volatility, and their auto-

correlations die out quickly.

o Almost all real data have more extreme events than suspected.

o Volatility correlations decay slowly.

o Trading volumes have memory the same way that volatilities do.

o Past price changes are negatively correlated with future volatilities.

The survey of literature mentioned above raises many concerns, which are expressed

as follows:

>- Has the world's financial system become more volatile in recent times?

>- Has financial deregulation and innovation lead to an increase in financial

volatility or has it successfully permitted its redistribution away from risk

averse operators to more risk neutral market participants?

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;;.. Is the current wave of financial innovation leading to a complete set of

financial markets, which will efficiently distribute risk?

;;.. Has global financial integration led to faster transmission of volatility and risk

across national frontiers?

;;.. What are the reasons for the volatility prevailing in the Indian markets?

;;.. Can financial managers most efficiently manage risk under current

circumstances?

:;. What role the regulators ought to play in the process?

Addressing such concerns the present study seeks

• To throw an insight into the existence of a possible relationship between such

variables which capture financial and economic integration as market

capitalisation to GDP, country credit risk ratings.

• This study also tries to show that the change in volume of trade in the market

directly affects the volatility of asset returns.

This is important to investigate because

• Finally, at the level of the investor, frequent and wide stock market variations

cause uncertainty about the value of an asset and affect the confidence of the

investor. Risk averse and risk neutral investors may shy away from the market

with frequent and sharp price movements.

• An understanding of the market volatility is thus important from the regulatory

policy perspective.

With the help of theoretical background developed in first chapter and reviewing

available literature, the study will make a humble attempt to study volatility and

155

manipulation during the period 2006 - 2008 to capture the salient features of

stock market volatility in India.

The stock market was reviewed historically from 199 I -2006. The factors that affected

it were studied live from July 2006-January 2008. Several factors emerged as the

causal agents for volatility. They were broadly classified as

I. Fundamentals

ii. Feedback trading including technical analysis

III. Miscellaneous factors. The factors found to influence stock markets were

identified and titrated against the empirical data emerging from the market

performance.

The present research is an attempt to locate and analyze the factors that may be

responsible for volatility and scope of market manipulations. Following the

formation of SEBI, there are regulatory controls that aim at curbing the market

manipulation. The market players may still be able to get around to

manipulating. The study aims to investigate whether this indeed can happen.

2.2 RESEARCH METHODOLOGY

2.2.1 Introduction

The theoretical base developed in the earlier Chapters and the above point with the help

of various books and available literature has helped the researcher to understand the

research problems in a better and systematic manner. Research Methodology is a

systematic and structured procedure to arrive at the conclusion of a defined problem.

This point provides an insight to the Research Methodology with the help of which the

present study is carried out.

2.2.2 Motivation for the study

The prime motivation was to empower the small individual investor with

understanding of the market behaviour and evolve strategies for protection and growth

ofhis/her capital. It was thought that volatility and market manipulation are likely to be

156

prime factors that corrupt the market function and dupe the individual investor. And, it

was thought that a background of work in finance and banking sector along with the

market exposure as a small individual investor, are the correct prerequisites to start the

present study. The primary motive leads to the universe of the study. It is from the

focal point of a single investor to the global perspective of global financial players. The

motivation further propels to study rapidly changing scenario of financial state of

global economy that is influencing bourses and to use it to the advantage of the

individual investor.

Gut wrenching volatility ranging from 3 percent to 9 percent or indices hitting circuit

filters, literally makes the retail investor throwaway stocks sometimes at great losses.

The volatility affects the retail investor the most. Several instances are cited in the

media of investors sometimes taking drastic steps because of huge losses suffered due

to the roller coaster ride of the stock market on a day to day basis even though when we

take a year to year analysis of the bell weather indices they have delivered returns

between 30 to 40 percent. Many studies do reflect that volatility may have reduced in

the past few years after the introduction of derivative products. But to put a case to

elaborate the volatility of the Indian stock market for an investor who may have bought

1000 shares of Steel Authority of India on the 16th September 2007. That would be an

investment of Rs. 154,000/00. On the opening bell of l7'h September 2007 of the stock

market his investment would be down by 10 percent. The value would be Rs.

140,000/00. The indices had hit a circuit filter as the Finance Minister had banned

investment in Stock Market via P notes for Fils. After, clarifications were made by

both the Finance Minister and the SEBI chief the markets recovered and went to hit

new highs. But investors who would be holding on to shares are unnerved on seeing the

market value of their shares depreciating in matter of seconds and they simply book out

by incurring losses or taking minimal profits.

So we have decided to focalise and, chose a small segment to study: intraday and

day to day volatility. We have tried to align them with likely causes so as to

explain the occnrrence. That is almost uncharted in the last decade following drastic

reforms.

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Middle class invests hard earned money in the stock market that is beneficial to the

country as well as to the industry. Profitability and appreciation are natural dues for the

individual and the system owes to safeguard them. Consequences of market crashes

result in payment of heavy price socially. We have imported a model of stock market

from the West and implemented it ipso/aclo. There is a tremendous lot of transparency

and yet India cannot match the West in terms of investigative journalism. A way has to

be found that mal-practices do not occur. It is not sufficient that the miscreants can now

be brought to books. The damage is done before that.

Though the stock market indices are a measure of the Nation's wealth, as well as

individual stock prices of a particular stock determine the worth of promoter or

majority stake holder, it cannot be logical that the worth of a venture can pulsate in a

wide percentile range in a single day. Obviously the elementary principle of supply and

demand drives the price. The number of a particular stock floating in the market is

always limited: it has to be titrated against the amount of money that is targeting it. The

money supply though not limitless, can be enormous; particularly if a few players

combine to take a conce.ied action by forming a syndicate. This is the fundamental

basis of volatility.

