Risk Management in portfolio selection

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TITLE: Risk Management and Survilleance

COMPANY NAME : Angel Broking Ltd

Submitted by

Students Name: Mohd Asif Anis

Class: MBA (IB)

Enrolment no: A7002008092

Specialization: Finance

Under guidance of:

Industry Guides : Himanshu Singh Faculty Guide Prof. Anil

Sharma

Designation: Financial Advisor ABS, Lucknow

Organization: Angel Broking

(SUMMER INTERNSHIP REPORT IN PARTIAL FULFILLMENT OF THE AWARD OF FULL TIME

MASTERS IN BUSINESS ADMINISTRATION (2008-10)

AMITY BUSINESS SCHOOL

AMITY UNIVERSITY UTTAR PRADESH LUCKNOW

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Students Certificate

Certified that this report is prepared based on the dissertation reportundertaken by me in Angel Broking ,under the able guidance of MrAnil Sharma in partial fulfillment of the requirement for award ofdegree of master of business Administration from Amity UniversityUttar Pradesh .

Date :30/07/09

Signature Signature

Name ; Mohd Asif Anis prof Anil SharmaEnrollment :A7002008092 Faculty guide

SignatureProf R.P SinghDirector A.B.S

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Certificate of faculty Guide

This is to certify that mr Mohd Asif Anis student of MBA 3rd

semester of amity university ,Uttar Pradesh has under gone adissertation report under myb guidance. The report entitled Riskmanagement in portfolio selection has been completed by the student

to my entire satisfaction.

Date: 30/07/09

Prof Anil SharmaFaculty Guide

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D E C L A R A T I O N

I here by declare that this dissertation report submitted by me to

Amity Business school in my own and it has not been submitted to

any other university or published at any time before .

Mohd Asif Anis

Place :Lucknow

Date :30/07/09

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A C K N O W L E D G E M E N T

I am highly indebted to Mr. Ejaz Mohyi, Branch Manager,

Angel Broking Ltd., Lucknow, for his invaluable time and advice given to

me from his busy schedule in completion of this project successfully.

He not only told me about various policies of Angel Broking

Ltd. but of others also, he helped me to get to the market so that I may

easily collect data and helped me peep into the working style of Angels

employees.

Besides each and every member of the team, were very supportive and

kind to me during the whole training period.

I am also highly thankful to my industry guides Mr. Himanshu

Singh, Equity Advisor, Angel Broking Ltd and Mr. Ali Asad, Sales, Angel

Broking Ltd for their efforts in guiding me as and when I required their

guidance.

In the end, I am thankful to my parents; friends & teachers who directly

or indirectly helped me while preparing the report.

Executive Summary

The activities of large, internationally active financial institutions have grown

increasingly complex and diverse in recent years. This increasing complexity has necessarilybeen accompanied by a process of innovation in how these institutions measure and monitor

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their exposure to different kinds of risk. One set of risk management techniques that has

attracted a great deal of attention over the past several years, both among practitioners and

regulators, is "stress testing", which can be loosely defined as the examination of the

potential effects on a firms financial condition of a set of specified changes in risk factors,

corresponding to exceptional but plausible events.

This report represents the findings of a Working Group on Macro Stress Testing

established by the Committee on the Global Financial System. The group was asked to

investigate the current use of stress testing at large financial institutions, in line with the

Committees overall mandate to improve central banks understanding of institutional

developments relevant to global financial stability. The term "macro" in the groups name

indicates another element of the groups mandate, namely to explore the possibility thataggregating financial firms stress test results might produce information that is of use to

central banks, other financial regulators, and private-sector practitioners.

Members of the group interviewed risk managers at more than twenty large,

internationally active financial institutions, both in their home countries and as a group at a

meeting hosted by the Banque de France. From these interviews, the group gained a

substantial base of knowledge on the current "state of the art" in the design and

implementation of risk management and on the role of stress testing in risk management

decisions at the corporate level.

Drawing on this knowledge, the group then considered some of the issues relating to

the aggregation of the results of risk management conducted at different financial firms. The

group concluded that, under ideal circumstances, aggregate stress tests could potentially

provide useful information in a number of areas. Aggregate risk management might be used

by financial firms to help make ex ante assessments of market liquidity risk under stress

when evaluating the riskiness of a trading strategy. Central banks and financial regulators

might use them to more effectively monitor broad patterns of risk-taking and risk-

intermediation in financial markets. However, the group also noted that it is as yet unclear

whether such ideal circumstances prevail. In particular, it is unclear whether an appropriate

reporting population can be assembled, whether the stress tests currently conducted by

financial firms are compatible with one another, and whether the information obtained would

justify the reporting burden.

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This report represents practices application of risk management techniques in the

portfolio section. Risk management can be integrate by fundamental and technical method

which are used for calculation of annul return and E.P.S for the portfolio .

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I N D E X

Particular Page

Student certificate 2

Certificate of faculty Guide 3

Declaration 4

Acknowledgement 6

Executive summary 7

Chapter 1 10

THEORETICAL PRESENTATION OF THE TOPIC

Chapter 2 42

ORGANIZATIONAL PROFILE OF THE COMPANY

Chapter3 60

PRESENTATION OF DATA AND ANALYSIS

Chapter4 62

FINDINGS, CONCLUSIONS AND SUGGESTIONS

Conclusion 93

Bibliography 94

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CHAPTER I

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RISK MANAGEMENT IN PORTFOLIO

SELECTION

Objective

WHY DO I INTEND TO TAKE UP THIS STUDY ?

1. Analyse risk factors affecting different portfolios,

2.Analyse the different portfolio preference of various income

groups,

3.Select best suited portfolio for different risk appetite clients,

4.Minimize risk exposure to the present portfolio,

5.Minimize losses at the time of extreme shock,

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INTRODUCTION

Over the past fifteen years, several investors have suffered huge losses

due to extreme events.

Barings Bank failed in 1995, Long Term Capital Management collapsed

In 1998, and Enron went bankrupt in 2001. Furthermore, the terrorist attacks in

the U.S. (2001), Spain (2004), and the U.K. (2005) and the most recent

American economy collapse causing slow down (2008-09) have tremendously

affected Indian financial markets.

Extreme market moves and distress condition throughout the world have

occurred since the beginning of organized market even so, 1998 was

distinguished by the number of spectacular market stresses. Many market

participants should have learned powerful lessons. But 1998 and in 2008 and

shows that many of us are still ill prepared.

