Risk Measurement and Analytics Flashcards
6 cards from real Financial Risk Management practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Risk Measurement and Analytics flashcards as text
Monte Carlo simulation in risk management is used to:
Answer: Generate thousands of random scenarios to build a loss distribution for complex portfolios
Monte Carlo simulation generates many random market paths using assumed statistical processes to build empirical distributions of portfolio losses, especially for non-linear instruments.
What is 'volatility clustering' and why is it relevant to risk models?
Answer: The empirical pattern where high-volatility periods tend to be followed by more high-volatility periods
Volatility clustering means risk is time-varying; GARCH models capture this by allowing volatility to evolve dynamically based on recent market observations.
Risk-adjusted return on capital (RAROC) is used to:
Answer: Measure the return earned per unit of economic capital, enabling comparison across business lines
RAROC = risk-adjusted return / economic capital, enabling apples-to-apples comparison of profitability across activities with different risk profiles.
What is a copula in financial risk modeling?
Answer: A statistical function that links marginal distributions to model joint dependency structures
A copula separates the modeling of marginal distributions from their dependency structure, allowing flexible modeling of joint extreme events between risk factors.
The 'Sharpe ratio' measures:
Answer: Risk-adjusted return defined as excess return per unit of total volatility
Sharpe ratio = (Portfolio Return − Risk-Free Rate) / Portfolio Standard Deviation, measuring return earned per unit of total risk taken.
Which of the following best describes 'model validation' in financial risk management?
Answer: An independent review to assess whether a risk model is conceptually sound and performs as intended
Model validation is an independent internal process evaluating model design, data quality, assumptions, and performance to identify limitations and ensure fitness for purpose.
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