measuring and managing market risk

DANIAL MUNSOOR FIN955 REPORT

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Table of Contents

1 INTRODUCTION ............................................................................................................ 2

1.1 Background ................................................................................................................. 2

1.2 Objective ..................................................................................................................... 2

2 BANK CAPITAL STANDARDS AND MARKET RISK ............................................. 2

3 SIGNIFICANE OF MARKET RISK TODAY .............................................................. 2

3.1 Securitization ............................................................................................................... 3

3.2 Growth of Financial Derivatives Market .................................................................... 3

3.3 Adoption of new Accounting Standards ..................................................................... 3

4 MORDERN APPROACHES TO MARKET RISK MEASURMENT ........................ 3

4.1 Value at Risk Models Responding to Market Risk ..................................................... 4

4.1.1 Variance Covariance Method .............................................................................. 4

4.1.2 Historical Method ................................................................................................ 6

4.2 Stress Testing .............................................................................................................. 8

5 VAR MODEL BACKTESTING AS PER THE BASEL COMMITTEE .................... 8

6 TRADITIONAL APPROACH TO MARKET RISK MEASURMENT ..................... 9

7 FIVE MAIN CATEGORIES OF MARKET RISKS .................................................. 10

8 MARKET RISK AND THE FINANCIAL CRISIS .................................................... 10

9 CONCULSION ............................................................................................................... 11

10 REFERENCES ................................................................................................................ 12


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1 INTRODUCTION

1.1 Background:

This report examines one of the most important risks associated with banks known as the

Market Risk. Market Risk is defined as the risk of losses in the value of a bank’s

Trading Portfolio due to the changes in market factors such as interest rates, stock prices,

exchange rates, inflation, economic growth, unemployment etc. This risk is also referred

to as Price Risk.

1.2 Objective:

The objective of this report is to understand the origin of Market Risk, its significance

today, and how it can be measured using Traditional and Modern techniques. The report

also highlights as to how we can perform VAR Model Backtesting according to the

Basel Committee. However, for a better understanding of the subject, graphs and case

studies/examples have also been used.

2 BANK CAPITAL STANDARDS AND MARKET RISK:

The original Basel Agreement was unable to deal with one of the most important risk

being faced by banks today i.e. Market Risk. Therefore, in January 1996, the Basel

Committee on Banking Supervision modified the Basel Agreement by adding certain

rules which permitted the largest banks to conduct risk measurement and estimate the

amount of capital necessary to cover market risk (Rose and Hudgins, 2010:495).

3 SIGNIFICANE OF MARKET RISK TODAY:

Market risk has become significant due to banks’ involvement in Cross Border

transactions and Diverse Markets around the globe. Hence they are exposed to price


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fluctuations in these markets that can adversely affect the earnings on their trading

portfolios (Hughes and Macdonald, 2002:301).

Market risk has been gaining importance on international financial markets over the last

decade due to the following three reasons:

3.1 Securitization:

The securitization process has resulted in illiquid assets (loans/mortgages) being

increasingly replaced by assets that have a liquid secondary market, and therefore

a price. This process has given rise to Mark to Market method for measuring the

value of assets held by Financial Institutions (Resti and Sironi, 2007:106).

3.2 Growth of Financial Derivatives Market:

This market mainly looks at the change in the relevant market value caused by

changes in underlying asset prices (Resti and Sironi, 2007:106).

3.3 Adoption of new Accounting Standards:

Adoption of new accounting standards, which particularly result in an immediate

effect of profits and losses linked to short term changes in market conditions, have

played an important role in making market risk effects more noticeable while

highlighting their importance (Resti and Sironi, 2007:106).

4 MORDERN APPROACHES TO MARKET RISK MEASURMENT:

Market Risk can be measured using two very sophisticated and quantitative methods

known as Value at Risk (VAR) and Stress Testing. However, these methods have been

described below in great detail along with comprehensive case studies.


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4.1 Value at Risk Models Responding to Market Risk:

It is supposed to be the premiere risk management technique. This method

measures market risk over a certain period of time under normal market conditions

(Hughes and Macdonald, 2002:462). Due to the weakness in the original Basel

Agreement and their lack of flexibility in responding to advances in finance

industry, bank regulators started to allow the major banks to use their own internal

models to determine the loses they might incur. These models are known as the

VAR Models which determine the amount we tend to lose over a certain period

for a given Confidence Level/Probability (Rose and Hudgins, 2010:495). The

most commonly used confidence/significance levels are 1% and 5%.

Suppose that the 1% Daily VAR is $18 million. Now this situation can be

interpreted in two ways as shown below:

There is a 99% chance that the daily losses will exceed $18 million or the

maximum daily loss would be $18 million.

There is a 1% chance that the daily losses will not exceed $18 million or the

minimum daily loss would be $18 million.

However, the VAR can be calculated using any one of the following methods:

4.1.1 Variance Covariance Method:

This method assumes that portfolio returns are normally distributed i.e. it is

described by its Expected Value (µ) and Standard Deviation ( ). To have

a better understanding of this method, a case study has been discussed

below which covers all the important aspects relevant to the VAR method.