Extreme incidences that makes the world economies to slow down also

effect the india stock

Market causing the fall in index from 20 to 60 percent some of the

extreme events that affect the Indian stock market.

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Moments of crisis often present unusual but fleeting

opportunities to profit from strategic repositioning.

The following is a comprehensive list of actions that should be considered:

Buy protection or insurance for risks that can be immunized.

Restructure business, client, or product mix.

Price differently to include previously unidentified risk.

Get out of the position, market, or business.

Dont change the business but systematically monitor and manage the business

through more stress

Testing, and develop contingency plans for the shocks.

Evaluate the returns over the life cycle of the business for the total economics.

Beware of the industry herd mentality and the resulting concentration of risks.

Be careful of the .greater fool. Assumption.

Prepare for liquidity and funding issues that naturally occur in stressful market

environments by increasing

Credit/counterparty lines/ limits and funding sources, and managing liability structure for

adequate short-, medium-, and long-term funding in a crisis.

In general, a capital charge is not a useful tool for dealing with the results of stress tests. One

or more of the above solutions should provide the protection more effectively.

Taken together, the above seeks to first ensure that the firm can survive the stress

events (which include the impact on capital adequacy, reported earnings, firm liquidity,

credit ratings, and customer and investor confidence). In addition, the actions aim to preserve

enough resilience in distressed market conditions and to enable the firm to take the offensive

and move quickly, because moments of crisis often present unusual but fleeting opportunities

to profit from strategic repositioning.

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Risk Management

Since the occurrence of these incidents, the importance of risk management has been

extensively recognized by banks and securities firms when deciding the amount of risk they

are willing to take.

Moreover, bank regulators now put an emphasis on risk management practices in

attempting to reduce the fragility of financial and banking system.

Setting up of risk management cell is been practiced by various banks, brokerage

houses and other financial firms. Basic objective of this department was to eliminate risk

exposure to the firm and the clients portfolio as much as possible.

In volatile financial markets, both market participants and market regulators need

models for measuring, managing and containing risks. Market participants need risk

management models to manage the risks involved in their open positions. Market regulators

on the other hand must ensure the financial integrity of the stock exchanges and the clearing

houses by appropriate margining and risk containment systems.

The successful use of risk management models is critically dependent upon estimates

of thevolatility of underlying prices. The principal difficulty is that the volatility is not

constant overtime - if it were, it could be estimated with very high accuracy by using a

sufficiently long sample of data. Thus models of time varying volatility become very

important. Practitioners and econometricians have developed a variety of different models forthis purpose. Whatever intuitive or theoretical merits any such model may have, the ultimate

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test of its usability is how well it holds up against actual data. Empirical tests of risk

management models in the Indian stock market are therefore of great importance in the

context of the likely introduction of index futures trading in India.

There are several risk management models available, but the

most popular in them are :-

Value-at-Risk,

Stress Testing,

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Value-at-Risk,

In financial risk management, Value at Risk (VaR) is a widely used measure of

the risk of loss on a specificportfolio of financial assets. For a given portfolio,

probability and time horizon, VaR is defined as a threshold value such that the probability

that the mark-to-market loss on the portfolio over the given time horizon exceeds this value

(assuming normal markets and no trading in the portfolio) is the given probability level.

For example, if a portfolio of stocks has a one-day 5% VaR of $1 million, there is a

5% probability that the portfolio will fall in value by more than $1 million over a one day

period, assuming markets are normal and there is no trading. Informally, a loss of $1 million

or more on this portfolio is expected on 1 day in 20. A loss which exceeds the VaR threshold

is termed a VaR break.

VaR has five main uses in finance: risk management, risk measurement,

financial control, financial reporting and computing regulatory capital. VaR is sometimes

used in non-financial applications as well.

Varieties of VaR

The definition of VaR is no constructive, it specifies a property VaR must have, but

not how to compute VaR. Moreover, there is wide scope for interpretation in the

definition. This has led to two broad types of VaR, one used primarily in risk

management and the other primarily for risk measurement. The distinction is not sharp,

however, and hybrid versions are typically used in financial control,financial reporting and

computing regulatory capital.

To a risk manager, VaR is a system, not a number. The system is run periodically

(usually daily) and the published number is compared to the computed price movement in

opening positions over the time horizon. There is never any subsequent adjustment to the

published VaR, and there is no distinction between VaR breaks caused by input errors

(including Information breakdowns, fraud and rogue trading), computation errors (including

failure to produce a VaR on time) and market movements.

A frequentist claim is made, that the long-term frequency of VaR breaks will equal

the specified probability, within the limits of sampling error, and that the VaR breaks will

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be independent in time and independent of the level of VaR. This claim is validated by

aback test, a comparison of published VaRs to actual price movements. In this interpretation,

many different systems could produce VaRs with equally good back tests, but wide

disagreements on daily VaR values.

For risk measurement a number is needed, not a system. A Bayesian probability claim

is made, that given the information and beliefs at the time, the subjective probability of a

VaR break was the specified level. VaR is adjusted after the fact to correct errors in inputs

and computation, but not to incorporate information unavailable at the time of

computation. In this context, backtest has a different meaning. Rather than comparing

published VaRs to actual market movements over the period of time the system has been in

operation, VaR is retroactively computed on scrubbed data over as long a period as data areavailable and deemed relevant. The same position data and pricing models are used for

computing the VaR as determining the price movements.

Although some of the sources listed here treat only one kind of VaR as legitimate,

most of the recent ones seem to agree that risk management VaR is superior for making

short-term and tactical decisions today, while risk measurement VaR should be used for

understanding the past, and making medium term and strategic decisions for the future.

When VaR is used forfinancial control orfinancial reporting it should incorporate elements

of both. For example, if a trading deskis held to a VaR limit, that is both a risk-management

rule for deciding what risks to allow today, and an input into the risk measurement

computation of the desks risk-adjusted return at the end of the reporting period.

Risk measure and Risk metric

The term VaR is used both for a riskmeasure and a riskmetric. This sometimes

leads to confusion. Sources earlier than 1995 usually emphasize the risk measure, later

sources are more likely to emphasize the metric.

The VaR risk measure defines risk as mark-to-market loss on a fixed portfolio over a

fixed time horizon, assuming normal markets. There are many alternative risk measures in

finance. Instead of mark-to-market, which uses market prices to define loss, loss is often

defined as change in fundamental value. For example, if an institution holds a loan that

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declines in market price because interest rates go up, but has no change in cash flows or

credit quality, some systems do not recognize a loss. Or we could try to incorporate

the economic cost of things not measured in daily financial statements, such as loss of market

confidence or employee morale, impairment of brand names or lawsuits.