Thus the universe of the study extends globally in geography: in terms of disciplines, it

encompasses economics, finance, all aspects of commercial dealings in the market

including numerous products on the exchanges, behaviour psychology, mathematics

and econometrics including stochastics. Obviously this is just not only formidable; it is

impossible. Thus we have to limit our study to the Indian stock market and National

Stock Exchange.

2.2.3 OBJECTIVES OF THE STUDY

The prime aim of the present study is to locate and analyse the factors that may be

possible for volatility and the scope of market manipulations with respect to the --formation of SEBI. Based on tbe above prime aim, some specific objectives are also set

for better understanding of the research problem. It is intended to study:

158

o whether the stock market behaviour has also been changed following the

liberalisation of the Economy since 1991,

o the fundamental factors driving and affecting the Indian Stock Market,

o the extent of volatility in Indian Stock Market and whether it is possible

to predict the market volatility and movement day to day,

o how liberalisation has brought about this [volatility] phenomenon In

Indian Capital Markets,

o the role of market manipulators who bring about the intraday volatility

and the extent of market manipulators,

o whether volatility and manipulations of the secondary market can be

curbed and whether it is possible to generate more effective mechanism

and policies to curb volatility and manipulations,

o the volatility in the secondary market and to identify various strategies

for the regulators and,

o Whether it is possible for an individual investor to invest as the "Smart

Money" and to propose an investment strategy for the small investors.

2.2.4 Significance of the study

This study is probably one of the few that has not only,

(i) employed a relatively simple method of volatility measurement that IS

consonant to intra-day and day to day trading

(ii) But it has also tried to link the causative factors to it so as to evolve a rational

and empirical basis. Causative factors have been identified: the study shall

guide as to what to expect when these causes are seen to be operative.

(iii) This shall provide a further platform to do such studies on intraday movements

of the stocks that may include the entire range of tick prices since such data

may soon become available because of advances in technology.

The parameters of determining volatility based on returns are impractical in

such trading: there the key lies in the hand of operator and he has to develop a

159

"gut sense" to press the right key when he sees the appropriate situation/s

emergIng.

(iv) It will be most useful to the small time traders/ investors but shall also provide

useful insights to hedge and arbitrage fund managers.

2.2.5 Scope of the study

The scope of the study is limited to the intraday fluctuations of the Nifty, the values of

which have been collected from the National Stock Exchange.

2.2.6 Time Span

Secondary data as obtained from the market performance between 1 ,t July 2006 to

30th June 2008, is tabulated with reference to volatile behaviour and attempt is made

to define likely factors that cause feedback trading.

The determination of volatility was worked out as; percentage variation and

coefticient of variation (vide infra). The market was reviewed and searched for the

causative factors from CNBC TV 18, NDTV Profit, UTV, ET Now and other business

channel telecasts, news papers (data was derived from The Economic Times, Business

Standard, Gujarat Samachar and such other dailies), bulletins from broker houses such

as Finnapolis, market analysts and chartists. This was juxtaposed with the market

performance for analysis. Collation so accrued was noted.

2.2.7 Data Collection and Methodology

The present study is descriptive type in nature. The major purpose of descriptive

research is description of the State of affairs as it exists at present87. If we take a

broader outlook, the research is described as Descriptive and non-experimental.

To measure intraday volatility we have used a lonnula which is given below:

160

d =x-x

Standard Deviation

Coefficient of variation _0 __ x 100

[Percentage variation from mean 1

d = Deviations from Mean

L = Sigma = Summation

(J = Standard Deviation

The study relies heavily on secondary data. The secondary data is collected from:

a. RBI, report on Currency and Finance.

b. Economic Surveys

c. CMIE Reports

d. RBI Annual report

e. SEBI

f. Economic Surveys of GOI

g. Various journals, report and magazines both international and national.

h. Database ofNSE

I. Database of BSE

Secondary data was gathered as it generated from day to day stock market behaviour.

It was studied as the ongoing process every day as markets behaved from day to day

from I st July 2006 to 30 June 2008. Most studies reviewed by us are retrospective;

they are conducted academ ically after some time: at times after number of years.

Probably this may be a unique instance where the market behaviour was studied and

analyzed in terms of volatility. not only from day to day but, as it occurred. It was

reviewed vis-a-vis the emerging fundamentals. Technical analysis involved the study

of several models: Japanese candle sticks, Elliot waves, Relative Strength Index,

Gann Calculator, Stochastics, Moving Average Convergence and Divergence, Money

161

flow, Relative strength. Mr.rket products, such as Metastock, Advance Get and

Japanese candle sticks were utilized to collate the technical charts.

Secondary data for market manipulations was obtained from the published reports of

the litigations against two well established market manipulators, Harshad Mehta and

Ketan Parekh. It is discussed already.

2.2,8 Plan of Data Collection

A population is the set of all the elements in a studl8.

At the onset variOus products available on the stock market and their

interconnectedness in regulating the market were studied. For sound and concise

empirical data, Nifty was chosen as a parameter.

Nifty a product of the NSE is a composite index of 50 scripts and is traded as a stock.

The included scripts are varied from time to time. The prices of the included stocks

are appropriately weighted as per their capital. This is fine tuned arrangements and

though the movements of anyone of the constituent does reflect in Nifty at that

moment, its reflection is as much as the value apportioned to the stock as index

weightage. The fluctuations in the price of the Nifty included stocks electronically get

reflected in Nifty and, therefore, Nifty reflects market price variability/volatility

appropriately. Alternatively, Nifty trading is subject to manoeuvrings by bulk buying

and selling the heavy weight stock; thus empowering the manipulators to drive the

market.