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CASE STUDY:

A portfolio manager at XYZ Bank is interested in computing the 5% Yearly VAR

for a portfolio consisting of two classes of assets. The first asset class consists of a

group of securities of the stocks traded on the NYSE. On an annual basis, the

expected return on this asset class is 13.52% and the standard deviation is 12.97%.

The second asset class consists of a group of securities traded on NASDAQ. The

expected annual return on this asset class is 16.80% and the standard deviation is

25.78%. The correlation between the annual returns of the two asset classes is

0.79. The market value of the portfolio is $20 million, and the portfolio is invested

60% and 40% in the two asset classes, respectively (Source: Resti and Sironi,

2007)

NYSE = 13.52% = 12.97% = 0.6

NASDAQ = 16.80% = 25.78% = 0.4

For such a scenario, we should first recall that the Expected Return of a Portfolio

( ) is a weighted average of the expected returns of its component stocks or asset

classes, which in our case are NASDAQ and NYSE. We should also know that the

Variance of a Portfolio ( ) is a weighted average of the variances and covariance

of the component stocks or asset classes. In order to calculate and , we use

the following formulas:

= +

= 0.60(0.1352) + 0.40(0.168) = 0.1483 = 14.83%

= + + 2


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= + + 2(0.60)(0.40)(0.1297)(0.2578)(0.79) = 0.0294

= = 0.1715 = 17.15%

In order to calculate VAR we apply the following formula:

5% Yearly VAR = ( - 1.656 ) x Value of Portfolio

= [0.1483 – 1.656 (0.1715)] x $20,000,000

= -$2.714 million

It means that there is a 5% chance that the yearly loses will exceed $2.714 million

OR there is a 95% chance that the yearly loses will not exceed $2.714 million.

However the same procedure would be followed if we were to calculate 1%

Yearly VAR. the only difference would have been that instead of using ( -

1.656 ) we would be using ( – 2.336 ) to calculate the 1% Yearly VAR.

4.1.2 Historical Method:

This is another method which can be used to calculate VAR. This method

uses data from the returns of the portfolio over a recent past period. To

- 1.656


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have a better understanding of this method, a case study has been discussed

below.

CASE STUDY:

To keep the example simple, it is assumed that only one-stock portfolio is used.

Suppose we have the following 16 worst monthly returns on IBM stock during

the last 20 years. Assume that the value of the portfolio is $200,000 (Source: Resti

and Sironi, 2007).

-0.17867 -0.10655 -0.08065 -0.07220

-0.17505 -0.09535 -0.07779 -0.07126

-0.17296 -0.09348 -0.07237 -0.05031

-0.16440 -0.08236 -0.07234 0.04889

(Note: The data above has already been arranged in descending order)

Before we proceed, the first thing to be noted here is that in 20 years we will have

240 monthly returns. But here we are able to see only 16 returns. However, the

amount of data given above is sufficient to compute the VAR. Suppose that a

financial analyst is interested in computing 1% and 5% monthly VAR. In order

to calculate VAR he would do the following:

For 5% monthly VAR:

Out of 240 returns, 5% of them are the 12 worst returns (240 x 5%).

Therefore, the historical VAR would be the 12th

worst return. Since the data

is already arranged in descending order, we can see from the table that this

return is -0.07234 (highlighted in blue). So the VAR would be $14,468

(0.07234 x 200,000).

For 1% monthly VAR:

Out of 240 returns, 1% of them are the 2.4 worst returns (240 x 1%). In this

situation we would probably use the 2nd

worst return, which is -0.17505


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(highlighted in red). Therefore, the monthly VAR would be $35,010 (0.17505

x 200,000).

4.2 Stress Testing:

It is a computer simulation technique that measures market risk over a certain

period of time under abnormal market conditions. Stress tests provide information

summarizing the firm’s exposure to extreme, but possible circumstances (Hughes

and Macdonald, 2002). It identifies and manages situations which can cause

extraordinary losses by revaluing the portfolio under simulated conditions (Resti

and Sironi, 2007:218).

5 VAR MODEL BACKTESTING AS PER THE BASEL COMMITTEE:

The Basel Committee makes it obligatory for the banks to regularly backtest their VAR

models on a quarterly basis, based upon 250 trading days. The table below helps a

bank in performing a backtest in order to have an idea that whether its VAR model has a

satisfactory quality level or not (Resti and Sironi, 2007:246).

Area Number of

Exceptions

Increase Multiplying

Factor

Green 0 0.00 3.00

1 0.00 3.00

2 0.00 3.00

3 0.00 3.00

4 0.00 3.00

Yellow 5 0.40 3.40

6 0.50 3.50

7 0.65 3.65

8 0.75 3.75

9 0.85 3.85

Red ≥10 1.00 3.95

Suppose a bank is using a model to calculate Daily VAR at 99% Confidence Level. In

this case, we are likely to expect loses only in 1% of the cases i.e. on 2.5 out of the 250


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trading days. If the “Number of Exceptions”, i.e. the number of days on which loses are

in excess of VAR, is lower than, equal to, or slightly higher than 2.5 we can assume that

the model is working perfectly. However, if the number of exceptions is significantly

higher than predicted by the adopted confidence level, we can assume that the model is

encountering some problems (Resti and Sironi, 2007:246).