Rather than assuming a fixed portfolio over a fixed time horizon, some risk measures

incorporate the effect of expected trading (such as a stop loss order) and consider the

expected holding period of positions. Finally, some risk measures adjust for the possible

effects of abnormal markets, rather than excluding them from the computation.

The VaR risk metric summarizes the distribution of possible losses by a quantile, a

point with a specified probability of greater losses. Common alternative metrics are standard

deviation, mean absolute deviation, expected shortfall and downside risk.

VaR risk management

Supporters of VaR-based risk management claim the first and possibly greatest

benefit of VaR is the improvement in systems and modeling it forces on an institution. In

1997, Philippe Jorion wrote:

The greatest benefit of VAR lies in the imposition of a structured methodology for

critically thinking about risk. Institutions that go through the process of computing their VAR

are forced to confront their exposure to financial risks and to set up a proper risk

management function. Thus the process of getting to VAR may be as important as the

number itself.

Publishing a daily number, on-time and with specified statistical properties holds

every part of a trading organization to a high objective standard. Robust backup systems and

default assumptions must be implemented. Positions that are reported, modeled or priced

incorrectly stand out, as do data feeds that are inaccurate or late and systems that are too-

frequently down. Anything that affects profit and loss that is left out of other reports will

show up either in inflated VaR or excessive VaR breaks. A risk-taking institution that does

notcompute VaR might escape disaster, but an institution that cannotcompute VaR will

not.

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The second claimed benefit of VaR is that it separates risk into two regimes. Inside

the VaR limit, conventional statistical methods are reliable. Relatively short-term and

specific data can be used for analysis. Probability estimates are meaningful, because there are

enough data to test them. In a sense, there is no true risk because you have a sum of

many independent observations with a left bound on the outcome. A casino doesn't worry

about whether red or black will come up on the next roulette spin. Risk managers encourage

productive risk-taking in this regime, because there is little true cost. People tend to worry

too much about these risks, because they happen frequently, and not enough about what

might happen on the worst days.

Outside the VaR limit, all bets are off. Risk should be analyzed with stress

testing based on long-term and broad market data.[14]

Probability statements are no longermeaningful. Knowing the distribution of losses beyond the VaR point is both impossible and

useless. The risk manager should concentrate instead on making sure good plans are in place

to limit the loss if possible, and to survive the loss if not.

One specific system uses three regimes.

1. Out to three times VaR are normal occurrences. You expect periodic VaR

breaks. The loss distribution typically has fat tails, and you might get more than one

break in a short period of time. Moreover, markets may be abnormal and trading may

exacerbate losses, and you may take losses not measured in daily marks such as

lawsuits, loss of employee morale and market confidence and impairment of brand

names. So an institution that can't deal with three times VaR losses as routine events

probably won't survive long enough to put a VaR system in place.

2. Three to ten times VaR is the range forstress testing. Institutions should be

confident they have examined all the foreseeable events that will cause losses in this

range, and are prepared to survive them. These events are too rare to estimate

probabilities reliably, so risk/return calculations are useless.

3. Foreseeable events should not cause losses beyond ten times VaR. If they do

they should be hedged or insured, or the business plan should be changed to avoid

them, or VaR should be increased. It's hard to run a business if foreseeable losses are

orders of magnitude larger than very large everyday losses. It's hard to plan for these

events, because they are out of scale with daily experience. Of course there will be

unforeseeable losses more than ten times VaR, but it's pointless to anticipate them,

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you can't know much about them and it results in needless worrying. Better to hope

that the discipline of preparing for all foreseeable three-to-ten times VaR losses will

improve chances for surviving the unforeseen and larger losses that inevitably occur.

"A risk manager has two jobs: make people take more risk the 99% of the time it is safe to do

so, and survive the other 1% of the time. VaR is the border."

VaR risk measurement

The VaR risk measure is a popular way to aggregate risk across an institution.

Individual business units have risk measures such as duration for a fixed

incomeportfolio orbeta for anequitybusiness. These cannot be combined in a meaningful

way.It is also difficult to aggregate results available at different times, such as positions

marked in different time zones, or a high frequency trading desk with a business holding

relatively illiquid positions. But since every business contributes to profit and loss in

an additive fashion, and many financial businesses mark-to-market daily, it is natural to

define firm-wide risk using the distribution of possible losses at a fixed point in the future.

In risk measurement, VaR is usually reported alongside other risk metrics such

as standard deviation, expected shortfall and greeks (partial derivatives of portfolio value

with respect to market factors). VaR is a distribution-free metric, that is it does not depend on

assumptions about the probability distribution of future gains and losses. The probability

level is chosen deep enough in the left tail of the loss distribution to be relevant for risk

decisions, but not so deep as to be difficult to estimate with accuracy.

Risk measurement VaR is sometimes called parametric VaR. This usage can be

confusing, however, because it can be estimated either parametrically (for

examples, variance-covarianceVaR or delta-gamma VaR) or nonparametrically (for

examples, historical simulation VaR orresampled VaR). The inverse usage makes more

logical sense, because risk management VaR is fundamentally nonparametric, but it is

seldom referred to as nonparametric VaR.

VaR has been controversial since it moved from trading desks into the public eye in

1994. A famous 1997 debate between Nassim Taleb and Philippe Jorion set out some of themajor points of contention. Taleb claimed VaR:[20]

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1. Ignored 2,500 years of experience in favor of untested models built by non-

traders

2. Was charlatanism because it claimed to estimate the risks of rare events,

which is impossible

3. Gave false confidence

4. Would be exploited by traders

More recently David Einhorn and Aaron Brown debated VaR in Global Association of

Risk Professionals Review[12][21] Einhorn compared VaR to an airbag that works all the time,

except when you have a car accident. He further charged that VaR:

1. Led to excessive risk-taking and leverage at financial institutions

2. Focused on the manageable risks near the center of the distribution and

ignored the tails

3. Created an incentive to take excessive but remote risks

4. Was potentially catastrophic when its use creates a false sense of security

among senior executives and watchdogs.

