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WorksheetsWorksheet: Risk Management and Portfolio Theory
Total questions: 95
Worksheet time: 48mins
Two portfolios have identical expected returns and standard deviations. However, Portfolio A has a lower semi-variance than Portfolio B. A downside-risk-averse investor would prefer Portfolio A because
It has higher beta
It reduces total risk
It minimizes negative deviations
It dominates the efficient frontier
In mean–variance optimization, an increase in correlation among assets will
Increase diversification benefits
Shift the efficient frontier inward
Eliminate systematic risk
Increase Jensen’s Alpha
The key limitation of variance as a risk measure is that it
Ignores expected return
Penalizes upside and downside volatility equally
Cannot be estimated empirically
Assumes non-normality
If an asset has a beta of zero, it implies
The asset is risk-free
The asset has no total risk
The asset is uncorrelated with the market
The asset has negative expected return
Which condition violates the assumptions of Modern Portfolio Theory?
Investors are risk-averse
Returns are normally distributed
Investors have heterogeneous expectations
Markets are frictionless
A portfolio located below the efficient frontier is considered
Dominant
Efficient
Sub-optimal
Risk-free
Jensen’s Alpha is most appropriate when
Total risk is relevant
Portfolio is well diversified
Returns are negatively skewed
Correlation is zero
Which risk measure is coherent under Artzner et al.’s axioms?
Variance
Standard deviation
Value at Risk
Conditional Value at Risk
Increasing the number of assets in a portfolio indefinitely will
Eliminate total risk
Eliminate systematic risk
Reduce unsystematic risk only
Increase beta
Portfolio risk reduction through diversification becomes ineffective when
Correlation approaches −1
Assets are identical
Correlation approaches +1
Number of assets increases
A 99% one-day VaR of ₹10 million implies that
Losses will never exceed ₹10 million
Expected loss is ₹10 million
Losses exceed ₹10 million on 1% of days
Maximum loss is capped at ₹10 million
The principal weakness of parametric VaR lies in its
High computational cost
Assumption of linear payoffs and normality
Dependence on historical data
Inability to aggregate risks
Economic capital differs from regulatory capital because economic capital
Is fixed by regulators
Covers expected losses
Reflects firm-specific risk profile
Is irrelevant for pricing
Which risk interaction most contributed to the 2008 financial crisis?
Market–operational risk
Credit–liquidity risk
Operational–strategic risk
Legal–reputational risk
Aggregating risks without considering correlation will most likely
Underestimate risk
Overestimate risk
Eliminate tail risk
Improve capital efficiency
Stress testing complements VaR because it
Uses historical volatility
Focuses on normal conditions
Captures low-probability, high-impact events
Replaces capital requirements
Enterprise Risk Management (ERM) creates value primarily by
Eliminating risks
Increasing leverage
Improving risk-adjusted decision making
Reducing compliance burden
Unexpected loss is best covered by
Provisions
Capital reserves
Insurance contracts
Risk transfer via derivatives
A major criticism of VaR is that it
Ignores diversification
Is not regulator-approved
Fails to describe tail severity
Overstates extreme losses
Firmwide risk management fails when
Risks are measured quantitatively
Risks are managed in silos
Capital is risk-based
Risk appetite is defined
Credit risk is asymmetric because
Gains and losses are equal
Upside is capped while downside is large
Defaults are continuous
Credit spreads are stable
Expected Credit Loss (ECL) under IFRS 9 is calculated as
PD + LGD
PD × EAD
PD × LGD × EAD
LGD × recovery rate
Credit migration risk primarily affects
Expected loss
Market value of credit instruments
Liquidity ratios
Operational capital
Structural credit risk models assume default occurs when
Cash flows decline
Firm value falls below debt obligations
Ratings are downgraded
Interest rates rise
Credit risk differs from market risk because credit losses are
Continuous
Normally distributed
Event-driven
Easily hedged
Concentration risk undermines diversification because
Correlation increases
Exposure becomes idiosyncratic
PD falls
LGD becomes zero
Credit derivatives primarily facilitate
Risk creation
Risk pricing
Risk transfer
Risk elimination
Higher recovery rates imply
Higher LGD
Lower LGD
Higher PD
No impact on loss
Credit risk capital is required mainly to cover
Expected losses
Unexpected losses
Operating expenses
Market volatility
Which borrower characteristic increases default risk most?
