Session 1, Understanding Risk

8/9/2019 Session 1, Understanding Risk

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Financial Modeling for Risk

Management

Himanshu JoshiJ.U.I.T

Session : Introduction to Risk management


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@Himanshu Joshi,JUIT (H.P) 2

Introduction

The Academic discipline of finance was developed inthe U.S during the forties, fifties and sixties withpioneering works by Nobel Laureates Tobin,Markowitz, Modigilani, Miller and others.

Subsequently, a whole range of ideas and modelswere developed in the theory of investments-Markowitz Model, Capital Asset Pricing Model and

Arbitrage Pricing theory are among the major works.Relying on such theories and models, trillions of

dollars are invested throughout the world.In I.T, Finance and Investment domain is one of the

largest and perhaps most lucrative domain.


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State of risk management


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What it is not?

Man in white coat syndrome


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What it is?

art of approximations


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Interest in risk management is due to

volatility


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1971 Collapse of Bretton woods EX rates became flexibleand volatile

1973 Oil price shocks Inflation

1987Black Monday

1trillion capital shaved

1989 Japanese stock marketdeflated

Nikkei declined from39,000 to 17,000 in 3 yrs

1994 Fed incraesed rates 6

times consecutively

Bond market debacle

1997 Asian crisis Emerging marketsbecame pariahs

And the saga continues

We live in a risky world


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Fed rates 1990-05


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LIBOR 2000-05

LIBOR: London Inter Bank Offer Rate


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NYMEX crude oil futures Jan 2004-05


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INR USD 1973-93


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INR USD 1993-04


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@HimanshuJoshi,JUIT(H.P)

Nifty2003-0

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Reasons for volatility

Deregulation

Globalization


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Taxonomy of risks

Market risk

Credit risk

Liquidity risk

O

perational risk Legal risk


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Market risk

the risk incurred in trading assets and liabilities

due to changes in interest rates, exchange

rates, and other asset prices

Directional risk risk of loss due to unfavorable

movement in the direction of u/l asset,

exchange rate, interest rate or index

Volatility risk

risk of loss due to unfavorablemovement in volatility


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Credit risk

the risk that the promised cash flows from loans and

securities held by FIs may not be paid in full

Counterparty risk- risk of loss due to Counter Party default

Sovereign risk risk of loss due to sovereign action

Settlement risk risk of loss due to failure by a CP to settle

trade/cashflow


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Liquidity risk

Risk of loss due to inability to liquidate assets or

obtain funding

Asset liquidity risk

risk of loss due to the inabilityto conclude a transaction at the prevailing prices

Funding risk- risk of loss due to inability to secure

new funding or rollover of existing funding


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Operational risk

the risk of loss due to errors in processes andcontrols

Model risk risk of loss due to errors in thefinancial mathematics or assumptions underlyinga model used for valuation purposes


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Legal risks

Risk of loss due to legal events


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What to do with all these?

ART philosophy

Accept the risk (e.g., self-insure)

Remove the risk (divest, diversify)

Transfer the risk (hedging, insurance)


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Risk Management process

Risk Identification

Risk quantification

Risk monitoring and reporting


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Risk identification -1

A proper identification of risk is possible only

after a thorough understanding of a product,

transaction, market or process has been gained.

A floating rate borrower

A fixed rate depositor


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All dimensions of risk must be identified; risks

that might be less apparent at the time of

analysis should not be ignored as they canbecome more prominent as market conditions

change

Eg : OTC option bought by an investor

Risk identification -2


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Identification process should follow a logical

progression beginning with the most common or

essential and moving on to the more complex oresoteric

Risk identification


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Market risks

Directional risk

Curve risk

Volatility risk

Basis risk Skew risk

Volatility of volatility risk

Cross volatility risk

Fundamental risks

esoteric risks


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Risk quantification

Risks discovered in the identification stage should bedecomposed into quantifiable terms; this allows exposures to

be constrained and monitored

Models are based on assumptions that may or may not be

realistic; assumptions and the impact they can have on

valuation, must be well understood

Models should not be used to the point of blind faith

they are only ancillary tools intended to supplement the risk

process

safe assets can become risky in a crisis

quantifying thedownside of such exposures is useful


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Risk monitoring

Monitoring involves internal scrutiny and tracking ofexposures in relation to limits/policies

Reporting means communication of exposures internally and

externally

It is more useful to have timely report of 90% of a firmsrisk exposure than delayed reporting of 100%

Information should not come from multiple sources a single

independent source should be used as the kernel for all

reports and should be audited for accuracy on a regular

basis


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Risk monitoring

Profits must be reviewed with the same rigor aslosses as they may be indicative of large, orunknown risks

Watch out your star traders


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VaR - evolution

Risk measured in currency unit (in rupee terms) instead ofstandard deviation of returns

Developed in early 1990s by J.P. Morgan by Chairman DennisWeatherstone.