New York Times reporterJoe Nocera wrote an extensive piece Risk Mismanagement on

January 4, 2009 discussing the role VaR played in the Financial crisis of 2007-2008. After

interviewing risk managers (including several of the ones cited above) the article suggests

that VaR was very useful to risk experts, but nevertheless exacerbated the crisis by giving

false security to bank executives and regulators. A powerful tool for professional risk

managers, VaR is portrayed as both easy to misunderstand, and dangerous when

misunderstood.

A common complaint among academics is that VaR is not subadditive. That means the

VaR of a combined portfolio can be larger than the sum of the VaRs of its components. To a

practicing risk manager this makes sense. For example, the average bank branch in the

United States is robbed about once every ten years. A single-branch bank has about 0.004%

chance of being robbed on a specific day, so the risk of robbery would not figure into one-

day 1% VaR. It would not even be within an order of magnitude of that, so it is in the range

where the institution should not worry about it, it should insure against it and take advice

from insurers on precautions. The whole point of insurance is to aggregate risks that are

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beyond individual VaR limits, and bring them into a large enough portfolio to get statistical

predictability. It does not pay for a one-branch bank to have a security expert on staff.

As institutions get more branches, the risk of a robbery on a specific day rises to within

an order of magnitude of VaR. At that point it makes sense for the institution to run internal

stress tests and analyze the risk itself. It will spend less on insurance and more on in-house

expertise. For a very large banking institution, robberies are a routine daily occurrence.

Losses are part of the daily VaR calculation, and tracked statistically rather than case-by-

case. A sizable in-house security department is in charge of prevention and control, the

general risk manager just tracks the loss like any other cost of doing business.

As portfolios or institutions get larger, specific risks change from low-probability/low-

predictability/high-impact to statistically predictable losses of low individual impact. That

means they move from the range of far outside VaR, to be insured, to near outside VaR, to be

analyzed case-by-case, to inside VaR, to be treated statistically.

Even VaR supporters generally agree there are common abuses of VaR.

1. Referring to VaR as a "worst-case" or "maximum tolerable" loss. In fact, you

expect two or three losses per year that exceed one-day 1% VaR.

2. Making VaR control or VaR reduction the central concern of risk

management. It is far more important to worry about what happens when losses

exceed VaR.

3. Assuming plausible losses will be less than some multiple, often three, of

VaR. The entire point of VaR is that losses can be extremely large, and sometimes

impossible to define, once you get beyond the VaR point. To a risk manager, VaR is

the level of losses at which you stop trying to guess what will happen next, and start

preparing for anything.

4. Reporting a VaR that has not passed abacktest. Regardless of how VaR is

computed, it should have produced the correct number of breaks (within sampling

error) in the past. A common specific violation of this is to report a VaR based on the

unverified assumption that everything follows a multivariate normal distribution

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Risk management in Indian capital market

Categorisation of stocks for imposition of margins

Stock are classifed into three categories on the basis of their liquidity and impact cost.

The Stocks which have traded at least 80% of the days for the previous six months

shall constitute the Group I and Group II.

Out of the scrips identified above, the scrips having mean impact cost of less than orequal to 1% are categorized under Group I and the scrips where the impact cost is

more than 1, are categorized under Group II.

The remaining stocks are classified into Group III.

The impact cost is calculated on the 15th of each month on a rolling basis considering

the order book snapshots of the previous six months. On the basis of the impact cost

so calculated, the scrips move from one group to another group from the 1st of the

next month.

For securities that have been listed for less than six months, the trading frequency and

the impact cost are computed using the entire trading history of the security.

Categorisation of newly listed securities

For the first month and till the time of monthly review a newly listed security is

categorised in that Group where the market capitalization of the newly listed security exceeds

or equals the market capitalization of 80% of the securities in that particular group.

Subsequently, after one month, whenever the next monthly review is carried out, the actual

trading frequency and impact cost of the security is computed, to determine the liquidity

categorization of the security.

In case any corporate action results in a change in ISIN, then the securities bearing

the new ISIN are treated as newly listed security for group categorization.

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Margins

Daily margins payable by members consists of the following:

1. Value at Risk Margin

2. Extreme Loss Margin

3. Mark to Market Margin

Daily margin, comprising of the sum of VaR margin, Extreme Loss Margin and mark to

market margin is payable.

Value at Risk Margin

All securities are classified into three groups for the purpose of VaR margin

For the securities listed in Group I, scrip wise daily volatility calculated using the

exponentially weighted moving average methodology is applied to daily returns. The

scrip wise daily VaR is 3.5 times the volatility so calculated subject to a minimum of

7.5%.

For the securities listed in Group II, the VaR margin is higher of scrip VaR (3.5

sigma) or three times the index VaR, and it is scaled up by root 3.

For the securities listed in Group III the VaR margin is equal to five times the index

VaR and scaled up by root 3.

The index VaR, for the purpose, is the higher of the daily Index VaR based on S&P CNX

NIFTY or BSE SENSEX, subject to a minimum of 5%.

NSCCL may stipulate security specific margins from time to time.

The VaR margin rate computed as mentioned above is charged on the net outstanding

position (buy value-sell value) of the respective clients on the respective securities across all

open settlements. There is no netting off of positions across different settlements. The net

position at a client level for a member is arrived at and thereafter, it is grossed across all the

clients including proprietary position to arrive at the gross open position.

For example, in case of a member, if client A has a buy position of 1000 in a security and

client B has a sell position of 1000 in the same security, the net position of the member in the

security is taken as 2000. The buy position of client A and sell position of client B in the

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same security is not netted. It is summed up to arrive at the members open position for the

purpose of margin calculation.

The VaR margin is collected on an upfront basis by adjusting against the total liquid

assets of the member at the time of trade.

The VaR margin so collected is released on completion of pay-in of the settlement.

Extreme Loss Margin

The Extreme Loss Margin for any security is higher of:

1. 5%, or

2. 1.5 times the standard deviation of daily logarithmic returns of the security price in

the last six months. This computation is done at the end of each month by taking the

price data on a rolling basis for the past six months and the resulting value is

applicable for the next month.

The Extreme Loss Margin is collected/ adjusted against the total liquid assets of the member

on a real time basis.

The Extreme Loss Margin is collected on the gross open position of the member. The

gross open position for this purpose means the gross of all net positions across all the clients

of a member including its proprietary position.

There is no netting off of positions across different settlements. The Extreme Loss

Margin collected is released on completion of pay-in of the settlement.