The table above, which was developed by the Basel Committee in 1996, also shows that

the Multiplying Factor ranges from 3 (if the number of exceptions is equal to a

maximum of 4) to 4 (if the number of exceptions is 10 or more). Note that if the VAR

model falls in the red area, the multiplying factor of 3 will be raised to 4. In other words,

it means that the worse the model’s, performance the greater the increase (Resti and

Sironi, 2007:247).

Backtesting helps a bank to evaluate its past forecasting performance and also indicates

a higher capital requirement if the bank has been inconsistent in forecasting, thus

encouraging the bankers to develop better models.

6 TRADITIONAL APPROACH TO MARKET RISK MEASURMENT:

The Traditional Approach to market risk management was based on nominal values of

individual positions. Moreover, the risk exposure was also directly proportional to the

nominal value of the financial instruments (Resti and Sironi, 2007:107). This approach

was well known because:

It was simple to use

Cost was relatively low

It did not require much information and updates because the nominal value of

an asset remains constant.


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On the contrary it also has several limitations out of which the three main ones are as

follows:

Nominal value of a position doesn’t reflect its market value

Nominal values cannot capture the different degree of sensitivity of different

securities to changes in market factors

Nominal value does not consider the volatility and correlation conditions of

market prices/rates

7 FIVE MAIN CATEGORIES OF MARKET RISKS:

As described in the report, Market Risk is the risk of loss associated with the unexpected

movements in the market factors and is to be distinguished from other types of risks such

as Credit (Default) Risk, Operational Risk etc. (Dowd, 2005:15). However, we cannot

completely separate market risk from them because it can sometimes be created by them.

For e.g. defaults can lead to changes in market prices and rates i.e. they might affect

bond spreads or bond prices, which can result in market losses. On the other hand,

operation failures can also lead to market losses (Dowd, 2005:15). The five main

categories of market risk are:

Equity Risk

Commodity Risk

Volatility Risk

Exchange Rate Risk

Interest Rate Risk

8 MARKET RISK AND THE FINANCIAL CRISIS:

The current “Financial Turmoil” has completely changed the picture of the finance

industry for all institutions, including the banks as they are now operating in a rapidly

changing environment. The crisis has forced many banks to think outside the box i.e.


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banks cannot rely heavily on their internal models for predicting stress events, asset price

uncertainty, and ineffective hedge behaviour that leads to indirect deterioration of

portfolio quality and pressure on net interest margin (African Development Bank, 2009).

Current distorted and illiquid market conditions have created valuation challenges for the

investment, borrowing and derivatives portfolio. Moreover, in a distressed liquidity and

credit environment, “fire-sale” prices are often significantly lower than expected

recovery values. And so, in spite of the credit downgrades of counterparties, it is difficult

to liquidate positions (African Development Bank, 2009).

9 CONCULSION:

In conclusion, for Financial Institutions (FI’s) taking speculative positions in currencies,

bonds or stocks, there is a high possibility that loses associated with market risk can eat

the profits which have been realized over a period of time by these FI’s. Despite of

market risk’s growing significance, the Basel Agreement was unable to deal with it, and

therefore the bank regulators allowed the major banks to use their own internal model

known as Value at Risk (VAR) model. As shown above, VAR helps a bank to determine

the amount it tends to lose over a certain period for a given Confidence Level. Banks can

also use Stress Testing to measure market risk under pessimistic scenarios. Apart from

using the latter two modern techniques, banks can also use the Traditional Approach to

measure market risk. Moreover, the current financial crisis also forced many banks not to

rely heavily upon their internal models.


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10 REFERENCES:

Hughes, J. & Macdonald, S. 2002, International Banking Text and Cases, Pearson

Education, Boston.

Resti, A. & Sironi, A. 2007, Risk management and shareholders’ value in banking,

Wiley, London.

Rose, P. & Hudgins, S. 2010, Bank Management and Financial Services, McGraw-Hill,

New York.

Dowd, K. 2005, Measuring Market Risk, Avaliable:

http://books.google.co.uk/books?id=wL7hwpuTa9sC&printsec=frontcover&source=gbs

_ge_summary_r&cad=0#v=onepage&q&f=false [Accessed 29th

March, 2011]

African Development Bank, 2009, Market Risk Review, Available:

http://www.afdb.org/fileadmin/uploads/afdb/Documents/Financial

Information/Market%20Risk%20Review%202009.pdf [Accessed 28th

March, 2011]

http://books.google.co.uk/books?id=wL7hwpuTa9sC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false

http://books.google.co.uk/books?id=wL7hwpuTa9sC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false

http://www.afdb.org/fileadmin/uploads/afdb/Documents/Financial%20Information/Market%20Risk%20Review%202009.pdf

http://www.afdb.org/fileadmin/uploads/afdb/Documents/Financial%20Information/Market%20Risk%20Review%202009.pdf