Financial Risk Manager (FRM) Current Issues and Investment Risk Syllabus

Current Issues and Investment Risk syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Current Issues and Investment Risk in Financial Risk Manager (FRM), not a summary of it.

1. 1

   Investment and Portfolio Risk Management

   4 topics

   • Factor investing and risk-factor allocation
   • Portfolio construction with risk constraints
     • Risk parity and risk budgeting
     • Constraints, transaction costs, and rebalancing
   • Risk monitoring for institutional portfolios
     • Tracking error and active risk
     • VaR and stress testing for funds
   • Liquidity and leverage in investment management
2. 2

   Hedge Funds and Alternative Investments

   4 topics

   • Hedge fund strategies and risk profiles
   • Performance measurement and survivorship bias
   • Risk-sharing, fees, and the convexity of incentives
   • Illiquidity and valuation of alternative assets
3. 3

   Climate and Emerging Risk

   3 topics

   • Physical and transition climate risk
     • Climate scenario analysis and stress testing
     • Disclosure frameworks and supervisory expectations
   • Cyber risk and operational resilience trends
   • Geopolitical risk and supply chain disruption
4. 4

   Technology, Data, and Machine Learning in Risk

   4 topics

   • Machine learning fundamentals for risk applications
     • Supervised, unsupervised, and reinforcement learning
     • Overfitting, interpretability, and model governance
   • Artificial intelligence in credit and fraud detection
   • Big data and alternative data in risk modeling
   • Distributed ledger technology and digital assets
5. 5

   Financial Innovation and Market Structure

   4 topics

   • Stablecoins, central bank digital currencies, and tokenization
   • Replacement of LIBOR and the transition to risk-free rates
   • Nonbank financial intermediation and shadow banking
   • Lessons from recent banking stress and contagion episodes

Current Issues and Investment Risk flashcards for Financial Risk Manager (FRM)

23 of 51 cards from the Current Issues and Investment Risk deck — real questions with worked answers.

1. What is factor investing, and what distinguishes a risk factor from an individual asset?

   Factor investing is an approach that targets systematic, persistent drivers of return (factors) rather than individual securities. A risk factor is a common source of return and risk (e.g., market, size, value, momentum, quality, low-volatility) that explains co-movement across many assets and earns a risk premium, whereas an individual asset's return is a combination of factor exposures plus idiosyncratic (diversifiable) risk.

2. In a linear multi-factor model, how is an asset's expected excess return expressed?

   As a linear combination of factor exposures times factor risk premia: $$E[R_i] - R_f = \sum_{k=1}^{K} \beta_{i,k}\,\lambda_k$$ where $\beta_{i,k}$ is asset $i$'s sensitivity (loading) to factor $k$ and $\lambda_k$ is the premium for factor $k$.

3. Name the classic Fama-French and Carhart factors used in factor-based risk allocation.

   Market (MKT, excess market return), Size (SMB, small minus big), Value (HML, high minus low book-to-market), Momentum (WML/UMD, winners minus losers from Carhart), and later additions Profitability (RMW) and Investment (CMA) in the Fama-French five-factor model.

4. What is the difference between asset allocation and risk-factor allocation?

   Asset allocation distributes capital across asset classes (equities, bonds, real estate). Risk-factor allocation distributes risk across underlying systematic factors, recognizing that nominally distinct asset classes often share common factor exposures (e.g., credit and equity both load on growth), giving a clearer view of true diversification.

5. What is the objective function of mean-variance portfolio construction with a risk-aversion parameter?

   Maximize utility $$U = w^{\top}\mu - \frac{\lambda}{2}\,w^{\top}\Sigma w$$ where $w$ is the weight vector, $\mu$ expected returns, $\Sigma$ the covariance matrix, and $\lambda$ the risk-aversion coefficient. The first-order condition gives optimal weights $w^{*} = \frac{1}{\lambda}\Sigma^{-1}\mu$.

6. In portfolio construction, what is a risk budget and how does it relate to a constraint?

   A risk budget allocates a portfolio's total risk (e.g., volatility or tracking error) among positions, factors, or strategies. A risk constraint then caps that allocation, for example limiting total ex-ante volatility, tracking error to a benchmark, or any single factor's marginal contribution to risk.

7. Define marginal contribution to risk (MCTR) and component contribution to risk for a portfolio.

   For portfolio volatility $\sigma_p = \sqrt{w^{\top}\Sigma w}$, the marginal contribution to risk of asset $i$ is $$\text{MCTR}_i = \frac{\partial \sigma_p}{\partial w_i} = \frac{(\Sigma w)_i}{\sigma_p}.$$ The component contribution is $w_i \cdot \text{MCTR}_i$, and these components sum to total volatility $\sigma_p$ (Euler's theorem for homogeneous functions).

8. What is a risk-parity portfolio?

   A construction in which each asset (or factor) contributes an equal share of total portfolio risk, i.e., $w_i \cdot \text{MCTR}_i$ is equalized across all $i$. It often overweights low-volatility assets like bonds and is frequently combined with leverage to reach a target return.