Mark-to-Market Margin

Mark to market loss is calculated by marking each transaction in security to the

closing price of the security at the end of trading. In case the security has not been traded on

a particular day, the latest available closing price at NSE is considered as the closing price. In

case the net outstanding position in any security is nil, the difference between the buy and

sell values shall be is considered as notional loss for the purpose of calculating the mark to

market margin payable.

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The mark to market margin (MTM) is collected from the member before the start of the

trading of the next day.

The MTM margin is collected/adjusted from/against the cash/cash equivalent

component of the liquid net worth deposited with the Exchange.

The MTM margin is collected on the gross open position of the member. The gross

open position for this purpose means the gross of all net positions across all the clients of a

member including its proprietary position. For this purpose, the position of a client is netted

across its various securities and the positions of all the clients of a member are grossed.

There is no netting off of the positions and setoff against MTM profits across two

rolling settlements i.e. T day and T+1 day. However, for computation of MTM profits/losses

for the day, netting or setoff against MTM profits is permitted.

Trade for Trade segment Surveillance segment

In case of securities in Trade for Trade Surveillance segment (TFT-S segment) the

upfront margin rates (VaR Margin + Extreme Loss Margin) applicable is 100 % and each

trade is marked to market based on the closing price of that security.

The details ofall marginsVAR, extreme loss margin and mark to market as at end of

each day are downloaded to members in their respective Extranet directory.

Margins collection from Client

Members should have a prudent system of risk management to protect themselves

from client default. Margins are likely to be an important element of such a system. The same

should be well documented and be made accessible to the clients and the Stock Exchanges.

However, the quantum of these margins and the form and mode of collection are left to the

discretion of the members.

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Margin Shortfall

In case of any shortfall in margin:

The members shall not be permitted to trade with immediate effect.

There is a penalty for margin violation

Penalty applicable for margin violation is levied on a monthly basis based on slabs as

mentioned below:

Instances of

Disablement Penalty to be levied

1st instance 0.07% per day

2nd to 5th instance

of disablement0.07% per day +Rs.5000/- per instance from 2nd to 5th instance

6th to 10th instance

of disablement

0.07% per day+ Rs. 20000 ( for 2nd to 5th instance) +Rs.10000/- per

instance from 6th to 10th instance

11th instance

onwards

0.07% per day +Rs. 70,000/- (for 2nd to 10th instance) +Rs.10000/- per

instance from 11th instance onwards. Additionally, the member will be

referred to the Disciplinary Action Committee for suitable action

Instances as mentioned above shall refer to all disablements during market hours in a

calendar month. The penal charge of 0.07% per day shall is applicable on all disablements

due to margin violation anytime during the day.

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Liquid assets

Embers are required to provide liquid assets which adequately cover various margins

and Security Deposit requirements. A member may deposit liquid assets in the form of cash,

bank guarantees, fixed deposit receipts, approved securities and any other form of collateral

as may be prescribed from time to time. The total liquid assets comprise of the cash

component and the non cash component wherein the cash component shall be at least 50% of

liquid assets.

1. Cash Component:

a. Cash

b. Bank fixed deposits (FDRs) issued by approved banks and deposited withapproved

custodiansor NSCCL.

c. Bank Guarantees (BGs) in favour of NSCCL from approved banks in the specified

format.

d. Units of money market mutual fund and Gilt funds where applicable haircut is 10%.

2. Non Cash Component:

a. Liquid (Group I) Equity Shares in demat form, as specified by NSCCL from time to

time deposited with approved custodians.

b. Mutual fund units other than those listed under cash component decided by NSCCL

from time to time deposited with approved custodians.

Margins for institutional deals

Nstitutional businesses i.e., transactions done by all institutional investors

are margined from T+1 day subsequent to confirmation of the transactions by the

custodians. For this purpose, institutional investors include

Foreign Institutional Investors registered with SEBI. (FII)

Mutual Funds registered with SEBI. (MF)

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Public Financial Institutions as defined under Section 4A of the Companies Act,

1956. (DFI)

Banks, i.e., a banking company as defined under Section 5(1)(c) of the Banking

Regulations Act, 1949. (BNK)

Insurance companies registered with IRDA. (INS)

Pension Funds registered with PFRDA (PNF)

Levy of margins:

Institutional transactions are identified by the use of the participant code at the time of

order entry.

In respect of institutional transactions confirmed by the custodians the margins are

levied on the custodians.

In respect of institutional transactions rejected/not accepted by the custodians the

margins are levied on the members who have executed the transactions.

The margins are computed and levied at a client (Custodial Participant code) level in

respect of institutional transactions and collected from the custodians/members.

Retail Professional Clearing Member:

In case of transactions which are to be settled by Retail Professional Clearing

Members (PCM), all the trades with PCM code are included in the trading members

positions till the same are confirmed by the PCM. Margins are collected from respective

trading members until confirmation of trades by PCM.

On confirmation of trades by PCM, such trades are reduced from the positions of

trading member and included in the positions of PCM. The PCMs are then liable to pay

margins on the same.

Exemption upon early pay-in of securities

In cases where early pay-in of securities is made prior to the securities pay-in, such

positions for which early pay-in (EPI) of securities is made are exempt from margins.

Members are required to provide client level early pay-in file in a specified format. The EPI

of securities is allocated to clients having net deliverable position, on a random basis unless

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specific client details are provided by the member/ custodian. However, member/ custodian

shall ensure to pass on appropriate early pay-in benefit of margin to the relevant clients.

Additionally, member/custodian can specify the clients to whom the early pay-in may be

allocated.

Exemption upon early pay-in of funds

In cases where early pay-in of funds is made prior to the funds pay-in, such positions

for which early pay-in (EPI) of funds is made are exempt from margins based on the client

details provided by the member/ custodian in the specified format . Early pay-in of funds

specified by the member/custodians for a specific client and for a settlement is allocated

against the securities in the descending order of the net buy value of outstanding position of

the client.

Cross Margin

Salient features of the cross margining available are as under:

1. Cross margining benefit is available across Cash and Derivatives segment

2. Cross margining benefit is available to all categories of market participants

3. For client/entities clearing through same clearing member in Cash and Derivatives

segments, the clearing member is required to intimate client details through a file

uploadthrough Collateral Interface for Members (CIM) to avail the benefit of Cross

margining

4. For client/entities clearing through different clearing member in Cash and Derivatives

segments they are required to enter into necessary agreements for availing cross

margining benefit.

