Performance of Indian Mutual Funds Using VaR

8/3/2019 Performance of Indian Mutual Funds Using VaR

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Analysis of performance of Indian mutual funds

using VaR as a measure of Risk

Submitted By :

Akshaya Pandey

Y9125002


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Acknowledgements

I am sincerely thankful to Prof. B.V. Phani who guided me through the study. His Insightful

questions, suggestions on different facets of my study were extremely helpful in coming up

with the report. I would also like to thank my colleague Mr. Rajeev Ranjan for helping me

throughout the study, discussing various issues and giving their suggestions at various stages

of the study. Last but not the least; I would like to extend my sense of gratitude to IME

Department, IIT Kanpur for providing me resources and opportunity to work on this project.


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Table of ContentsExecutive Summary ............................................................................................................................. 1

Methodology ....................................................................................................................................... 2

Introduction ........................................................................................................................................ 4

What are mutual funds? ................................................................................................................. 4

Types of Mutual Funds .................................................................................................................... 5

Performance Measurement of Mutual Funds in India ....................................................................... 6

The Treynor Measure ...................................................................................................................... 7

The Sharpe Measure ....................................................................................................................... 7

What is Value at Risk ........................................................................................................................... 8

Hypothesis........................................................................................................................................... 9

Model to Estimate VaR ..................................................................................................................... 10

Assumptions .................................................................................................................................. 10

Model ............................................................................................................................................ 10

Maximum Likelihood Approach .................................................................................................... 11

Testing of the Model ..................................................................................................................... 11

Limitations ........................................................................................................................................ 13

Results ............................................................................................................................................... 14

Appendix I : Large Cap Funds ............................................................................................................ 16

Appendix II : Mid Cap Funds ............................................................................................................. 17

Appendix III: Closed Ended Funds ..................................................................................................... 18

Appendix IV: Dividend Yield Funds ................................................................................................... 19

References ........................................................................................................................................ 20


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List of Figures

Figure 1: Mutual Funds Operations .................................................................................................................. 4

Figure 2: Clasification of Mutual funds ............................................................................................................. 5


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List of Tables

Table 1: Large Cap Funds VaR ........................................................................................................................ 16

Table 2: Mid Cap Funds VaR ........................................................................................................................... 17

Table 3: Closed Ended Funds VaR................................................................................................................... 18Table 4 : Dividend Yield Funds VaR ................................................................................................................ 19


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Executive Summary

Since the 1990s when the mutual fund space opened up to the private sector, the industry

has traversed a long path, adapting itself continuously, to the changes that have come

along. Growth in Assets Under Management (AUM) experienced has been unprecedented,

growing at a CAGR of 28% over the last four years, slowing down only over the last twoyears, as a fallout of the global economic slowdown and financial crisis. average assets

under management indicated vibrant growth levels posting a y-o-y growth of 47% in 2009-

10, and the total AUM stood at Rs 613,979 crore, as of March 31,2010. Aggregate funds

mobilized during the year also grew 84%, supplemented by around 174 new schemes

launched during April 2009 to March 2010. The investor base has also steadily expanded

and between November 2009 to March 2010, there was an addition of 60,834 investors.[1]

In todays volatile market environment, there is one major question in the investors mind, if

I invest in the mutual funds, what is maximum downside risk. This study aims to conductsan empirical study in order to analyze the weekly downside risks posed by the mutual funds.

It uses Value at Risk (VaR) as a measure in order to analyze the risks. This measure is

currently not being used by the mutual fund industry in India or elsewhere.

This is an empirical study of equity funds. For our purpose, we have considered four

categories, ie. Open ended Large Cap funds, Mid Cap funds, Closed Ended Funds, and

Dividend Yield Funds. The benchmarks that have been chosen are CNX Nifty and CNX Mid

Cap.


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Methodology

The classification of funds has been taken as per the valueresearchonlinemethodology

1

. The funds were categorized into four categories:o Large Cap Fundso Mid Cap Fundso Closed Ended Fundso Dividend Yield Funds

Due to the lack of sufficient historical data on closed ended funds in India (except forMorgan Stanley Growth Funds) for the time frame covered in our analysis, the Tax

saver funds(ELSS) are taken as proxy for the closed ended funds because of their 3-

years lock in Period.

