FinQuiz smart summary, study session 14, reading 34

RISK MANAGEMENT AS A PROCESS ranges for exposures, measurement of these exposures & the execution of appropriate adjustments when required.. Risk management process to a hypothetical bus

“ RISK MANAGEMENT ”

1 INTRODUCTION

Identification, measurement & control of risk are key to the investment process

Risk management framework is applicable to the management of both enterprise & portfolio risk

Indentify which risks are worth taking on a regular or occasional basis & which should be avoided altogether

2 RISK MANAGEMENT AS A PROCESS

ranges for exposures, measurement of these exposures & the execution of appropriate adjustments when required

Risk management is a continuous processes (subject to evaluation & revisions) not just an activity

Risk management process to a hypothetical business enterprise:

Set Policies &

Procedures

Define Risk Tolerance

Identify Risks

Measure Risks

Adjust Level

of Risk

Execute Risk Mgmt

Transactions Identify Appropriate Transactions

Price Transactions

Execute Transactions

Derivatives

Non-Derivatives

Uncertainty

Select Appropriate Model

Determine Market Price or Value

Determine Model Price or Value

Compare

Transaction

RG = Risk Governance

ERM = Enterprise Risk

Management

CG = Corporate Governance

DB = Defined Benefit

ESG = Environmental, Social

Governance

VAR = Value at Risk EAR = Earning at Risk

CR = Credit Risk

IR = Information Ratios RAROC = Risk-Adjusted Return on Capital

2 RISK MANAGEMENT AS A PROCESS

Risk management process to portfolio management:

Companies hedge risk that arise from areas in which they have

no expertise or comparative advantage &hedge tactically where they have an edge (e.g primary line of business)

Risk management involves risk modification

3 RISK GOVERNANCE

RG ⇒process of setting overall policies & standards in risk management is called RG

RG is of good quality if it is transparent, effective, efficient &

accountable

Risk Governance Structure

Single risk management group to monitor & control the risk

Also called ERM or firm wide risk management

Risk management by individual business unit managers

Benefits

Economies of scale

Allows a company to recognize the offsetting nature of distinct exposures

Enterprise-level risk estimates may be
than individual units (risk-mitigating benefits of diversification)

Consider each risk factor to which a firm is exposed (in isolation & in terms of any interplay)

Benefit

People closer to actual risk taking are allowed to manage it

Effective RG is possible only if the organization has effective CG

Steps in effective ERM system:

Identify individual risk factors

Quantify exposure in monetary term

Use these inputs to a risk estimation model (e.g VAR)

Indentify overall risk exposures & contribution from each risk factor

to overall risk

Process of risk reporting to senior management

Monitor compliance with policies & risk limits

Effective ERM systems have centralized data warehouses which may require a significant & continuing investment

4 IDENTIFYING RISKS

Effective risk management requires the separation of risk exposures into specific categories that reflect their distinguishing characteristics

Accounting Taxes

Legal

Regulations

Settlement

Operations Model

Liquidity Risk

Market Risk (interest rate, exchange rate, equity prices & commodity prices risk)

4.1 Market Risk

Market risk is linked to supply & demand in various marketplaces

DB plan measures market exposure in asset/liability management context

4.2 Credit Risk

Development of credit derivatives has blurred the lines b/w credit risk & market risk

Before OTC credit derivative recognition, bond portfolio managers & bank officers were the primary credit risk managers

4.3 Liquidity Risk

potential inability to efficiently accommodate the desired trading size

In case of short squeezing, liquidity may completely dry up in the market

For illiquid underlying, derivatives market may also be illiquid

Size of the bid-ask spread is an imprecise measure of liquidity risk (because it is suitable only for small trade size)

Complex liquidity measures are available to address the issue of trading volume

Liquidity risk is difficult to observe & quantify

4.4 Operational Risk

external events

The risk can arise from:

Human errors (unintentional errors or willful misconduct)

Computer breakdown (hardware, software problems)