He wanted a single measure of overall portfolio risksummarizing the companys overall global risk exposureduring the next 24 hours.

The resulting report was known as the 4.15 Report.

In 1994 J P Morgan provided all #s used in theircalculations on a web site.

Widely used today. Some impetus for VaR is the fact thatregulators require banks to calculate VaR and use it indetermining required bank capital.


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VaR translates the risk of any financial instrument

into its potential loss under specific assumptions

Value at risk (VaR)


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The critical elements in VaR computation are

volatility

the confidence level

risk horizon

VaR inputs


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HDFC Growth Fund

FROM TO FROM TO PERIOD SCHEME COMMENTS

23-Jun-2004 23-Jun-2005 15.393 25.403 Last 1 year(s) 65.0296 CAR*




11-Sep-2000 23-Jun-2005 10 25.403 Since Allotment 21.5181 CAR*


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Other details of the fund???

Mean 0.001804

Standard Error 0.000661

Median 0.003632

Mode 0.005977

Standard Deviation 0.014828

Sample Variance 0.00022

Kurtosis 8.455849

Skewness -1.31903

Range 0.170856

Minimum -0.10488

Maximum 0.065976

Sum 0.90759

Count 503


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So what VaR does is

Express the maximum loss that a portfolio is likelyto experience over a particular period.

A simple way to compute VaR is to assume thatreturns are normally distributed and compute the

standard deviation. SD = 1.48% per day


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Computation.. (2)

VaR = 1.65 X Value of PF X volatility

Assuming we require the probable losses with 95%confidence level

VaR (HDFCGF) =1

.65 x1

59

Cr x1

.48

5 =3

.89

6 Cr.

Can be interpreted as the loss that will be

experienced by the portfolio on any day will

exceed Rs 3.896 Cr five out of hundred days


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Converting one day VaR to N-day VaR

The one day VaR is Rs 3.896 Cr. suppose we are

interested in 10-day VaR?

VaR = 1.485% x x 159 = 7.467 Cr.n/10


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Volatility estimation

Risk metrics

Wt2 = P Wt-12 + (1- P) rt-12

GARCH

Wt2 = [2 + F Wt-12 + E rt-12


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Computation methods

Historic simulation - It uses empirical percentilesfrom the historical return distribution and getsaround the problem of making distributionalassumptions


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Be alert

VaR is not a unique number

Choice of time interval

Confidence level

Distribution of asset returns


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Data on NIFTY returns from 1990-2003 was used for testing

some of the assumptions.

Empirical analysis


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Nifty statistics

Mean 0.000422

Standard Error 0.000336

Median 0.000328

Mode 0

Standard Deviation 0.018344

Sample Variance 0.000336

Kurtosis 5.074193

Skewness 0.043609

Range 0.24608

Minimum -0.12522

Maximum 0.120861Sum 1.254431

Count 2972


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Results

Stock returns are leptokurtic

Volatility clustering may be noted


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-.15

-.10

-.05

.00

.05

.10

.15

500 1000 1500 2000 2500

NIFTYRET


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Histogram

0

50

100

150

200

250

300

350

400

-0.125

219354

-0.1115482

41

-0.097

877128

-0.084

206015

-0.070

534903

-0.056

86379

-0.043

192677

-0.0295215

64

-0.015

850451

-0.002

179338

0.01

14917

75

0.0251

62888

0.03

88340

01

0.05

25051

14

0.06

61762

26

0.07

98473

39

0.09

35184

52

0.1071

89565

More

Frequen

cy


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HS 95 RM 95 HW 95 HS 99 RM 99 HW 99

Mean 3.00 2.90 2.78 5.18 4.09 3.36

Range 0.71 4.17 3.63 7.29 5.89 6.24Minimum 2.65 1.08 1.12 2.68 1.52 1.88

Maximum 3.36 5.25 4.75 9.97 7.41 8.12

95% Range

No. of violations 27 19 21 7 8 8

510 DAYS

16 < N < 36 1 < N < 11


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Managerial implications

From capital allocation perspective Average VaR was lowest

Range of fluctuations is tolerable

From traders perspective the position limits under HW method may be

conservative


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Caveat de emptor

VaR is not a panacea

VaR systems are back-ward looking

VaR systems are based on certain assumptions

that may not be valid in certain circumstances

VaR is to be treated as tool and used by people

who know how and how not, to use them!!


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Internet Resources

www.glor a d .o g

www.contingencyanalysis.com

www.value-at-risk.net

www.r sk e r cs.com

Session 1, Understanding Risk

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