In choosing a time span of historical data used for volatility, the first considerationshould be whether major market events from several years ago should be

influencing forecasts today. For example, including extreme events of the magnitude

of early months of 2008 or a 9/11 in a volatility estimating model will have the effect

of raising the long term volatility forecasts by several percentages. Thus, the period

from 1-Jan 2005 till 31-Dec-2007 was chosen for the purpose of the study.

Data for 2 years period, i.e., Periods 1-Jan-2005 to 31-Dec-2006 was taken toformulate the model and estimate the variance.

Data for Period from 1-Jan-2007 till 31-dec-2007 was taken to back test & verify themodel

When a mutual fund declares a dividend, the NAV of the fund decreases by theamount of dividend payout post the record date for dividend payout. In order to

prevent distortion of NAV due to impact of dividend declaration on the NAV only the

NAVs for Growth option was considered for purpose of our analysis.

The funds with AUM of over Rs 200 crores were chosen for our analysis. Thefollowing funds have been selected for our analysis:

o Diversified Large Cap Funds Fidelity Equity Fund Kotak 50 HSBC Equity Franklin India Blue Chip Fund UTI Equity Fund Taurus Starshare HDFC Equity Fund

o Diversified Mid Cap Funds1

Every fund company has its own method of differentiating between the categories. Hence, we havetaken the categorization done by valueresearchonline. For details refer.http://www.valueresearchonline.com/story/h2_storyView.asp?str=8548


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HSBC Mid Cap Fund Sundaram Select Mid Cap Reliance Growth Fund Kotak Mid Cap Fund ICICI Prudential Discovery Fund Tata Equity Opportunity Fund Sahara Mid Cap

o Closed Ended Funds Morgan Stanley Growth Fund Reliance Tax Saver Franklin India Taxshield Kotak Tax Saver Escorts Tax Plan Sundaram Taxsaver 98

o Dividend Yield Funds Tata Dividend Yield Fund Principal Dividend Yield Fund

The benchmark indices considered werea. Large Cap funds: CNX Niftyb. Mid Cap Funds: CNX Mid Cap

Returns(ui) for Week i are Calculated using Ui = Ln((Nav)t/(Nav)t-1 Standard Deviations are calculated using EMWA & Moving Average Model. EMWA:

Variance=(Variance)t-1* + (1-)*Ut-12

From the historical data, using the Maximum likelihood approach (determining theparameter values which maximize the chance for event to occur), the value for was

estimated with the help of Excel solver.

Moving Average variance was calculated for previous 8 weeks using formula = ( + . . +)/

Finally, the Variance was used to estimate the future VaR for 1 week. Back testing was done by comparing the estimated VaR at 95% confidence level and

99% confidence level with the actual fluctuations in the mutual fund NaV.

Since, the returns from mutual funds for any two periods are independent of eachother; Bernoullis trial approach was used to estimate the expected failure count and

results were compared with the actual frequency of failures.

On successful validation of the model, the future estimate for 1 week VaR at 95%confidence level is calculated.


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Introduction

What are mutual funds?

A Mutual Fund is a trust that pools the savings of a number of investors who share a

common financial goal. It is essentially a diversified portfolio of financial instruments - thesecould be equities, debentures / bonds or money market instruments. The corpus of the fund

is then deployed by the Asset Management Company (AMC)in investment alternatives that

help to meet predefined investment objectives. The income earned through these

investments and the capital appreciation realised are shared by its unit holders in

proportion to the number of units owned by them.[1]

Figure 1: Mutual Funds Operations

The key reason advantage for an investor to invest through the mutual fund routes is that a

typical individual is unlikely to have the knowledge, skills, inclination and time to keep track

of events, understand their implications and act speedily. An individual also finds it difficult

to keep track of ownership of his assets, investments, brokerage dues and bank transactions

etc. A mutual fund is the answer to all these situations. It appoints professionally qualified

and experienced personnel who manage each of these functions on a full time basis. The

large pool of money collected in the fund allows it to hire such staff at a very low cost to

each investor. In effect, the mutual fund vehicle exploits economies of scale in all three

areas - research, investments and transaction processing.[1]

Investors

AMC

Securities

(Stocks, Bonds etc)

Returns

Pool their money

Invests the money.Generates

Passed back to

investors.