5. For the client/entities who wish to avail cross margining benefit in respect of

positions in Index Futures and Constituent Stock Futures only, the entitys clearing

member in the Derivatives segment has to provide the details of the clients and not

the copies of the agreements. The details to be provided by the clearing members in

this regard are stipulated in the Format

1. Positions eligible for cross-margin benefit

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2. Entities/clients eligible for cross margining

3. Facility of maintaining two client accounts

4. Computation of cross margining benefit

5. Provisions in respect of default

6. Additional Reports for Cross Margin

1. Positions eligible for cross-margin benefit:

Cross margining is available across Cash and F&O segment and to all categories of market

participants. The positions of clients in both the Cash and F&O segments to the extent they

offset each other are being considered for the purpose of cross margining as per the following

priority

a. Index futures and constituent stock futures in F&O segment

b. Index futures and constituent stock positions in Cash segment

c. Stock futures in F&O segment and stock positions in Cash segment

i. In order to extend the cross margin benefit as per (a) and (b) above, the basket

of constituent stock futures/ stock positions should be a complete replica of

the index futures. NSCCL specifies the number of units of the constituent

stocks/ stock futures required in the basket to be considered as a complete

replica of the index on the website of the exchange

(www.nseindia.com/NSCCL/Notification) from time to time.

ii. The number of units are changed only in case of change in share capital of the

constituent stock due to corporate action or issue of additional share capital or

change in the constituents of the index.

iii. The positions in F&O segment for the stock futures and index futures should

be in the same expiry month to be eligible for cross margining benefit.

iv. The position in a security is considered only once for providing cross

margining benefit. E.g. Positions in Stock Futures of security A used to set-

off against index futures positions will not be considered again if there is an

off-setting positions in the security A in Cash segment.

v. Positions in option contracts are not considered for cross margining benefit.

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2. Entities/clients eligible for cross margining

The clearing member has to inform NSCCL the details of client to whom cross

margining benefit is to be provided. The cross margining benefit is available only if clearing

members provide the details of clients in such manner and within such time as specified by

NSCCL from time to time.

1. Client/entity settling through same clearing member in both Cash

and F&O segment

i. The clearing member has to ensure that the code allotted (code used while

executing client trade) to client/entity in both Cash and F&O segment is same

ii. The clearing member must inform the details of clients to whom cross

margining benefit is to be provided through a file upload facility provided in

Collateral Interface for Members (CIM).

2. Client/entity settling through different clearing member in Cash and F&O

segment

i. In case a client settles in the Cash segment through a trading member /

custodian and clears and settles through a different clearing member in F&Osegment, then they are required to enter into necessary agreements.

ii. In case where the client/entity settles through Custodian in Cash segment, then

the client/entity, custodian and the clearing member in F&O segment are

required to enter into a tri-partite agreement as per the format

iii. In case where the client/entity clears and settles through a member in Cash

segment, and a different clearing member in F&O segment, then the member

in Cash segment and the clearing member in F&O segment have to enter into

an agreement as per theformat. Further, the client/entity must enter into an

agreement with the member as per the format.

iv. The clearing member in the F&O segment must intimate to NSCCL thedetails

of the client/entity in F&O segment along-with letter from trading

member/custodian giving details of client/entity in Cash segment who wish to

avail cross margining benefit.

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3. Facility of maintaining two client accounts

As specified by SEBI, a client may maintain two accounts with their respective members to

avail cross margin benefit only. The two accounts namely arbitrage account and a non-

arbitrage account may be used for converting partially replicated portfolio into a fully

replicated portfolio by taking opposite positions in two accounts. However, for the purpose

of compliance and reporting requirements, the positions across both accounts shall be taken

together and client shall continue to have unique client code.

4. Computation of cross margining benefit

i. The computation of cross margining benefit is done at client level on an online real

time basis and provided to the trading member / clearing member / custodian, as the

case may be, who, in turn, shall pass on the benefit to the respective client.

ii. For institutional investors the positions in Cash segment are considered only after

confirmation by the custodian on T+1 basis and on confirmation by the clearing

member in F&O segment.

iii. The positions in the Cash and F&O segment are considered for cross margining only

till time the margins are levied on such positions.

iv. While reckoning the offsetting positions in the Cash segment, positions in respect of

which margin benefit has been given on account of early pay-in of securities or funds

are not considered.

v. The positions which are eligible for offset, are subject to spread margins. The spread

margins are 25% of the applicable upfront margins on the offsetting positions or such

other amount as specified by NSCCL from time to time.

vi. The difference in the margins on the total portfolio and on the portfolio excluding off-

setting positions considered for cross margining, less the spread margins is considered

as cross margining benefit. Example

5. Provisions in respect of default

In the event of default by a trading member / clearing member / custodian, as the case

may be, whose clients have availed cross margining benefit, NSCCL may:

i. Hold the positions in the cross margin account till expiry in its own name.

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ii. Liquidate the positions / collateral in either segment and use the proceeds to meet the

default obligation in the other segment.

iii. In addition to the foregoing provisions, take such other risk containment measures or

disciplinary action as it may deem fit and appropriate in this regard.

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Stress testing in risk management

Stress testing defines a scenario and uses a specific algorithm to determine the expected

impact on a portfolio's return should such a scenario occur. There are three types of

scenarios:

Extreme event: hypothesize the portfolio's return given the recurrence of a historical

event. Current positions and risk exposures are combined with the historical factor returns.

Risk factor shock: shock any factor in the chosen risk model by a user-specified

amount. The factor exposures remain unchanged, while the covariance matrix is used to

adjust the factor returns based on their correlation with the shocked factor.

External factor shock: instead of a risk factor, shock any index, macro-economic

series (e.g., oil prices), or custom series (e.g., exchange rates). Using regression analysis, new

factor returns are estimated as a result of the shock.

In an exponentially weighted stress test, historical periods more like the defined

scenario receive a more significant weighting in the predicted outcome. The defined decay

rate lets the tester manipulate the relative importance of the most similar periods. In the

standard stress test, each period is equally weighted.

Instead of doing financial projection on a "best estimate" basis, a company may do stress

testing where they look at how robust a financial instrument is in certain crashes, a form

ofscenario analysis. They may test the instrument under, for example, the following stresses:

What happens if the market crashes by more than x% this year?

What happens if interest rates go up by at least y%?

What if half the instruments in the portfolio terminate their contacts in the fifth year?

What happens if oil prices rise by 200%?