High leverage
Stable cash flows
Strong collateral
Long credit history
Basel I was criticized primarily for
Ignoring credit risk
Excessive complexity
Lack of risk sensitivity
High capital requirement
Pillar III of Basel II aims to
Increase capital
Improve supervisory control
Enhance market discipline through disclosure
Reduce operational risk
Basel III strengthened capital quality by emphasizing
Tier-3 capital
Hybrid instruments
Common Equity Tier 1
Subordinated debt
The countercyclical buffer is intended to
Increase profitability
Smooth credit booms and busts
Replace monetary policy
Reduce operational losses
Liquidity Coverage Ratio (LCR) addresses
Long-term solvency
Structural funding risk
Short-term liquidity stress
Credit concentration
Net Stable Funding Ratio (NSFR) discourages
Capital accumulation
Short-term wholesale funding
Credit growth
Asset diversification
Basel III was primarily a response to
Asian Financial Crisis
European Debt Crisis
Global Financial Crisis (2008)
COVID-19 shock
Risk-weighted assets (RWA) increase when
Asset quality improves
Risk exposure rises
Liquidity improves
Capital increases
One criticism of Basel norms is that they
Ignore risk
Are pro-cyclical
Eliminate lending
Remove bank competition
Basel implementation in India follows
A delayed adoption model
Full deviation model
RBI-calibrated phased approach
Market-driven approach
Operational risk losses typically exhibit
Normal distribution
Low severity, high frequency
Fat-tailed distribution
Symmetric distribution
Scenario analysis in operational risk is particularly useful when
Historical data is abundant
Loss events are rare
Systems are automated
Correlation is stable
Internal fraud is difficult to model because it
Is systematic
Has predictable frequency
Involves behavioral factors
Is insured
Liquidity risk materializes fastest through
Credit deterioration
Funding withdrawal
Capital erosion
Accounting losses
Market liquidity risk increases when
Trading volume rises
Bid-ask spread widens
Volatility declines
Information symmetry improves
A maturity mismatch primarily exposes a bank to
Market risk
Credit risk
Liquidity risk
Operational risk
Liquidity stress testing differs from solvency testing because it focuses on
Asset quality
Cash flow timing
Capital adequacy
Profitability
Funding liquidity risk and market liquidity risk are linked because
Both reduce capital
Asset sales can depress prices
Both eliminate diversification
Both are regulatory risks
A contingency funding plan is activated when
Capital falls
Normal funding sources fail
Profits decline
Credit ratings improve
Effective liquidity management ultimately supports
Risk elimination
Short-term profits
Institutional survival
Regulatory arbitrage
A coherent risk measure must satisfy all EXCEPT
Subadditivity
Translation invariance
Positive homogeneity
Mean-variance efficiency
Which condition ensures diversification benefit in portfolio risk aggregation?
Zero beta
Negative covariance
Identical expected returns
High volatility
Skewness in return distribution primarily affects
Expected return
Variance
Downside risk perception
Correlation
Why does CAPM fail empirically in many markets?
Risk-free rate is unstable
Beta does not fully explain returns
Investors are risk neutral
Markets are perfectly efficient
A portfolio optimized under variance may be suboptimal when returns are
Symmetric
Normally distributed
Fat-tailed
Independent
Downside-risk measures are preferred over variance because they
Reduce estimation error
Focus only on unfavorable outcomes
Ignore volatility
Eliminate tail risk
Correlation breakdown during crises implies
Improved diversification
Stable portfolio risk
Underestimated portfolio VaR
Lower systemic risk
Which assumption of Modern Portfolio Theory is most violated in practice?
Risk aversion
Rationality
Stable correlations
Diversification
Portfolio optimization under expected shortfall differs from VaR because it
Penalizes tail losses
Assumes normality
Ignores correlations
Uses historical means only
A negatively skewed distribution implies
Frequent small gains and rare large losses
Frequent losses
Stable returns
Low kurtosis
VaR fails as a risk measure mainly because it
Overstates losses
Violates subadditivity
Is computationally complex
Is regulator imposed
Expected Shortfall (CVaR) improves upon VaR by
Reducing confidence levels
Measuring average tail loss
Eliminating model risk
Removing volatility
Monte Carlo VaR is preferred when portfolios contain
Linear instruments
Plain vanilla bonds
Non-linear derivatives
Risk-free assets
Economic capital allocation supports value creation by
Maximizing leverage
Pricing risk correctly
Reducing disclosures
Avoiding regulations
Why did banks with high VaR still fail in 2008?