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Types of Mutual Funds

Mutual Fund schemes can be predominantly classified as per the following diagram.[1]

Figure 2: Clasification of Mutual funds

By Structure:

Open-ended Funds: The open-end schemes are available for subscription all through the

year. These do not have a fixed maturity. Investors can conveniently buy and sell units at

Net Asset Value ("NAV") related prices. The key feature of open-end schemes is liquidity.

Closed-ended Funds: The closed-end schemes have a stipulated maturity period which

generally ranges from 3 to 15 years. The schemes are open for subscription only during a

specified period. Investors can invest in the scheme at the time of the initial public issue andthereafter they can buy or sell the units of the scheme on the stock exchanges where they

are listed. In order to provide an exit route to the investors, some close-ended funds give an

option of selling back the units to the Mutual Fund through periodic repurchase at NAV

related prices.

By Investment Objective:

Equity/Growth Funds

The aim of growth funds is to provide capital appreciation over the medium to long- term.Such schemes normally invest a majority of their corpus in equities. It has been proven that

Structure

Open

Ended

Closed

Ended

Investment Objective

Equity

Balanced

Debt

Special Scheme

ELSS

Sector

Funds


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returns from stocks, have outperformed most other kind of investments held over the long

term. Growth schemes are ideal for investors having a long-term outlook seeking growth

over a period of time.

Debt Funds: The aim of income funds is to provide regular and steady income to investors.

Such schemes generally invest in fixed income securities such as bonds, corporate

debentures and Government securities. Income Funds are ideal for capital stability and

regular income.

Balanced Funds : The aim of balanced funds is to provide both growth and regular income.

Such schemes periodically distribute a part of their earning and invest both in equities and

fixed income securities in the proportion indicated in their offer documents. In a rising stock

market, the NAV of these schemes may not normally keep pace, or fall equally when the

market falls. These are ideal for investors looking for a combination of income and

moderate growth.

Special Schemes

Tax Saving Schemes(ELSS): These schemes offer tax rebates to the investors under specific

provisions of the Indian Income Tax laws as the Government offers tax incentives for

investment in specified avenues. The investments in these funds are subject to a lock in

period of 3 years,

Sectoral Schemes: Sectoral Funds are those, which invest exclusively in a specified industry

or a group of industries such as Infrastructure Funds, IT funds, FMCG funds etc.

Performance Measurement of Mutual Funds in India

The value of a mutual fund is determined by its NAV( Net Asset Value). Net Asset Value is

the market value of the assets of the scheme minus its liabilities. It is the net asset value of

the scheme divided by the number of units outstanding on the Valuation Date. Thus, if there

is an appreciation in the NAV of a mutual fund scheme, it is generating positive returns for

the investors. In case, there is a decline in the NAV of a mutual fund, there is a negativereturn for the investor.

Currently, Indian Mutual funds industry focuses on parameters such as Treynor measure,

Sharpe ratio, benchmarking the mutual funds performance against some index or other

mutual funds etc as their performance evaluation criteria.[2]

Even some of the entities such as ICRA are involved in ranking and rating of mutual funds.

The parameters used by ICRA for evaluations of mutual funds performance and ranking are

given below[3]:

1. Risk-Adjusted Return


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2. Portfolio Concentration Characteristics3. Liquidity4. Corpus Size5. Average Maturity6. Portfolio Turnover Ratio

In order to determine the risk-adjusted return for mutual funds, the following are the most

common measures used:

The Treynor Measure

Treynor Index is a ratio of return generated by the fund over and above risk free rate of

return during a given period and systematic risk associated with its (beta). Symbolically, it

can be represented as:

Treynor's Index (Ti) = (Ri - Rf)/Bi.

Where,

Ri represents return on fund,

Rfis risk free rate of return

Bi is beta of the fund.