The intra-day fluctuations are registered electronically from second to second. They

show a fresh calculation of the market index every time a trade takes place for an

index component. Most of the time, more than one trade takes place in a given

second, so multiple records are found for the same second. Hence, we often see days

where there are more than 100,000 observations for Nifty. The records are registered

on the screen in correct time-sorted order, even though it appears that they all have the

same timestamp.

162

Thus electronic trading has not only made the data available almost instantaneously

but transactions are totally transparent. Anyone can have access to them and, it is also

possible to know which broking house is trading what to what extent. This can give a

lot of insight into market manipulations. Legally they stay as movements bui can well

be seen as a syndicated well orchestrated manipulation.

Constitution ofNSE and the product Nifty has given the researchers a novel and well

documented tool for study. NSE archives store and supply enormous data. Nifty is a

consolidated value stock of 50 stocks that is being traded as a stock. Thereby it is a

reliable tool for studying the market movements.

We have therefore taken movements of Nifty as the parameter for the study. BSE

figures are also mentioned concurrently.

2.2.9 Sampling method and Sample Size:

Sample is a subset of the population drawn to collect data, whereas sampling is the

process of drawing a sample from population. Sampling can be broadly divided into:

Probability, Non-Probability and Mixed Sampling. Some of the commonly used non­

probability sampling methods are: Purposive sampling, judgmental sampling and

convenience sampling. In this study we have utilised convenience sampling and a

sample of 498 trading sessions has been selected to understand the volatility and their

causes on that particular day.

2.2.10 Presentation of the data

The day to day figures ofNSE and BSE were obtained.

2.2.10.1 Sam pie Design

The samples were obtained by sitting face to face with the NSE screen on the Internet

on all the working days of the study. Two parameters were applied:

163

I. Figures for the close of the prevIous day were compared with the close of

present day to determine the percentage variation in prices from day to day.

2. Figures for the opening. high of the day, low of the day and close of the day

were obtained and co-efficient of variation was obtained for them.

• Whenever the change of I % or more of the two consecutive days closing prices

was found occurring, the day when the change was noted was considered

volatile trom day to day; and,

• Whenever the coefficient of variation was found to be more than I for the

intraday trading. that day was considered as having intra-day volatility;

• The figures were collated with the factors that cause feedback trading.

2.2.10.2 Sample Size

No of trading days studied: 498 trading days between 1st July 2006 to 30th June 2008

2.2.11 Geographical Area

The Nifty is the bench mark index of the NSE and quotes of the Nifty taken pertaining

to India.

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2.2.12 TABLE: SUMMARY OF RESEARCH DESIGN

RESEARCH TOPICITITLE Stock Market Behavior following Liberalization of

Economy (1991-2006) with focalization on Volatility,

Market Manipulation and Cyclic Nature: Causes,

effects and remedies

RESEARCH APPROACH Descriptive

RESEARCH METHOD Convenience Sampling

SAMPLE DESIGN Convenience Sampling techniques

SIZE OF THE SAMPLE 498 trading days of the National Stock Exchange

SOURCE OF DATA Collected from NSE

COLLECTION

GEOGRAPHICAL AREA India

TYPE OF DATA Secondary

TIME SPAN l;t July 2006 to 30to June 2008

SOURCES OF Research Journals, Magazines, Websites, Research

SECONDARY DATA Reports etc.

STATISTICAL Co-efticient of Variation

MEASURES

DATA DISPLAY Narrative, Text, Graphical Displays, Tables

165

2.3 Defining Volatility

2.3.1 Introduction to Volatility

The study was inspired by the huge volatility and established manipulation of the

market from 1991-2006. During this period. volatility up to 20% on a single day in

some stocks was observed on several occasions.

The volatility is well paraphrased. Volatility is defined as changeability or randomness

- fluctuations - of asset prices over a given time frame. It is measured either in terms of

percentage variations in the prices during the study period or, variations in the rates of

returns. Most oftbe published studies rely on the differences in tbe opening and closing

prices over the time frames adopted and study variations in the rates of returns to

measure volatility. For the short period like intraday or day to day, it is absurd to

study rates of return. Percentage variations in the prices have therefore been

taken as the rational criteria for voillfility.

When tbe study began. there was no bench mark for volatility on Nifty. (It is pertinent

to note that SEBI also found volatility studies of importance and introduced VIX -

volatility index from April 2008). In US markets. VIX was introduced in 1993 and is

a traded product. The VlX, introduced by the Chicago Board Options Exchange in

1993, was a weighted measure of the implied volatility of eight S&P 100 at-the­

money put and call options. Ten years later. it expanded to use options based on a

broader index, the S&P 500, which allows for a more accurate view of investor

expectations on future market volatility. VIX values greater than 30 are generally

associated with a large amount of volatility as a result of investor fear or uncertainty,

while values below 20 generally correspond to less stressful, even complacent, times

in the markets.

The VlX attempts to predict the volatility of tbe S&P 500 index over the next 30

trading days using options data from the index's 500 underlying stocks. Specifically,

the VIX is a weighted average of the implied volatilities from a large basket of

options. That basically means that it's a cumulative index of uncertainty.

166

The VIX is interpreted as a measure of volatility in a certain sense, but it more

accurately measures fear rather than volatility. That is why the VIX is important to the

average investor: it is a sign of uncertainty. So a fall ing VIX may portend better times

ahead.

