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FRM Updated
Should a portfolio construction process use VaR, CVaR, or variance as its risk measure?
VaR, CVaR, and variance answer different risk questions, so the right choice depends on portfolio shape and investor preferences...
Why is VaR or CVaR on high-frequency returns tricky?
High-frequency VaR can be distorted by microstructure noise, tick effects, liquidity, and dependence between observations...
Why do cross-gammas make delta-gamma VaR more complicated?
Cross-gammas capture second-order interaction between risk factors, which can matter in nonlinear multi-factor books...
Why is Expected Shortfall harder to estimate by simulation than VaR in heavy-tailed portfolios?
Expected Shortfall is simulation-sensitive because it averages extreme losses rather than only locating a quantile cutoff...
What does the Kupiec proportion-of-failures test check in VaR backtesting?
The Kupiec test checks whether the total VaR exception count matches the expected breach rate for the confidence level...
What makes CDS spread scenarios harder than ordinary equity return scenarios?
CDS spread scenarios are hard because credit data can be sparse, sticky, and heavily affected by changing issuer fundamentals...
Why are overlapping returns tricky when estimating multi-day VaR?
Overlapping returns give more multi-day observations, but those observations are dependent and can overstate precision...
How should I think about VaR for a portfolio that includes options?
Option portfolio VaR often needs greek approximations or full revaluation because delta-neutral does not mean risk-free...
Should VaR for a portfolio of funds use look-through holdings or fund-level returns?
Fund-level VaR captures realized fund behavior, while look-through VaR captures current underlying exposure when data is reliable...
Why is time-scaling Cornish-Fisher VaR more delicate than scaling normal VaR?
Cornish-Fisher VaR is harder to scale because skewness and kurtosis do not behave like volatility under naive time scaling...
What is the simplest way to remember VaR and CVaR formulas across distributions?
VaR is a distribution quantile, while CVaR is the average loss beyond that quantile under the chosen tail convention...
What common mistakes show up in a Monte Carlo VaR implementation?
Monte Carlo VaR can fail through inconsistent returns, bad dependence inputs, and weak portfolio revaluation...
Can Extreme Value Theory be used for a portfolio that contains options?
EVT can be used with option portfolios, but the tail model must respect nonlinear risk drivers and revaluation...
What are the main approaches to stress testing a portfolio?
Stress testing explores severe but plausible scenarios through historical replays, factor shocks, and reverse stress tests...
What are the core steps in a Monte Carlo VaR calculation?
Monte Carlo VaR builds a loss distribution by simulating scenarios, revaluing the portfolio, and reading a tail percentile...
When estimating tail risk, should I fit the whole return distribution or only the tail?
Tail risk estimation balances stable full-sample fitting against focused but noisier tail-only modeling...
Why is CVaR usually easier than VaR to use in portfolio optimization?
CVaR is often easier to optimize because it averages tail losses instead of focusing only on a cutoff quantile...
How do historical, variance-covariance, and Monte Carlo VaR differ?
The three VaR methods differ by how they build the loss distribution: history, assumptions, or simulation...
Can I annualize Expected Shortfall with square-root-of-time the same way I annualize volatility?
Expected Shortfall does not scale as cleanly as volatility, so annualization depends heavily on distribution assumptions...
Why do desks still use Black-Scholes greeks for risk management when better models exist?
Black-Scholes greeks remain popular because they are a common operational risk language even when richer models are used elsewhere...
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