All risk-averse investors would like to maximize this value. While a high and positive

Treynor's Index shows a superior risk-adjusted performance of a fund, a low and negative

Treynor's Index is an indication of unfavourable performance.[3]

The Sharpe Measure

Sharpe Ratio, which is a ratio of returns generated by the fund over and above risk free rate

of return and the total risk (volatility), associated with it. According to Sharpe, it is the total

risk of the fund that the investors are concerned about. So, the model evaluates funds on

the basis of reward per unit of total risk. Symbolically, it can be written as:

Sharpe Index (Si) = (Ri - Rf)/Si

Where,

Ri represents return on fund,

Rfis risk free rate of return

Si is standard deviation of the fund.


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While a high and positive Sharpe Ratio shows a superior risk-adjusted performance of a

fund, a low and negative Sharpe Ratio is an indication of unfavourable performance.[3]

Thus, the key focus is on determining the risk adjusted returns generated by the mutual

funds. In case funds generates extra return for the same amount of risk, or outperforms its

benchmark, category of funds, it is considered a better fund and rated higher. However,

they fail to focus on the max downside risk or rather max loss an investor of mutual funds

may incur over certain period of time. Thus, there is a need to include an additional measure

which is an indicator of the maximum loss/downside risk which an investor in the mutual

fund can be expected to face.

What is Value at Risk

Value at Risk (VaR) is defined as the expected maximum loss (or worst loss) over a target

time horizon within a given confidence interval. VaR is the loss over next N days which will

be exceeded only (100-X) % of the times. [4]

VaR is a function of two parameters:

Time horizon (N days):N-day VaR = 1 day Var * (N)

Confidence level (X %)Thus, VaR may be defined using the formula

Var(X%, t) = +

Where,

= estimate of Mean return

= estimate of standard deviation.

It is a useful measure of the worst case scenario for evaluating any investment opportunity

and has number of application in number of areas. Essentially, it provides the answer to

question, How bad the things can get?.[4]

Thus, VaR analysis is important to determine the maximum downside risk for the mutual

funds. For the funds, this measure should be calculated and compared with the benchmark

such as BSE Sensex, Nifty etc to evaluate the efficacy of the risk management practices of

the mutual funds industry in India.

The study uses two models to estimate the volatility of mutual funds, the moving average

and exponentially weighted moving average model. This volatility estimate is later used to

estimate the VaR for the mutual funds. Both the models rely upon the historical data to

estimate the future volatility and return, but the key difference is in the way they use thepast data. Once, volatility is estimated, it is used to determine the VaR for the mutual fund.


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Similar procedure is followed to estimate the VaR for the benchmarks. Finally, a comparison

of the two is done to arrive at the conclusion regarding the downside risk of the two.

HypothesisIf one goes through various articles and literature on the mutual funds industry, one

frequently comes across the following claims:

In mutual funds, stocks & portfolio selection is done by a dedicated team of expertshaving experience and expertise to manage funds, they are less risky way of

investing in markets. Also, by following an active investment strategy unlike the

benchmarks, these are able to outperform their benchmarks, as well as have a lower

downside risks.[5]

Large caps are well researched and information is more easily available. Mid caps onthe other hand have less transparent management and accounting practices. Also,

large caps are much more liquid than the small caps. Thus, large caps funds are safer

than mid-small caps funds and exhibit a lower downside risk.[6]

In case of open ended funds, due to constant pressure on the fund manager forredemptions, he has to maintain liquidity, thus have some idle cash. The redemption

pressure is generally higher during market decline and lower during bullish phase.

Thus the cash exposure depends on the market conditions. Thus, it provides a

downside cushion during market crashes due to higher cash levels. However, a

manager of closed ended fund does not face constant redemption pressures, thus

remains invested throughout the bull and bear phases of the market cycles.