The volatility index is an index which measures expectations of volatility, or

fluctuations in price, of the S & P 500 index in US. Higher values for the volatility

index indicate that investors expect the value of the S&P 500 to fluctuate wildly - up,

down, or both - in the next 30 days. VIX depends on investor's fear of a further

decline in stock prices. Even if the VIX continues to fall, that does not mean that high

volatility for stocks is finished. Investors should still hope for volatility, realized from

a recovering rally. But a low VlX - signalling reduced uncertainty - would likely

signal a coming rally, rather than stagnation in prices.

To illustrate the point, two charts are produced below: one before the beginning of the

study and the other at the time of submission. They speak for themselves.

VOLATILITY AT 20-YEAR LOVY'S

45.---n---------------------~------~1-------,

40

35

30

25

20

15

10

VIi: INDEX, wI1I1 • 50-day movmg average 'in black)

j.

I,

5+---~--~--_r--~--~--~----~--~--~--~ 1966 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Volatility soared during the Crash of 1987. It jumped when Iraq invaded Kuwait a few years later. It jumped during the Asian crisis in late 1997, and after the crash of the LTCM hedge fund in 1988. It jumped up after September lIth, 2001. Volatility in the stock market soars after major uncertainty appears

167

VIX From April 8, 2004 through April g, 2009 .. so

70 .. .. '0

"" ~

10

0 .. ~ .. .. .. .. ~ ~ .. ... " .. " 'C 1:- -' .. , 8 " ~ ~ 'C 1:- 8 ~ .. ,

" ~

TIME "J,', I

Note: The period of study finishes at the end of June 2008, Yet the chart is produced

in its entirety for the sake of relevance.

As noted before it is pertinent to note that SEBI also found volatility studies of

impol1ance and introduced VIX - volatility index from April 2008. SEBI and NSE

issued the following press release:

NSE has been in the forefront of bringing the latcst products and services to the Indian capital markets

for the benefit of the investors. In another innovation in the Indian markets, NSE is pleased to

announce the launch of India VIX, a volatility index based on the Nifty 50 Options prices.

Over the last decauc or so, there has been a paradigm shift in the Indian capital markets, The Indian

markets are no longer isolated from the global economic events. We have witnessed bouts of volatility

in our markets, some of which may have their origin in global events. The recent subprime crisis and

nev,'s of probable recession emerging from the U.S., is an example of how events which are

international, can be a cause ofvolJtility in our markcts. Events both domestic and international playa

role in affecting the volatility of stocks. lntlation ratC$, global energy prices, exchange rate fluctuations

etc. are witnessing constant changes in the recent years, These arC affecting the volatility of the

markets.

A Volatility Index renects the markets expectation of volatility over the near term. The index captures

the implied volatility embedded in option prices. Volatility is often described as the "rate and

magnitUde of changes in prices" and. in. finance ollen referred to as risk. Volatility Index is a measure,

of the amount by which an underlying Index is expected to lluetuate, in the near term, (calculated as

annualised volatility, denoted in percentage c.g.,20%) based on the order book of the underlying index

168

options. Market volatility keeps changing as new information tlows into the market. It would be

imperative for market participants to have an index designed to track market volatility.

The India VIX is a simple but useful tool in determining the overall volatility of the market. Not only is

the volatility index used as an indicator of implied volatility of the market, various tradable products,

such as futures and options contracts are available on the volatility index internationally. There is no

intention to introduce tradable products based un the India VIX in the immediate future. It is important

that the market participants get used to understanding and tracking the India VIX number and what it

signifies.

Presently. India VIX vVDuld be calculated for the entire day and made available at the end of the day, on

the website of NSE (v\"\vw.nseindia.com). Subsequently, the index would move to on-line

dissemination.

Thus, the isslies of volatility and risk have become increasingly important in recent

times to financial practitioners, market participants, regulators and researchers.

Volatility estimation is important for several reasons and for different people in the

market. Pricing of securities is supposed to be dependent on the volatility of each

asset. Mature markets/developed markets continue to provide over a long period of

time a high return with low volatility. Amongst emerging markets, except India and

China all countries exhibited low returns (sometimes negative returns) with high

volatility. India with a long history and China with a short history, both provide as

high a return as the US and UK markets could provide but the volatility in both

countries is higher. Indian markets have started becoming information-efficient.

Volatility is both the boon and bane of all traders - you cannot live with it and you

cannot really trade without it. Volatility is commonly perceived as: "choppy" markets

and wide price swings. These basic concepts are accurate, but they also lack nuance.

Volati lity is simply a measllre of the degree of price movement in a stock, futures

contract or any other markets.

Volatility most frequently refers to the standard deviation of the change in value of a

financial instrument with a specific time horizon. It is often used to quantify the risk

of the instrument over that period. Volatility is typically expressed in annualized

terms, and it may be either an absolute number or a fraction of the initial value. In

169

financial terms, volatility is the degree to which the price of a security, commodity, or

market rises or falls within a short-term period. There are several points to note about

this definition. Most importantly, the definition specifically mentions price increases

and decreases. People are usually most concerned about volatility during periods

when prices decrease or go through a "correction". In addition, most people use

volatility and risk interchangeably.

2.3.2 Volatility in stock markets

2.3.2.1 Stock volatility89

It is the relative rate at which the price of a security moves up and down. Volatility

is found by calculating the annualized standard deviation of daily change in price. If

the price of a stock moves up and down rapidly over short time periods, it has high

volatility. If the price hardly changes, it has low volatility.

A variable in option-pricing formulas shows the extent to which the return of the

underlying asset will fluctuate between now and the options expiration. Volatility, as

expressed as a percentage coefficient within option-pricing formulas, arises from

daily trading activities.

How volatility is measured will affect the value of the coefficient used.