This type of analysis has become increasingly widespread, and has been taken up by

various governmental bodies (such as the FSA in the UK) as a regulatory requirement on

certain financial institutions to ensure adequate capital allocation levels to cover potential

losses incurred during extreme, but plausible, events. This emphasis on adequate, risk

adjusted determination of capital has been further enhanced by modifications to banking

regulations such as Basel II. Stress testing models typically allow not only the testing of

individual stressors, but also combinations of different events. There is also usually the

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ability to test the current exposure to a known historical scenario (such as the Russian debt

default in 1998 or9/11 attacks) to ensure the liquidity of the institution.

Stress testing reveals how well a portfolio is positioned in the event forecasts prove true.

Stress testing also lends insight into a portfolio's vulnerabilities. Though extreme events are

never certain, studying their performance implications strengthens understanding.

Stress Testing Approaches

The following comprise a fairly comprehensive set of approaches for stress testing:

Historical event analysis.

What happens if the severe market event happens again? For example,

what is the impact on your portfolio if the Reliance industries drops 23% as it did on

October 19, 1987?

Scenario analysis based on historical events.

Develop scenarios based on historical events but update them for current conditions.

Institution-specific scenario analysis.

Identify scenarios based on the institution.s portfolio, businesses,and structural risks. This

seeks to identify the vulnerabilities and the worst-case loss events specific to the firm.

Extreme standard deviation scenarios.

Identify extreme moves and construct the scenarios in which such losses can occur. For

example, what can cause a 5-, 6-, 7-, 8-, 9-, 10-standard-deviation loss event?

Extreme incremental market moves and tail risk.

This approach does not identify the scenarios but just quantifies a set of progressively severe

market moves and the resultant loss. For example,

what is the potential loss if all equity markets markets gap by plus and minus 5%, 10%,

15%, 20%,etc.?

Quantitative evaluation of distributions of tail events and extreme value theory.

Based on observed historical market events, quantify the impact of a series of tail

events to evaluate the severity of the worst case losses. This approach also evaluates the

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distribution of tail events to determine if there are any patterns that should be used for

scenario analysis.

Specific Stress Tests by Category

Stress tests can be categorized by the types of assumptions they challenge, the

types of things that can go wrong, the nature of the surprises or market moves,model

parameters, product complexity, credit, sea changes.

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PORTFOLIO SELECTION

THE PROCESS OF SELECTING a portfolio may be divided into two stages.

The first stage starts with observation and experience and ends with beliefs about the

future performances of available securities.

Thesecond stage starts with the relevant beliefs about future performances and ends

with the choice of portfolio.

This report is concerned with the second stage. We first consider the rule that the

investor does (or should) maximize discounted expected, or anticipated, returns. This rule is

rejected both as a hypothesis to explain, and as a maximum to guide investment behavior.

We next consider the rule that the investor does (or should) consider expected return a

desirable thing andvariance of returnan undesirable thing. This rule has many sound points,

both as amaxim for, and hypothesis about, investment behavior. We illustrate relations

between beliefs and choice of portfolio according to the "expected returns-variance of

returns" rule.One type of rule concerning choice of portfolio is that the investor does (or

should) maximize the discounted (or capitalized) value of future returns.

. Since the future is not known with certainty, it must be "expected" or "anticipatded7'returns which we discount. Variations of this type of rule can be suggested. Following Hicks,

we could let "anticipated" returns include an allowance for risk.

We could let the rate at which we capitalize the returns from particular securities vary

with risk.

The hypothesis (or maxim) that the investor does (or should)maximize discounted

return must be rejected. If we ignore market imperfections the foregoing rule never implies

that there is a diversified portfolio which is preferable to all non-diversified portfolios.

Diversification is both observed and sensible; a rule of behavior which doesnot imply the

superiority of diversification must be rejected both as a maxim.There is a rule which implies

both that the investor should diversify and that he should maximize expected return. The rule

states that the investor does (or should) diversify his funds among all those securities which

give maximum expected return. The law of large numbers will insure that the actual yield of

the portfolio will be almost the same as the expected yield.5 This rule is a special case of the

expected returnsvariance of returns rule (to be presented below). It assumes that there is a

portfolio which gives both maximum expected return and minimum variance, and it

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commends this portfolio to the investor.This presumption, that the law of large numbers

applies to a portfolio of securities, cannot be accepted. The returns from securities are too

intercorrelated. Diversification cannot eliminate all variance.

The portfolio with maximum expected return is not necessarily the one with

minimum variance. There is a rate at which the investor can gain expected return by taking

on variance, or reduce variance by giving

Up expected return.We saw that the expected returns or anticipated returns rule is

inadequate. Let us now consider the expected returns-variance of returns (E-V) rule. It will

be necessary to first present a few elementary concepts and results . We will then show some

implications of the E-V rule. After this we will discuss its plausibility.In our presentation we

try to avoid complicated mathematical statements and proofs. As a consequence a price is

paid in terms of rigor and generality. The chief limitations from this source are (1) we do not

derive our results analytically for the n-security case; instead, we present them geometrically

for the 3 and 4 security cases;

(2) we assume static probability beliefs. In a general presentation we must recognize that

the probability distribution of yields of the various securities is a function of time.

The writer intends to present, in the future, the general,mathematical treatment which

removes these limitations.The concepts "yield" and "risk" appear frequently in

financialwritings. Usually if the term "yield" were replaced by "expected yield" or

"expected return," and "risk" by "variance of return," little change of apparent

meaning would result.

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Integrating Risk Management with Portfolio selection

Practical implementation of risk management at the selecting or creating the portfolio

can be a great tool which can insure the better performance of portfolio even in the

extremely volatile stock market . Risk management cannot protect you from losses in

extreme shock but it can minimize the risk and surprises . Risk measures such as VAR

provide useful base-case information. Risk capital serves as a last line of defense. Stress

testing, together with daily management dialogue and decision making, provides proactive

and dynamic management of risk. No risk management can prevent losses but the best can

minimize surprises. Stress testing is a powerful means of anticipating, understanding, and

preparing for shocks and the resulting potential losses.