Consequently, the idle cash available with him are lower than that of the open

ended fund. Due, to these the closed ended funds exhibit a greater volatility and

higher downside risks as compared to the open ended funds. Though, in longer time

frame, they generally outperform their open ended counterparts.[7]

In case of closed ended funds, it is generally seen that fund manager invests with alonger term perspective, thus, the portfolio is generally biased towards small/mid

cap stocks in the hope of generating greater returns in the longer run. However, it

can also results in a higher volatility, thus, higher downside risks to the funds.[7]

Dividend Yield funds invest in companies having higher dividend yields, which isgenerally due to better and more stable cash flows. These companies are less

volatile then the generally market. Thus funds investing in these companies are a

good defensive bet in market downturns and exhibit lower downside risks.[8]

Thus, we have formulated the following hypothesis which will be tested using our model.

H1: Mutual funds are able to manage downside risks better than their benchmarks

indices.


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H2: Large caps funds exhibit lower downside risks as compared to mid-cap funds.

H3: Closed ended funds exhibit higher downside risks as compared to open ended Funds.

H4: Dividend yield funds exhibit lower downside risk as compared to other diversified

equity funds.

Model to Estimate VaR

Assumptions

a. The returns from a mutual fund portfolio follow a normal distribution with a meanreturn of 0.

b. The return (ui) for any two days/period is independent of each other. That is, thereturn on Day 1 is independent of the return on Day 0.

c.

Historical data is a good predictor of the future.

Model

The study uses the following models to estimate the volatility of the returns using the

historical data.

Two models for volatility estimates have been considered:

a. Moving Averages: It gives equal weight to all the past data and is calculated on arolling basis.

n2

= ( 12

+ 22

+.(n-1)2

)/(n-1)

Since, mean returns are zero (from assumption), it translates into,

Where :

M number of days

r(i) = returns for ith day

For the purpose of this study, a rolling return for the past 8 weeks was used.

b. Exponentially Weighted Moving Average: It gives more weight age to recent data ascompared to the older data. Thus, is more responsive to the changes to volatility.

The parameter lambda (also called decay factor) isan indicator of the

responsiveness of the model to the recent changes. A low value of indicates that a

great deal of weight is given to the recent observations, i.e. Un-1. whereas a high

value of indicates that the volatility estimate responds more slowly to the recent

data. As per Risk Metrics document of JP Morgan, was estimated to be around


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.94.[10] However, for the purpose of our study, has been calculated using

Maximum likelihood Approach using the help of Excel solver.

n2=()*n-1

2+ (1-)*Un-1

2

Once, the volatility estimates have been estimate, the confidence level needs to be

determined. From the empirical studies, 95% confidence level is considered

appropriate for most cases [9].

Finally, the 1 week - VaR for 95% confidence level is calculated using:

VaR (X%, t) = +

Where,

= mean return = 0 (due to assumption 1)

= standard deviation.

Maximum Likelihood ApproachIt is a frequently used method used to estimate the parameters for the model defined above, in our

case lambda( ). It aims to maximize the likelihood/chance of the data occurring again.

We have a set of m observations for X ( our Mutual Fund NAV).Let us assume that the

observations are u1, u2, u3...un. We denote the variance by v.

The likelihood of ui being observed is defined as the probability density function for X when

X=ui. This is given by:

1/(sqrt(2**v)) * exp(-ui2/2v)

Thus, the likelihood of m observations being observed in the order of their

occurrence is given by:

[1/(sqrt(2**v)) * exp(-ui2/2v)]

Taking logarithm of the expression, we get,

[-ln(v)-ui2/v]

Thus, we try to estimate the value of parameter lambda such that the value of above

expression is maximized.

Testing of the Model

Model validation is necessary to determine the accuracy of the proposed. Various

ways of testing the model are available such as stress testing back testing.


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Backtesting is a formal statistical approach which involves verifying that the actual

losses are in line with the losses projected by the model. It involves systematically

comparing the history of VaR forecast with the associated actual returns.

Any observations which fall outside the VaR limit are known as exceptions. In case of

a well calibrated model, the number of exceptions should be in line with theconfidence interval. It basically implies that at a 95% confidence interval, for every

100 observations back tested, the number of exceptions should be 5. In case number

of exceptions is too high, it indicated that the mode underestimates the risk and

needs to be recalibrated.