2.3.2.2 About high and low volatility

In other words, volatility of the stock refers to the amount of uncertainty or risk

about the size of changes in a security's value. A higher volatility means that a

security's value can potentially be spread out over a larger range of values. This

means that the price of the security can change dramatically over a short time period

in either direction. Whereas a lower volatility would mean that a security's value

does not fluctuate dramatically, but changes in value at a steady pace over a period

oftime.

170

One measure of the relative volatility ofa particular stock to the market is its beta. A

beta approximates the overall volatility of security's returns against the returns of a

relevant benchmark (usually the S&P is used). For example, a stock with a beta

value of 1.1 has historically moved 110% for every 100% move in the benchmark,

based on price level. Conversely, a stock with a beta of .9 has historically moved

90% for every 100% move in the underlying index.

2.3.2.3 More about volatile markets

During volatile times, many investors get spooked and begin to question their

investment strategies. This is especially true for novice investors, who can often be

tempted to pull out of the market altogether and wait on the sidelines until it seems

safe to dive back in. The thing to realize is that market volatility is inevitable. Its the

nature of the markets to move up and down over the short term.

Trying to time the market over the short term is extremely difficult. One solution is

to maintain a long-tenn horizon and ignore the short-term fluctuations. For many

investors this is a solid strategy, but even long-term investors should know about

volatile markets and the steps that can help them weather this volatility.

2.3.2.4 Dealing with volatility

One way to deal with volatility is to avoid it altogether. This means staying invested

and not paying attention to the short-term fluctuations. One common misconception

about a buy-and-hold strategy is that holding a stock for 20 years is what will make

you money, provided you find a company with a strong balance sheet and consistent

earnings, the short-term fluctuations won't affect the long-term value of the company.

In fact, periods of volatility could be a great time to buy if you believe a company is

good for the long-term.

l7l

2.3.3 Measuring volatility of a Stock: ATR90

The concept of awrage true range. cOlllmonly referred to as ATR. is a measure of a

security's volatility. The true range of a security for any given day is the greatest of

the following three distances:

*

* *

The distance Ii'om yesterday's close to today's high

The distance from yesterday's close to today's low

The distance ("rolll today's high to today's low

The average true range is a moving average of the true ranges. In order to use ATR

effectively. an investor needs to ensure that a sufficient sample is taken. For example,

obtaining a two day ATR or ATR (2) is not sut1icicnt to provide him with a

reasonable indication of that security's normal daily movement. Whereas using at

least 10 days in the average calculation. or an ATR (10) would provide him an

inuication of that security's daily movement over the last 10 trading days (2 weeks).

The ATR is usually expressed as ATR (X) where X is the number of days used in the

calculation of the moving average. T'he number of periods selected to obtain the

average would depend on his application.

One application of ATR is that they can be uscd quite effectively for setting exits, or

stops. Using A TR for exits allows the investor to tailor the stop loss to the sccurity

you arc trading. For example. if he used a standard 10% stop. this would be a tighter

stop (i.e. closer) for some securitics than lor others. If a security moves 5% a day on

average. then a 10% stop would be tighter than for a security that only moves I y,% a

day on average. Using A TR can alleviate this situation.

To use A TR lor exits, an investor would normally use a multiple of the A TR to ensure

a sufficient gap between his exit and the security's normal price movement.

Theretore, using the ATR without any modification would have his stop too close to

the price and would not allow the security he is are trading sut1icient room to move

and behave naturally. Depending on his trading style, he would normally consider

using something in the ordcr of 2 - 3.5 multiplied by the ATR as a suitable trailing

172

exit. If he used a 2.5 II TR stop. then his trailing stop will always be 2.5 times the

ATR below the highest price the security has reached since he entered the trade.

Another application of A TR is to loosely categorise securities as blue chips, mid­

capitalisation (mid-caps) or speculative companies. This concept is called Volatility

Perccntage. The calculation that is uscd is to take the ATR over the last 20 days and

divide that by the closing price of the share and then mUltiply by 100 to determine a

volatility percentage. The result will be an indication of what percentage the share

moves on average on a daily basis. As a guide, he will discover that most mid-cap and

blue chip companies have a volatility percentage of under 4%. and anything above 5%

is nonnally speculative. A value or under 1.5% indicates that it may be a propelty

trust or a security that olTers little potential lor short to medium term gains.

Investors need to be aware of the potential risks during times of stock volatility.

Choosing to stay invested can be a great option if one is confident of his strategy. An

investor or trader needs to have a strategy to trade during volatility, be aware of how

the market conditions will affect the trade. So making a study of the volatility carries

a great importance in gaining in stock market.

2.3.4 Stock Market: Volatility and Manipulation

Volatility and manipulation are intrinsic to the market. The players are in the market

to make money and not to practice morals. However, the order in a system is also

intrinsically a must so as to set the rules by which the game is played: then only the

game can go on. Finance and the stocks are the pieces on the chessboard of the stock

market and the game is played with the help of both. Both volatility and

manipulations are likely to increase when more lenient the rules and more difficult to

enforce them and, less transparency to detect the violations. Even thereafter, legal

deterrent and punishment subsequent to fraud should have a role, though it is seen in a

very few instances only. Therefore, reflections on these aspects are better made with a

watershed: before globalisation and after it.

Roy and Karmarkar91 (as cited earlier) surveyed volatility during the years 1935-92.

The retrospective study rightly focllsed on the year-wise and decade-wise volatility

173

and focussed more on the events; the derivations are also of the nature of historical

wisdom over a span of 58 years. This period witnessed many events, big and smail

directly or indirectly, related to stock market activities. Hence, an assessment based

on an analysis of this period can be truly called as an average measure of volatility of

the stock market of those times. Following the liberalisation of economy, stock

markets underwent transformation to the global scale. This period should be

considered, therefore, separately.