Modern portfolio theory aims to allocate assets by maximizing the expected risk

premium per unit of risk. In a mean variance frame work risk is defined in term of the

possible variation of expected portfolio return. The focus on standard deviation as the

appropriate measure for risk implies that the investor weigh the probability of negative return

equally against positive return. However it is a stylized fact that the distribution of many

financial return series are non-normal Furthermore there is ample evidence that agents often

treat losses and gains asymmetrically. There is a wealth of experimental evidence for loss

aversion. The choice therefore of mean variance efficient portfolios is likely to give rise to an

inefficient strategy for optimizing expected returns for financial assets whilst minimizing

risk. It would therefore be more desirable to focus on a measure for risk that is able to

incorporate any non-normality in the return distributions of financial assets. Indeed risk

measures such as semi-variance were originally constructed in order to measure the negative

tail of the distribution separately.

Typically mainstream finance rests on the assumption of normality, so that a move

away from the assumption of normally distributed returns is not particularly favored; one

drawback often stated is the loss in the possibility of moving between discrete and

continuous time frameworks. However it is precisely this simplifying approach, whereby any

deviations from the square root of time rule are ignored, which needs to be incorporated into

current finance theory. The ability to focus on additional moments in the return distribution

enables additional risk factors to be included into the optimal portfolio selection

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CHAPTER II

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COMPANY PROFILE

Angel Broking ltd. is one of India's leading financial institutions, offering

complete financial solutions that encompass every sphere of life. From stock

broking, to mutual funds, to life insurance, to investment, the group caters to the

financial needs of individuals and corporate.

Angel group is leading Retail Broking service provider in the country.

The group has emerged as one of the top 3 retail stock broking house offering a

gamut of retail centric services like Research, investment Advisory ,wealth

Management Services ,E-broking & Commodities to the individual investor.

Angel has a wide and efficient network of 21 regional hubs,150 branchesand 2200+ business associates in 115 cities all over the country services more

than 1.9 lac individual investors. The group is promoted by Mr. Dinesh

thakar, who started these enterprises as a sub broker in 1987.

Angel Broking's tryst with excellence in customer relations began in

1987. Today, Angel has emerged as one of the most respected Stock-Broking

and Wealth Management Companies in India. With its unique retail-focusedstock trading business model, Angel is committed to providing Real Value for

Money to all itsclients.

The Angel Group is a member of the Bombay Stock Exchange (BSE),

National Stock Exchange (NSE) and the two leading Commodity Exchanges in

the country: NCDEX & MCX. Angel is also registered as a Depository

Participant with CDSL.

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Research Strength at Angel

Angel Broking is one of the leading retail brokerage houses with a

professional and qualified research team. We deploy state of the art research

metrics and international news services like Bloomberg/ Reuters etc. to remain

in touch with global / domestic developments.

Angels research has a proven track record of over 5 years. Emphasis on

providing best investment value for money to the retail client is the core

philosophy at angel .angel principally focuses on the individual investor

community and has an investment / advisory desk to give first hand information

/ guidance to them. Angels research and advisory team comprises of 80+

professionals working continuously to discover potential multi- bagger stocks

for you.

Angel broking Research center the special research cell where some of

Indias finest financial analyst bring you intensive research reports on how the

stock market is faring. When is the right time to invest, when to execute your

order and more. Depending on what kink of investor you are, we bring you

fundamental or basis research and technical research. As an investor with angel

broking ltd., you get access to these research reports exclusively. You get

access to the following reports.

Intraday Calls

These calls are provided according to changing market situations. Be it

news, momentum or technical perspectives be updated with what are experts

advise you to do during the market hours.

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Daily Technical view

A technical view summarizing the previous day movement and what is

expected to happen on the current day. This report will also provide you withtechnical calls for trading along with various supports and resistances of chosen

stocks.

Sectorial Reports

Deciding which sector to invest in, our super sector report can guide you.

Know details including the effect of government policies and regulations and

estimates about how the sector is expected to behave.

M connect

At last but not the least you can get these expert tips and

recommendations as SMS on to your mobile phone.

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Angel broking limited

The journey so far (milestones)

February, 2008 Crossed the 400,000 mark in unique trading accounts

November, 2007 Received "Major Volume Driver" award for FY07

March, 2007 Crossed the 200,000 mark in unique trading accounts

December, 2006 Crossed the 2,500 mark in terms of business associates.

October, 2006 Received "Major Volume Driver" award for FY06

September, 2006 Commenced Mutual Fund and IPO distribution business

July, 2006 Formally launched the PMS function

March, 2006 Crossed the 100,000 mark in unique trading accounts

October, 2005 Received the prestigious "Major Volume Driver" award for FY05

September, 2004 Launch of Online Trading Platform

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Services of Angel Broking Ltd.

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Competitors

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KARVY

KARVY, is a premier integrated financial services provider, and ranked

among the top five in the country in all its business segments, services over 16

million individual investors in various capacities, and provides investor services

to over 300 corporate, comprising the who is who of Corporate India. KARVY

covers the entire spectrum of financial services such as Stock broking,

Depository Participants, Distribution of financial products - mutual funds,

bonds, fixed deposit, equities, Insurance Broking, Commodities Broking,

Personal Finance Advisory Services, Merchant Banking & Corporate Finance,

placement of equity, IPOs, among others. Karvy has a professional management

team and ranks among the best in technology, operations and research of

various industrial segments.

The birth of Karvy was on a modest scale in 1981. It began with the

vision and enterprise of a small group of practicing Chartered Accountants who

founded the flagship company Karvy Consultants Limited. We started with

consulting and financial accounting automation, and carved inroads into the

field of registry and share accounting by 1985. Since then, we have utilized our

experience and superlative expertise to go from strength to strengthto better

our services, to provide new ones, to innovate, diversify and in the process,

evolved Karvy as one of Indias premier integrated financial service enterprise.

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ICICI direct .com

ICICI Bank Demat Services boasts of an ever-growing customer base of

over 11.5 lacs account holders. In our continuous endeavor to offer best of the

class services to our customers we offer the following features:

E-Instructions: You can transfer securities 24 hours a day, 7 days a week

through Internet & Interactive Voice Response (IVR) at a lower cost. Now with

"Speak to transfer", you can also transfer or pledge instructions through ourcustomer care officer.

Consolidation Demat Account: Dematerialise your physical shares in

various holding patterns and consolidate all such scattered holdings into your

primary demat account at reduced cost.

Digitally Signed Statement: Receive your account statement and bill by

email.

Corporate Benefit Tracking: Track your dividend, interest, bonus through

your account statement.

Mobile Request: Access your demat account by sending SMS to enquire

about Holdings, Transactions, Bill & ISIN details.

Mobile Alerts: Receive SMS alerts for all debits/credits as well as for anyrequest which cannot be processed.

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