One drawback of this model is that number of exceptions may not be exactly the

same as projected by the model, i.e. for a 95% confidence, we would expect the

number of exceptions to be 5%. However, the actual number of number exceptions

may be between 4%-8% due to various factors such as bad luck. It does not imply

that the model is incorrect. However, in some cases the number of exceptions maybe too high, such as 15%, in such cases the models needs to be revaluated and

calibrated.[9]

For the purpose of our study, the accuracy of the volatility and the model accuracy

were tested using the Bernouillis trial approach, ie, by recording the failure rate.

Failure rate gives the proportion of times the VaR is exceeded for a given sample.

= /

where,

X= Number of Failures

T= Total number of days for which tested.

A failure is said to have occurred if the actual loss for a given period is higher than

the estimated VaR for that period. Ideally, with the increase in the sample size,

failure rate should converge to p.[9]

=

Where

P = probability of failure

T = Number of Trials


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Limitations

The moving average models suffer form a downward bias and underestimatethe volatility. Also, they react slowly to the new data because the old data is

also given the same weight ( 1/N) for an N period moving average model.

The EWMA estimate for volatility is free from that bias but it tends to providetoo conservative estimates of VaR.

Tax Saver funds, though used as a proxy, are not closed ended funds in thetrue sense.

The universe of Mutual funds in India is too large over 600 funds, hence thesample set may be not be totally representative.

Sample set for dividend yield funds ( 2 funds) is too small. The mean returns from the mutual funds have been assumed to be 0 in our

modeling. Though, a small number it is not 0 in real data.

Some of the major Fund houses such as SBI MF, DSP BlackRock have not beencovered in our study.


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Results

In our analysis, the CNX Nifty exhibited a 1 week VaR of 4.59% at 95% confidence

level and 6.5% at 99% confidence level using the volatility estimates for Simple

Moving Average. When the EMWA estimate was used, the volatility estimates were

higher at 5.82% for 95% confidence level and 8.24% at 99% confidence level.(ReferAppendix I)

The large cap funds exhibited a 1 week VaR of 5.44% at 95% confidence level and

7.70% at 99% confidence level using the volatility estimates for Simple Moving

Average. When the EMWA estimate was used, the volatility estimates were higher at

6.80% for 95% confidence level and 9.63% at 99% confidence level.(Refer Appendix I)

Thus, from appendix I, one can clearly observe that as of a whole the diversified

large cap funds exhibited a higher 1 week VaR than the benchmark, CNX Nifty. Also,

all the funds considered under the large cap category exhibited a higher downside

risk than their benchmarks.

Thus, one may conclude that when VaR is used as a measure for evaluating the fund

performance, the large funds perform poorly as compared to their benchmark.

In our analysis, the CNX Mid Cap exhibited a 1 week VaR of 8.42% at 95% confidence

level and 11.92% at 99% confidence level using the volatility estimates for Simple

Moving Average. When the EMWA estimate was used, the volatility estimates were

higher at 6.91% for 95% confidence level and 9.79% at 99% confidence level.(Refer

Appendix II)

The mid cap funds exhibited a 1 week VaR of 6.46% at 95% confidence level and

9.15% at 99% confidence level using the volatility estimates for Simple Moving

Average. When the EMWA estimate was used, the volatility estimates were higher at

6.63% for 95% confidence level and 9.39% at 99% confidence level.(Refer Appendix

II)

Thus, from appendix II, one can clearly observe that as of a whole the diversified mid

cap funds exhibited a lower 1 week VaR than the benchmark, CNX Nifty. Also, all the

funds considered under the mid cap category exhibited a lower downside risk than

their benchmarks. The only outliers in the group were Sundaram Mid Cap fund and

JM Mid cap fund which exhibited a higher downside risk than their benchmark when

EMWA estimated for volatility were considered.

Thus, one may conclude that when VaR is used as a measure for evaluating the fund

performance, the mid cap funds perform better as compared to their benchmark.

Though, when compared with the large cap funds, they exhibit a higher downside

risk, which should be expected due to a higher concentration of mid cap stocks whichtend to have a higher volatility than the large caps.