2.3.5 Stock Market Volatility with emphasis on News-Centric events

Stock market performance has emerged as one of the most visible and self-titrating

parameter of the economy that pulsates from moment to moment. An average investor

has little understanding of the play in the market; he is there to park his savings for a

better yield. Inflation being part of most developing economies, a middle class investor

tries to hedge the inflation by maximizing returns through investment in equity. The

investor puts in the money with an eye on the dividends and, mainly on appreciation

that should result from the synergistic application of capital, labour and productivity.

So his savings should mUltiply. But the logic goes only thus far.

The prices of the stocks are influenced by an enormous numbers of factors with which

a very large numbers of players fiddle. While every player in the stock market is there

for making money, the only common factor for their style of play is motivation. The

players range from highly professional and skilled, to utterly illiterate ones. Both the

types can be there in the market and they may be with large capital. And then, not

every professional makes it to riches and though most fools crash. lucky ones get

away with a pot full of cash. This is because, while stock market is a good parameter

of the state of economy of a country, its volatile behaviour is more a matter of

emotions, hunches, predictions and manipUlations rather than the state of economy at

a particular day, or more precisely at a particular moment. Thus, though wealth is

generated through production by fanners and workers in the industry with wise

application of financial policies to the economy and. so the stock market transactions

should follow simple rules of inductive logic; it is seen to be functioning on

anticipation and may be manipulations. At the bottom line is the basic principle of

demand and supply that titrates the price.

174

Indian bourses have witnessed high volati lity during the period 1991-2006. It may be

thought that global political events, fluctuations in the interest rates and global inflation

would be main determinants ofthis phenomenon. For instance, on September 12,2000,

in the aftermath of terrorist attacks in the U.S., equities in Indian bourses too bore the

brunt and sentiment was affected. But this is only partly true and markets are subject to

clever manipulations to the extent that intra-day fluctuations in the range of 1000

points on the Sensex have been seen several times for unaccountable reasons.

It emerged from reflections that there seems to be a logic in the market movements

that revolves around feedback trading. A major factor was evident. It was the inflows

and outflows offunds from abroad. This is substantiated as follows:

Foreign institutional investors (FII) have come to play a major role following

liberalisation of economy since they are major investors in large cap companies.

Some of them have huge finance available at their disposal - as much as annual

revenue in Indian Government budget - and they fan and fuel the market with this

money; that is hot money. By taxing short term trading gains at the rate of 10% only,

when the trade may have occurred only a day after, the Finance Minister has willingly

created this situation.

Volatility, thus, has been studied in sparse detail; there are just a few studies over a

large time span of the Indian markets92• The period ranges from decade to decade,

year to year, month to month etc. there are enough evidences of using monthly share

price index to measure volatility and the findings show that, in the long run, both

monthly and daily share price indices reveal identical trend (Schwert, 1989)93. To ollr

knowledge, post 2000 in India, there is no study as yet which has included day to day

variations and collating thelll with the causative Jactors. Day to day variations are the

basis on which a large number oj day traders operate.

Volatility is variously researched by researchers using different parameters.

2.3.6 Calculating Volatility94

I. Measure the day-to-day price changes in a market.

175

Calculate the natural log of the ratio (Rt) of a stocks price (S) from the

current day (t) to the previolls day (t-l):

2. Calculate the average day-to-day changes over a certain period.

Add together all the changes for a given period (n) and calculate an

average for them (Rm).

3. Find out how far price vary from the average calculated in Step 2.

2.3.6.1

The historical volatility (HY) is the "average variance" from the mean (the

"standard deviation"), and is estimated as:

Parkinson's model

Parkinson's (1980)95 model, which uses intra-day highs and lows, is used for the

estimation of intra-day volatility. Following is the Parkinson model. This volatility

measure is referred to as high-low volatility. The use factor of 0.601 scales down

volatility although, statistically. it is correct. Therefore, in order to provide additional

information on intra-day (high-low) volatility it was computed K=l also.

High-low volatility conveys extreme movements and dispersion during the trade time.

A very high high-low volatility is likely to scare investors and lead sometimes to

176

panic conditions in the market place. Therefore, regulators and policy makers strive to

implement policies that smoothen the information flow and they ensure certain

measures, which in turn ensure bounded extremes with the help of circuit breakers,

exposure limit, margin etc.

2.3.6.2 Historical Volatility measure

I) Open-Open

2) Close-Close

Open-to-open volatility is very important for several of the participants. High open-to­

open volatility reveals informational asymmetry and overflow of infonnation. Any

positive or negative information that comes after the close of the market and before

the start of the next day's trading is expected to get reflected in the opening prices of

shares and on the index. Significant economic and socia-political developments

induce price movements and the extent of price movement depends on severity of

information.

2.3.6.3 Garman & Klass model

The Garman and Klass (1980)96 estimator, which uses four intra-day variation

statistics of open, high, low and close, is Llsed for the calculations. The following

model is used for this estimator.

---

J ""I" L (1/2) [loj('1 t /1l)Y-G IG',c' 1 - 1J~"1 ( ,cIDJ Where HI. Lt, Ct, and Ot, denote intra-day high, low, close and open respectively.

177

2.3.6.4 Open-low-high-c1ose volatility

It provides information on change of the prices during the day. Volatility is a function

of length of time: the longer the trading hour, the higher the expected volatility. This

is important mainly for India as the trading hours increased over a period. In the open­

out-cry system, the market was open for about two hours. Later on the number of

trading hours was extended. With the implementation of computer screen-based

trading, the number of trading hours has been further enhanced.