Act of God (only cash compensation for losses can be covered through insurance)

unauthorized transactions or some combination of both

Companies manage operational risk by monitoring their systems, taking preventive actions & having a plan in place to respond if such events occur

4.5 Model Risk

Inappropriate model ⇒ chances of loss  & control over risk is impaired

Investors must scrutinize & validate all models they use

4.6 Settlement (Herstatt) Risk

while the counterparty is announcing bankruptcy

Transactions b/w exchange members & clearing house removes settlement risk

Netting arrangement reduces settlement risk

Transactions with foreign exchange component:

Don’t lend themselves to netting

Parties are unaware to each other

The risk is called Herstatt risk (bank Herstatt default)

Risk can be mitigated through continuously linked settlement (simultaneous payments)

4.7 Regulatory Risk

Risk associated with the uncertainty of how a transaction will be regulated or potential for regulation change

Regulation is a source of uncertainty (risk that existing regulatory regime will ∆ or unregulated market will become regulated)

Regulatory risk is difficult to estimate due to ∆ in political parties & regulatory personnel

Equivalent combinations of cash & derivative securities are not regulated by same way

or by same regulator

4.8 Legal/Contract Risk

contract in which an enterprise has a financial stake

Dealers should be very careful when writing contracts with their counterparties (due to their advisory nature)

Contract law is often federally or nationally governed

4.9 Tax Risk

Uncertainty associated with tax laws

Tax policy often fails to keep pace with innovations in financial instruments

Equivalent combination of financial instruments may be subject to different tax treatments

4.10 Accounting Risk

Uncertainty about transaction recording & potential for accounting rules & regulations

to ∆

Historically accounting standards varied from country to country (more disclosure requirements in some countries than others)

Accounting risk can be reduced by hiring personnel with latest accounting knowledge (accounting risk will always remain)

4.11 Sovereign and Political Risks

Sovereign risk;

Form of credit risk involving sovereign nation’s borrowing

Current credit risk & potential credit risk

Its magnitude involves likelihood of default & the estimated recovery rate

Willingness & ability to repay

Political risk ⇒ risk of ∆ in the political environment

4.12 Other Risks

Environmental risk ⇒ leads to variety of –ve financial & other

consequences

Social risk ⇒ risk regarding policies & practices of human

resources, contractual arrangements & work-place

Performance netting risk;

Applies to firms that fund more than one strategy

Firm will receive fee only if net +ve performance

Firm will pay its portfolio managers on basis of individual performance

Asymmetric incentive fee arrangements with portfolio managers

Firms may have to pay to its portfolio managers when firm’s revenue is zero

Settlement Netting Risk

Risk of netting arrangement on profitable transactions for the benefit of creditors challenged by liquidator of counterparty in default

Risk is mitigated by netting agreements that can survive legal challenge

5 MEASURING RISK

Exposure of actively traded financial instruments prices to ∆ in IR, exchange rates, equity prices & commodity prices

Volatility (S.D) is a statistical tool to describe market risk

Adequate description of portfolio risk

Suitable for instruments with linear payoffs

Portfolio’s exposure to losses due to market risk:

Primary or 1st order measures of risk ⇒ adverse movement in a key variable (linear)

2nd order measures ⇒ ∆ in sensitivities (curvature)

Examples of primary risk measures are β (for stocks), duration (for bonds) & delta, vega &

theta (for options)

Examples of 2nd

order measures are convexity & Gamma

5.1 Measuring Market Risk

5.2 Value at Risk

Probability-based measure of loss potential for a company, fund, portfolio, strategy

or transactions

Expressed either in % or in units of currency

Easily & widely used to measure loss from market risk but can also be used to measure credit & other exposures (subject to greater complexity)

Can be described as a minimum or maximum VAR

It measures a minimum loss

The probability,  the VAR in magnitude

VAR has a time element (the period,  the VAR)