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In case of Closed Ended Funds, the 1 week VaR was found to be of 6.08% at 95%

confidence level and 8.61% at 99% confidence level (Refer Appendix III) using the

volatility estimates for Simple Moving Average. When the EMWA estimate was used,

the volatility estimates were higher at 6.22% for 95% confidence level and 8.81% at99% confidence level.(Refer Appendix III). As group, the downside VaR exhibited by

the Closed Ended funds was higher than the benchmark, CNX Nifty. It was also higher

than the downside VaR exhibited by the open ended large cap funds.

Thus, one can conclude that the closed ended funds exhibit a higher downside risk

than the open ended funds. They perform poorly than the diversified large cap funds

when VaR is used as a measure.

The analysis of dividend yield funds yielded surprising results. They exhibited 1 week

VaR estimates of 7.43% and 10.52% at 95% and 99% confidence levels(Refer

Appendix IV) when volatility estimates were calculated using the Simple movingaverages. When estimates were calculated using the EMWA, the 1 week VaR at 95%

and 99% confidence interval were found to be 7.31% and 10.32%.Thus, as a group,

the dividend yield funds exhibited higher VaR than the diversified large cap funds

and closed ended funds. Also, the dividend yield funds fared worse than even the

mid cap funds and exhibited higher downside risks. Thus, the dividend yield funds

fare poorly when VaR is used as a performance measure.


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Appendix I : Large Cap Funds

Table 1: Large Cap Funds VaR

Result:

a. All the funds in the sample set exhibited higher downside risk as compared to theirbenchmarks.

b. Average downside risk exhibited by the funds was also higher than the benchmark.

Moving Avg Lower Risk than Benchmark EMWA Lower Risk than Benc

Large Cap Funds 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaRFidelity Equity Fund -4.95% -7.01% FALSE FALSE -5.85% -8.28% FALSE FALSE

Kotak 50 -5.37% -7.61% FALSE FALSE -7.08% -10.03% FALSE FALSE

HSBC Equity -4.62% -6.55% FALSE FALSE -6.27% -8.88% FALSE FALSE

Franklin Bluechip Fund -5.74% -8.13% FALSE FALSE -5.95% -8.43% FALSE FALSE

UTI Equity Fund -4.85% -6.87% FALSE FALSE -6.52% -9.24% FALSE FALSE

Taurus Starshare -7.10% -10.05% FALSE FALSE -9.11% -12.91% FALSE FALSE

HDFC Equity Fund Grow -5.35% -7.57% FALSE FALSE -6.77% -9.59% FALSE FALSE

Avg -5.43% -7.69% FALSE FALSE -6.79% -9.62% FALSE FALSE

Benchmarks

CNX Nifty -4.59% -6.50% -5.82% -8.24%


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Appendix II : Mid Cap Funds

Table 2: Mid Cap Funds VaR

Result

a. In case of moving averages, the downside risk exhibited by each of the fund wasbetter than its benchmark.

b. In case of EMWA estimate, except for Sundaram Mid Cap and JM Mid Cap, thedownside risk exhibited by the funds was better than their benchmark.

c. Average downside risks exhibited by the funds was also better than their benchmark.


Mid Cap Funds 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaR

HSBC Mid Cap -5.38% -7.61% TRUE TRUE -6.30% -8.93% TRUE TRUE

Relaince Growth -5.30% -7.51% TRUE TRUE -6.62% -9.37% TRUE TRUE

Sundaram Mid Cap -7.00% -9.92% TRUE TRUE -7.67% -10.87% FALSE FALSE

Kotak Mid Cap -6.34% -8.98% TRUE TRUE -6.73% -9.53% TRUE TRUE

Tata Equity Opportuniti -5.60% -7.93% TRUE TRUE -6.54% -9.26% TRUE TRUE

ICICI Prudential Discove -7.04% -9.97% TRUE TRUE -6.14% -8.70% TRUE TRUE

Sahara Mid Cap Fund -7.18% -10.18% TRUE TRUE -5.63% -7.97% TRUE TRUEJM Mid Cap -7.85% -11.12% TRUE TRUE -7.41% -10.50% FALSE FALSE

Avg -6.46% -9.15% TRUE TRUE -6.63% -9.39% TRUE TRUE

CNX Mid Cap -8.42% -11.92% -6.91% -9.79%


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18 | P a g e

Appendix III: Closed Ended Funds

Table 3: Closed Ended Funds VaR

Result

a. As a group, they exhibit higher downside risk than Diversified Equity Funds whenMoving Average volatility estimates are taken.

b. As a group, they exhibit lower downside risk than Diversified Equity Funds whenEMWA volatility estimates are taken.

c. Exhibit higher downside risk than the Benchmark (CNX Nifty), in both cases, Movingaverage and EMWA estimates for volatility.