2.3.6.5 Measurement by Point Changes

The perception that prices move a lot - and have been moving a lot more in recent

years - is in part merely a reflection of the historically high levels of popular

indexes97• Perception of both the public and the press about stock market volatility is

generally based on point changes. The point changes invariably overestimate and

create a false impression regarding the magnitude of volatility among the investors.

This cannot give comparative data for different periods with variable levels of index.

So the point movements are thus only psychological. They do not reflect the real

change. Percentages are therefore a better parameter.

2.3.6.6 NAV

Another tool is to go by the asset value fluctuations (NA V). It is more pertinent to the

study of mutual funds. However, even there, closed-ended funds frequently trade at a

discount to net asset value.

2.3.6.7 Rate of return vis-a- vis price

The rate of return from buying and holding a stock depends upon the price at the time

of purchase, the holding period, the total dividend payments received during this

period and the price at the end of the holding period98• This is a widely accepted

concept. It is based on rates of return. That is the logarithmic difference of prices of

two successive periods. This is fine tuned in econometrics as various GARCH

models. An autoregressive conditional heteroscedasticity (ARCH), model considers

178

the variance of the current error term V to be a function of the variances of the previous

time periods error terms. ARCH relates the error variance to the square of a previous

period's error. It is employed commonly in modelling financial time series that

exhibit time-varying volatility clustering, i.e. periods of swings followed by periods of

relative calm. Such studies can turn into spinning the fine yarn even finer. Apart from

the Vanilla Model of GARCH, there are several models and each has its limitations

and advantages.

2.3.7 Volatility: Time frames

Changeability or randomness - Ouctuations - of asset prices of necessity takes into

account a particular period of time. That defines volatility. It is also defined as a

measure of the dispersion of possible returns from tinancial asset over a period of

time99• Based on the previously declared dividend. it refers to the standard deviation

f .. ~ H' d K 101) • • o returns over vanous tllne ,rames. arvlll er aur mentIOns vanous parameters

of measurement of volatility by various workers as

• Markowitz expected variance (1952)

Risk has been defined as the standard deviation of stock returns

• Officer (1973)

Moving standard deviation of 12 months of monthly data around 12 months

arithmetic means

• French, Schwert and Stanbaugh (1987)

Daily percentage stock price changes to measure the monthly standard deviation

• Schwert (1989)

Emphasized that the percentage variation is more appropriate than point variation

(followed in present study)

v Statistical errors and residuals are t\\'Q closely related and casily confused measures of "deviation of a sample from the mean": the error of a sample is the deviation of the sample from the (unobservable) population mean or actual function. while the resid1lal of a sample is the difference between the sample and the (observed) sample mean or regressed (fitted) function.

179

• Harvinder Kaurs method

The rate of return based on change in day to price divided by the price of investment

at the beginning. The formula works out as

Rt + pt - pt-I

Pt- I

Where rate of return for the period t and pt and pt-I are the beginning and closing

prices for the successive periods t I and t.

For considering the rate of return, dividend yield of the last year is taken into account

and price earnings ratios are generated. Most studies on volatility are so modelled.

They indicate the volatile stocks in particular and, therefore likely to be manipulated

and involve high risk.

The fine tuned studies as described above turn out to be futile in presence of master

manipulators (ACC script in case of Harshad Mehta and K-IO scripts of Ketan

Parekh, such unnamed instances abound in the market). The manipulators select a

few scripts. Such a study based on volatility parameters as mentioned will not reflect

market trend as a whole; particularly before the introduction of Sensex and Nifty.

Wide fluctuations from day to day prices have been witnessed in the Indian stock

markets as well as globally therefore, the present study has focussed on this.

Obviously, the yield in terms of dividend earned becomes the least important

parameter since this can account for the day of the declaration of the dividend only.

Moreover study of one or a few scripts will not reflect holistically the entire market.

The above is mentioned as a review of the methods employed by various researchers.

The present study is different. simple and novel:

1. More than one percent fluctuation in the opening and closing price - day

to day,

2. Coeflicient of Variation of more than 1 for intraday fluctuations.

180

-_._------------------------

Even one percent change in NIFTY reflects a change in market capitalization

amounting to several thousand crores. It will cover a span of fifty large cap stocks.

Therefore it is decided that percentile change of more than 1% in Nifty is an

appropriate parameter for present study.

2.3.8 Operational Definition of the Present study

The present study was taken up since this has been sparsely studied so far to our

knowledge (Shah Ash and G. Omkarnath'U', Singh, Kapoor and Babbarvi, 102).

In her magnum opus, "The Indian Financial System", 13harati Pathak103 provides a

simpler and more appropriate definition of volatility. She defined volatility as

measurement of frequency with which changes in the market price take place over a

period of time. The present study relies on this definition since the study has main

focus on day to day volatility. Titrating intra-day fluctuations vis a vis dividend yields

and returns is apparently meaningless. Triggers for intra-day fluctuations are not the

dividend yields but should be looked elsewhere. Therefore, it was decided that

volatility should be measured in terms of percentage changes in prices. Thus, the

present study relies on percentage changes and not on point changes of prices.

The present study considers a day as volatile market day when the percentage

variation for the two consecutive closing prices was more than one percent and, when

the coefficient of variation of intraday movement was more than I. The rationale

behind the criteria (defined for the first time in the present study) was like this.

Intraday volatility is utilised by day traders - punters as they are derogatively called

sometimes; though they consider themselves the real farmers of the market. The basis

of intraday trade is that the brokers charge 0.2% commission. This added to the

service charge of 0.01 % will leave a profit on day to day trade in the hands of a day

time trader if he happens to tick on the lowest buy and highest sell prices on a

particular day.