VaR ignored profitability
VaR ignored liquidity and tail dependence
VaR overstated risk
VaR replaced stress tests
Risk aggregation becomes unreliable when
Risks are independent
Correlations are stable
Tail dependence exists
Capital is adequate
Stress testing is forward-looking because it
Uses past losses
Assumes normal markets
Simulates hypothetical extreme scenarios
Uses accounting data
Capital buffers primarily exist to absorb
Expected losses
Operating costs
Unexpected losses
Interest expenses
A key challenge in firmwide risk management is
Risk measurement
Risk governance and culture
Risk modeling software
Regulatory reporting
Silo-based risk management fails because it
Underprices risk
Ignores risk interactions
Increases diversification
Enhances transparency
Credit risk losses are non-linear because
Exposure is fixed
Default is a binary event
Returns are continuous
LGD is zero
Structural credit models are most sensitive to
Interest rates
Asset volatility
Inflation
Accounting earnings
IFRS 9 differs from Basel ECL because IFRS 9 is
Backward-looking
Forward-looking and lifetime-based
Ignoring LGD
Ignoring staging
Credit concentration risk becomes systemic when
Borrowers diversify
Correlations rise during downturns
Recovery rates increase
PD declines
Credit derivatives reduce risk at system level only if
Counterparty risk is negligible
Risk is transferred outside banking system
Spreads decline
Liquidity is high
Basel I was inadequate because it
Ignored capital
Treated all corporate loans equally
Overweighted market risk
Eliminated credit risk
Basel II internal ratings-based (IRB) approach allowed banks to
Reduce disclosure
Use internal PD, LGD estimates
Ignore operational risk
Eliminate capital buffers
Basel III capital reforms emphasized quality because
Quantity alone failed during crisis
Banks had excess capital
Profits were low
Liquidity was abundant
Procyclicality of Basel norms implies
Capital rises during booms
Lending amplifies business cycles
Risk declines in recessions
Liquidity improves in downturns
Countercyclical buffers are activated when
GDP contracts
Credit growth is excessive
Banks incur losses
Liquidity dries up
Operational risk distributions typically show
Thin tails
Normality
Extreme skewness and kurtosis
Stability over time
The greatest challenge in operational risk modeling is
Data abundance
Rare but severe loss events
Stable correlations
Regulatory clarity
Why is insurance insufficient for operational risk?
Premiums are high
Moral hazard and exclusions exist
Losses are predictable
Claims are instant
Liquidity risk differs from solvency risk because liquidity risk concerns
Capital adequacy
Asset valuation
Timing of cash flows
Profitability
Market liquidity and funding liquidity reinforce each other because
Asset sales depress prices
Capital increases
Credit improves
Correlations decline
During crises, liquidity evaporates primarily due to
Inflation
Loss of confidence
Accounting losses
Regulation
LCR focuses on
Long-term funding
30-day stress scenario
Profitability
Capital buffers
NSFR discourages
Long-term lending
Stable deposits
Short-term wholesale funding
Capital adequacy
Liquidity stress testing assumes
Perfect markets
Normal funding access
Severe but plausible shocks
Zero correlations
Contingency funding plans fail when
Capital is high
Triggers are unclear
Markets are liquid
Assets are diversified
Liquidity hoarding during crises leads to
Market stabilization
Credit contraction
Lower spreads
Improved trust
Systemic liquidity risk arises when
Individual bank fails
Many institutions act defensively
Capital ratios improve
Central banks intervene
Central bank lender-of-last-resort function addresses
Solvency problems
Structural deficits
Temporary liquidity shortages
Credit defaults
Effective liquidity risk management ultimately supports
Risk elimination
Regulatory arbitrage
Institutional survival
Short-term ROE
The biggest lesson from global financial crises for risk management is that
Models must be complex
Capital alone is sufficient
Liquidity, correlation, and behavior matter
Risk can be eliminated