Closed Ended Funds 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaRMorgan Stanley Growth -4.94% -7.00% FALSE FALSE -5.50% -7.79% TRUE TRUE

Reliance Tax Saver -7.21% -10.21% FALSE FALSE -5.89% -8.34% FALSE FALSE

Kotak Tax Advantage -6.69% -9.48% FALSE FALSE -5.77% -8.17% TRUE TRUE

Franklin India Taxshield -5.98% -8.47% FALSE FALSE -7.08% -10.03% FALSE FALSE

Escorts Tax Plan -5.55% -7.86% FALSE FALSE -6.88% -9.74% FALSE FALSE

Sundaram Taxsaver 98 -5.62% -7.96% FALSE FALSE -7.23% -10.25% FALSE FALSE


Benchmarks

CNX Nifty -4.59% -6.50% -5.82% -8.24%


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19 | P a g e

Appendix IV: Dividend Yield Funds

Table 4 : Dividend Yield Funds VaR

Result

a. Exhibit higher downside risk than the benchmark (CNX Nifty).b. Exhibit higher downside than the Diversified Equity Funds and Closed Ended Equity

Funds.

c. Exhibit the highest downside risk among all categories of funds covered underanalysis.


Closed Ended Funds 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaR 95% VaR 99% VaR

Principal Dividend Yield -7.41% -10.49% FALSE FALSE -6.84% -9.68% FALSE FALSE

Tata Dividend Yield Fun -7.45% -10.55% FALSE FALSE -7.79% -11.04% FALSE FALSE


Benchmarks

CNX Nifty -4.59% -6.50% -5.82% -8.24%


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References1. Indian Mutual Fund Industry -Towards 2015. CII 6th Mutual Fund Summit 2010.

http://www.pwc.com/en_IN/in/assets/pdfs/Publications-2010/CII-PwC_Mutual-Fund-

Summit-2010.pdf

2. AMFI India, Mutual Fund - Concept, Organisational Structure, Advantages andThttp://www.amfiindia.com/showhtml.aspx?page=mfconcept

3. Mutual Funds India Research Team(2011), Performance Measures Of Mutual Fundshttp://www.mutualfundsindia.com/perf.asp

4. Icra Mutual Funds Rating Methodology.http://www.mutualfundsindia.com/methodology.asp?rkperiod=18

5. John C. Hull , Options, Futures and other Derivatives,7th Edition, (2008), Prentice Hall6. Investopedia, Mutual Fund Basics

http://www.investopedia.com/university/mutualfunds/mutualfunds.asp7. Kumar,Arvind, (Dec- 2002) Long & Short of Mid-cap Funds

http://www.valueresearchonline.com/story/h2_storyView.asp?str=3736

8. Hung,Dan,(27 Jul-2007) Closed Ended Fundshttp://thecuriousinvestor.com/2007/07/27/closed-ended-funds/

9. Amit, Rajan, (April-2011) Dividend Yield Funds shine in Volatile Marketshttp://articles.timesofindia.indiatimes.com/2011-04-05/india-business/29383934_1_mid-

cap-funds-high-dividend-capital-appreciation

10.Philippe Jorion,Value At Risk, 2nd Edition 2001. Mc Graw Hill.11.Reuter JP Morgan, Risk Metrics Technical Document, Fourth Edition (1996),

Morgan Guarantee Trust Company

12.Source for Mutual Fund NAV Data:http://www.rrfinance.com/Mutual%20Fund/MF_NAV_H.aspx

13.Kumar,Arvind, (Dec- 2002) Long & Short of Mid-cap Fundshttp://www.valueresearchonline.com/story/h2_storyView.asp?str=3736

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