5.2.1 Elements of Measuring Value at Risk

Establishing an appropriate VAR measure requires

a no of decisions about the calculation structure

Three Important Decisions in VAR

The probability, more conservative the

VAR estimate is

Linear risk characteristics portfolios, two

probability level (e.g 5% & 1%) will

provide identical information

Optionality or nonlinear risks, select the

more conservative probability threshold

VAR magnitude is directly related to time interval selected

Relationship is nonlinear

Three standardized methods for estimating VARs (discussed below)

5.2.2 The Analytical or Variance-Covariance Method



Assumptions ⇒ portfolio returns are normally distributed

Standard normal distribution ⇒ expected value of zero & a SD of 1

Conversion of a nonstandard normal distribution to a standard normal distribution:

 = 
^− 


Estimation of expected returns & SD of returns is key to using analytical method

If we are comfortable with normal distribution assumption & accuracy of our estimates, we can confidently use the analytical method for a different time period by adjusting the avg returns & SD accordingly (e.g annual VAR can be converted to daily VAR by dividing avg return & SD to 250 (trading days))

VAR (usually daily VAR) can also be estimated by assuming an expected return of zero Two advantages:

No need to estimate E(R) which is harder to estimate than volatility

Easier to adjust VAR for a different time period(     =  √250)

Advantage/Disadvantage of Analytical Method

Simple method Normal distribution assumption often

does not hold (e.g.in options)

5.2.3 The Historical Method

Collect the historical return & indentify the return below which 5% or 1% of returns fall

No constraint to use normal distribution

If different portfolio composition than what actually had in the past to calculate historical VAR, it is more appropriate to call the method a historical simulation

not so acute)

5.2.4 The Monte Carlo Simulation Method

MCS produce random portfolio returns which are assembled into a summary distribution from which we can determine at which level the lower 5% (or 1%) of return outcomes occur

MCS does not require a normal distribution

MCS is more flexible approach & even suitable for portfolios containing options

As sample size  sample VAR converge to population VAR

MCS require extensive commitments of computer resources

5.2.5 Surplus at Risk": VAR as It Applies to Pension Fund Portfolios

Pension fund managers apply VAR methodologies to the surplus (rather their asset portfolio)

Managers express their liability portfolio as a set of short securities & calculate VAR on net position (any VAR methodology can apply)

5.3 The Advantages and Limitations of VAR

Can be difficult to estimate

Different estimation methods can provide different

results

If assumptions are not accurate, VAR often

underestimate magnitude or frequency of worst returns

Portfolio VAR is not simply the sum of individual

position’s VAR

VAR provide incomplete picture of overall exposure

(ignore +ve results)

Back testing should be applied to check method’s

accuracy

VAR estimate is not suitable for organization with

complex structure

VAR’s Imperfections

Quantify loss in simple terms

Easily understood by senior management

May be a requirement of regulatory body

VAR is a verstyle measure

VAR is often paired with stress testing

VAR results are input dependent

VAR’s Attractions

5.4 Extensions and Supplements to VAR

an asset

Provide extremely limited picture of the asset’s or portfolio’s contribution to risk

Cash flow at risk & EAR ⇒ with a given probability & over a specified time period, the minimum CF (earnings) that we expect to be exceeded

Useful for companies that generate CF or earnings but not readily valued in a publicly traded market

additional loss occurs

5.5 Stress Testing

Stress testing is used to supplement VAR as a risk measure

VAR assumes potential losses under normal market conditions while stress testing identifies additional losses due to unusual circumstances

Approaches in Stress Testing

Evaluating a portfolio under different scenarios

Effect of large movements in a key variable on portfolio’s value

force (e.g IR, exchange rate etc.)

sequential fashion (in reality shocks often happen simultaneously)

resulting from the events that occurred in the past

to analyze & confusing outcomes)

When a series of appropriate scenarios is established, the next step is to

apply them to the portfolio (consider assets’ sensitivities to the underlying

risk factors)