9. List key elements of a risk-monitoring framework for an institutional portfolio.

   Ex-ante risk estimates (volatility, VaR, expected shortfall), tracking error vs. benchmark, factor and sector exposure limits, concentration and liquidity limits, stress tests and scenario analysis, backtesting of risk models, and ongoing limit-breach reporting and escalation.

10. What is the difference between ex-ante and ex-post (realized) tracking error?

    Ex-ante tracking error is a forward-looking forecast of the standard deviation of active (portfolio minus benchmark) returns derived from a risk model. Ex-post tracking error is the realized standard deviation of historical active returns. Divergence between the two signals model or exposure misestimation.

11. How is the liquidity-adjusted Value at Risk (LVaR) typically constructed?

    By adding a liquidity cost component to standard VaR, e.g., $$\text{LVaR} = \text{VaR} + \frac{1}{2}\,(\text{spread})\cdot V$$ where the half-spread times position value $V$ captures the cost of liquidating, sometimes extended for the price impact of trading over multiple days under stress.

12. How does leverage affect a portfolio's return and risk, all else equal?

    Leverage scales both expected excess return and volatility by the leverage factor $L$: if unlevered excess return is $r$ and volatility $\sigma$, levered figures are approximately $L\cdot r$ and $L\cdot\sigma$ (the Sharpe ratio is unchanged), but leverage introduces funding risk, margin-call/forced-deleveraging risk, and a non-linear path to insolvency.

13. What is a margin spiral (leverage-liquidity feedback loop)?

    A destabilizing loop where falling asset prices trigger margin calls, forcing leveraged investors to sell, which pushes prices down further and tightens funding, prompting more margin calls and fire sales. It links market liquidity and funding liquidity, amplifying drawdowns during stress.

14. Compare market liquidity risk and funding liquidity risk.

    Market liquidity risk is the risk that an asset cannot be sold quickly without a large price concession (wide bid-ask, low depth). Funding liquidity risk is the risk that an investor cannot meet cash obligations or roll over financing. The two reinforce each other during crises (liquidity spirals).

15. What is the typical risk profile of an equity long/short hedge fund strategy?

    It holds long positions in undervalued and short positions in overvalued equities, retaining some net market (beta) exposure. Risks include residual market beta, factor and sector tilts, short-squeeze and crowding risk, and basis risk between long and short legs; returns aim to capture alpha plus partial market exposure.

16. Describe the risk profile of a fixed-income arbitrage hedge fund strategy.

    It exploits small pricing discrepancies between related fixed-income instruments using high leverage. Risk profile: typically small, steady gains with rare but severe losses (negative skew, fat left tail), high sensitivity to liquidity and credit spreads, and model/basis risk, exemplified by the LTCM collapse.

17. What is a global macro hedge fund strategy and its main risks?

    A directional strategy taking long/short positions across currencies, rates, equities, and commodities based on macroeconomic views, often using derivatives and leverage. Main risks: timing of macro calls, leverage and liquidity risk, and concentrated directional bets that can produce large drawdowns.

18. What is survivorship bias in hedge fund and mutual fund performance measurement?

    The upward bias in measured average performance that arises when failed or poorly performing funds drop out of a database, leaving only survivors. Because losers disappear, historical index returns and average fund performance are overstated, distorting risk and return estimates.

19. What is backfill (instant history) bias in hedge fund databases?

    The bias arising when a fund, upon joining a database, retroactively adds its prior (typically favorable, since funds report after early success) track record. This inflates reported returns because incubated/unsuccessful funds with poor early results never get backfilled.

20. Define the Sharpe ratio and state its key limitation for non-normal returns.

    $$\text{Sharpe} = \frac{E[R_p] - R_f}{\sigma_p}$$ the excess return per unit of total volatility. Limitation: it uses standard deviation, which penalizes upside and downside equally and fails to capture negative skew and fat tails common to hedge funds and illiquid strategies.

21. Define the Sortino ratio and the Information ratio.

    Sortino ratio: $\dfrac{E[R_p]-T}{\sigma_{\text{down}}}$, excess return over a target $T$ per unit of downside deviation. Information ratio: $\dfrac{E[R_p]-E[R_b]}{\text{TE}}$, active return over benchmark $b$ per unit of tracking error TE, measuring consistency of active management skill.

22. How do the standard '2 and 20' hedge fund fees, with high-water marks, create convexity in manager incentives?

    With a 2% management fee plus 20% performance fee, the incentive fee resembles a call option on fund assets: the manager shares gains but not losses. This convex payoff (long an option) rewards volatility, incentivizing risk-taking, especially when far below the high-water mark or when seeking to recover.

23. What is a high-water mark and how does it affect risk-taking incentives?

    A high-water mark is the peak NAV above which a manager must recover before earning new performance fees. After large losses, a fund deep below its high-water mark earns no incentive fee until recovery, which can encourage either excessive risk-taking ('gambling for resurrection') or closing the fund.

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