VI This study is singular in n:spcct of study or Jaily volatility and employs the percentage price fluctuation at BSE. It is futuristic too. since it aimed at predicting the movements following the year of study. However, it is rctrospecti\'e for having collected the data from records on PROWESS provided by Centre for Monitoring Indian Economy. Our study differs since we have done study on Nifty as a stock progressing from day to day.

181

That the punters in fact actually look forward to exercise such volatile moments is

what limits and, what causes volatility. This is the basis on which the charts perform.

Thus the present study has fixed that more than one percent variation in two

consecutive closing prices for the day and, coefficient of variation of more than 1

on a single day prices should be considered volatile.

It is seen frequently during the study period that there were variations ranging from

2- 8% during the trading in a single day but the index may close flat as it opened: this

will not reflect any volatility when reviewed on a day to day basis later on. In this

study, we have also taken into consideration the percentage variations during intra­

day movement of index. If the Nitty moved more than 1 % from the opening and close

of the market, we have considered that trading day as volatile day and tried to collate

the global occurrences as well as market forces at play on that particular day with

volatility. While clear indications emerged to ascribe cause and effect, it has not been

possible to do so all the times.

2.3.9 Market Manipulation

It came to be legally established that at least two mavericks, Harshad Mehta and

Ketan Parekh could manipulate the markets phenomenally. In May 2006, there were

three major falls on 15, 19 and 22nd• There were lower circuits hit on the BSE and

NSE in October 2007 and January 2008 too. This has greatly affected the investors

and had social repercussions too. The study was undertaken to analyze causation of

volatility and possibility of market manipulation. Preliminary review of the literature

showed scant data regarding market manipUlation pertaining to Indian markets. The

present study defines market manipulation as follows:

Market manipulation'04 describes a deliberate attempt to interfere with the free and

fair operation of the market and create artificial, false or misleading appearances with

respect to the price of, or market for, a security, commodity or currency. Market

manipUlation is prohibited in the United States under Section 9(a)(2)'05) of the

Securities Exchange Act of 1934, and in Australia under Section s 1041A of the

182

Corporations Act 2001. The Act defines market manipulation as transactions which

create an artificial price or maintain an artificial price for a tradable security.

Market manipulation can occur in multiple ways:

Pools

• "Agreements, olien written, among a group of traders to delegate authority to

a single manager to trade in a specific stock for a specific period of time and

then to share in the resulting profits or losses."lo6

Churning

• "When a trader places both buy and sell orders at about the same price. The

increase in activity is intended to attract additional investors, and increase the

price. 1I

Runs

• "When a group of traders create activity or rumours in order to drive the price

of a security up." An example is the Guinness share-trading fraud of the

1980s. In the US, this activity is usually referred to as painting the tape.

Ramping (the market)

• "Actions designed to artificially raise the market price of listed securities and

to give the impression of voluminous trading, in order to make a quick profit.

Wash trade

• "Selling and repurchasing the same or substantially the same security for the

purpose of generating activity and increasing the price"

Bear raid

• "Attempting to push the price of a stock down by heavy selling or short

selling."lo,

183

2.3.10 Summary

The prediction of volatility in financial markets has been of immense interest to

financials econometricians. During the past few years. the Indian Capital market has

undergone metamorphic reforms. Every segment of the market, viz primary and

secondary markets, derivatives, institutional investment and market intermediation,

has experienced the impact of these changes. Our market, today, is being recognized

as one of the most transparent, efficient and clean markets. Academicians, policy

makers, practitioners and investors, to test the extent of efficiency of the market, use

several techniques I instruments. We have outlined the operational definition of

volatility and market manipulation for this study in this point.

184

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189

78 Madhusudan Karmakar, 'Modelling Conditional Volatility of Indian Stock Markets, ' Vikalpa 30; pp40 -44. 2005

79 Harvinder Kaur, 'Stock Market Volatility in India,' The Indian Journal of

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81 Phil Rosenberg, 'The Halo Effect and eight other delusions that deceive the market managers, 'Boston 1994

82 Ernest Rossi, P.lychobiology of Gene Expression, London 200 I

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90 Measure Volatility by Arerage true range By Michael Carr, CMT, Investopedia

91 Roy M. K. and Karmarkar M. 'Stock Market Volatiltiy, Roots and Results,' Vikalpa, 20, 37-48, 1995

92 Roy, M K and Karmakar, 'M Irrational Movement of Share Prices: Evidences and

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94 Hamendra Kumar Porwal .. Volatility in Indian Stock Markets: DemystifYing the Opportzll1ily", Journal of Accounting and Finance Vol. 23; 30-33, October 2008

95 Parkinson M, "The extreme vallie method for estimating the variance of the rate of return", Journal of Business, Vol 53, pg 61-65, 1980

96 Garman M, and M. Klass, "On the estimation of security price volatilities from

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97 Schwer! W, Stock Market Volatility, Financial Analysts Journal, 46;23-34, 1990

98 0' Brien John and Srivastava Sanjay. Investments, Modern Portfolio Theory, p, 5-6 Cincinnati, 1995

99 0' Brien John and Srivastava Sanjay. Investments, Modern Portfolio Theory, Cincinnati, 1995

100 Harvinder Kaur, 'Stock Market Volatility in India,' The Indian Journal of

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101 Ash Narayan Sah and G. Omkarnath , Indian Institute of Capital Markets 9th

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]02 Singh Y.P., Kappor, S. and Babbar, S.K. 'Stock Market Voiariltiy in India, a case of Bombay Stock Exchange Sensex, ' 1. Ind. Acco. Assoc. 37;pp.6-20, 2007

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] 04 http://www.asx.com.au/supervision/participants/market_manipulation.htm

] 05 http://www.sec.gov/divisions/corpfin/34act/sect9.htm

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191