5.5.1 Scenario Analysis

Use an existing model & apply shocks to the model inputs in some mechanical way

Range of possibilities rather than a single set of scenarios

Computationally demanding

way & to work out the combined effect on the portfolio value

Model risk is present

will produce the maximum loss

5.5.2 Stressing Models

5.6 Measuring Credit Risk

CR has two dimensions:

Probability of loss

Amount of loss

Empirical data set on credit losses is quite limited with respect to time perspective, credit losses can be current or potential credit losses

Credit VAR can’t be separated from market VAR & focus on upper tail of the distribution of market returns

More accurate measures of default probability & recovery rate ⇒
& more accurate credit VAR

Estimating credit VAR is complicated because:

Credit events are rare & harder to estimate

CR is less easily aggregated than market risk

Correlations b/w CR of counterparties must be considered

5.6.1 option-Pricing Theory and Credit Risk

A bond with CR can be viewed as:

Default free bond plus

Short put option written by bondholders for shareholders (this put option reflects shareholders right of limited liability)

Traditional put-call parity with a little bit changes can be used to draw value of implicit put option

Value of put option is the difference b/w default-free bond & bond subject to default

5.6.2 The Credit Risk of Forward Contracts

Each party assumes the other’s CR

No current CR exists prior to expiration (no payments are due)

If the counterparty with –ve value declares bankruptcy before the contract expiration, the claim of non-defaulting counterparty is market value of forward contract at the time of bankruptcy

5.6.3 The Credit Risk of Swaps

CR is present at a series of points during the contract’s life

MV of contract can be calculated at any time to reflect potential CR

CR of IR & equity swaps is largest during middle of swap’s life

In case of currency swaps, the CR is greatest b/w midpoint to end of swap’s life

5.6.4 The Credit Risk of options

Options have unilateral CR (after paying premium credit risk accrues entirely to the buyer)

Credit risk on derivative transaction tends to be quite small relative to that on loan

5.7 Liquidity Risk

Cost of an illiquid instrument can be measured through bid-risk spread

Instruments that trade very infrequently at any price give illusion of
volatility)

Practitioners often liquidity-adjust the VAR estimates

5.8 Measuring Non-Financial Risks

These risks are very difficult to measure ⇒ usually lack of observable distribution

of losses related to these factors

Techniques like extreme value theory is used if possible to model sources of risk but these techniques are input dependent

5.8.1 Operational Risk

Well publicized losses at financial institutions (e.g rogue employees theft) have put operational risk justifiable into the forefront

Banks can measure their operational risk through Basel II

5.6.1 Option-Pricing Theory and Credit Risk

6 MANAGING RISK

Effective risk governance model

Systems & technology to provide timely & accurate risk information decision makers

Trained personnel to evaluate risk information

Risk management is just a good common business sense

6.1 Managing Market Risk

ERM system identifies appropriate risk tolerance levels

Taking too little risk is as much problematic as taking too much risk (e.g
possible rewards)

6.1.1 Risk Budgeting

Risk budgeting ⇒ efficient allocation of capital risk across various units of an organization or portfolio managers

Risk Budgeting

Allocation of an acceptable level of risk to various departments of an organization

In addition to VAR, risk can also be allocated based on individual transaction size, amount

of working capital needed etc

If correlation among departments is < 1, the sum of risk budgets for individual units >

than organizational risk budget

Assets class correlation adjusted IR can determine the optimal tracking risk allocation

Investment manager’s allocation is positively related to his correlation adjusted IR

Risk budget allocation should be measured in relation to risk to surplus (assets – liabilities)

6.2 Managing Credit Risk

Estimating default probability is difficult

Credit risk is not symmetric & normally distributed (downside only) thus not easily measured & controlled using SD & VAR

Not lend too much money to one entity

Not engage in too many derivative transactions with one counterparty

6.2.2 Reducing Credit Risk by Marking to Market

Credit risk can be reduced through marking to market an OTC derivative contract

OTC options are not marked to market (one sided +ve value)

Credit risk of option is normally handled by collateral