CAIA Level 2 Quiz 20

The core-satellite portfolio approach in private equity involves allocating capital to a ‘core’ portfolio of established, lower-risk funds and a ‘satellite’ portfolio of newer, higher-risk, or experimental funds. The satellite portfolio is designed to capture opportunities arising from market shifts or novel strategies. The decision to allocate more to the satellite portfolio, thus embracing more ‘exploration,’ is influenced by the investor’s time horizon, available resources, and the anticipated market environment. A longer time horizon allows for the potential benefits of ‘real options’ (investing more in successful successor funds) to materialize. Greater resource availability (a larger reserve buffer) enables more exploration without jeopardizing the core portfolio. Finally, a market environment expected to be volatile or disruptive necessitates a broader spread of options (larger satellite allocation) to capture potential upside from unforeseen changes, whereas a stable environment favors a more focused, ‘exploitation’ strategy.

The CAIA curriculum emphasizes a holistic approach to fund manager selection, recognizing that while quantitative metrics are important, qualitative factors are equally crucial, especially in less liquid asset classes like private equity. The “team, team, team” adage highlights the critical role of the management team’s expertise, cohesion, and alignment of interests. A thorough due diligence process involves not just reviewing past performance but also assessing the team’s dynamics, decision-making processes, and potential conflicts of interest. The ability to build rapport and trust with the fund manager is paramount for a long-term Limited Partner (LP) relationship, as incompatible views can lead to instability. While personal comfort is important, it should not overshadow objective analysis of the team’s capabilities and the fund’s structure. Therefore, a comprehensive evaluation balances objective data with subjective assessments of the team’s quality and the LP’s comfort level.

The question tests the understanding of how different replication methodologies aim to capture hedge fund returns. A factor-based approach seeks to replicate hedge fund performance by identifying and investing in liquid securities that exhibit similar systematic risk exposures (betas) to the target hedge fund strategies. This approach leverages the idea that a significant portion of hedge fund returns can be explained by common risk factors. The payoff distribution approach, conversely, focuses on replicating the statistical distribution of returns, often through dynamic trading of underlying instruments. Algorithmic approaches use systematic trading rules derived from historical data. Therefore, a factor-based replication strategy would most directly aim to capture the beta exposures of hedge funds.

The core of actionable risk management, as highlighted in the provided text, is the ability and willingness of the risk manager (or designated authority) to actively reduce risk exposure. This involves having the mandate to enforce risk limits and a history of actually doing so when necessary. Simply having access to risk reports or understanding quantitative measures like VaR or DV01 is insufficient if the risk manager cannot or will not act on that information to curtail potential losses, especially when faced with resistance from portfolio managers.

This question assesses the understanding of how a Limited Partner (LP) typically structures their commitment to a private equity fund. The commitment is not a lump sum paid upfront but rather a pledge that is drawn down over time as the General Partner (GP) calls capital for investments and expenses. The commitment period is the timeframe during which the GP can make new investments, and the fund’s life extends beyond this for managing existing investments. Therefore, the LP’s obligation is to fund capital calls as they occur within the fund’s investment period, up to the total committed amount.

Convertible arbitrage strategies aim to profit from mispricings between a convertible bond and its underlying stock. The delta of a convertible bond measures its sensitivity to changes in the underlying stock price. A delta of 0.672, as calculated in the provided example, indicates that for every one-unit increase in the underlying stock’s parity value, the convertible bond’s value is expected to increase by 0.672 units. This sensitivity is crucial for hedging purposes. By shorting 0.672 shares of the underlying stock for every convertible bond held, an arbitrageur can create a delta-neutral position, reducing exposure to stock price movements and isolating potential mispricing opportunities. The other options represent incorrect hedging ratios or misinterpretations of delta’s meaning. A delta of 1.0 would imply a dollar-for-dollar relationship, typically seen when the convertible is deep in-the-money and trading almost like the stock itself. A delta of 0.50 is characteristic of an at-the-money option, not necessarily the optimal hedge ratio for a convertible bond across all states. A delta of 0.10 would suggest very little sensitivity to the underlying stock price, which is usually associated with out-of-the-money convertibles.

The provided exhibit indicates that the correlation between U.S. inflation and the energy commodity index is statistically significant at the 1% level (denoted by ‘a’). This suggests a strong positive relationship, meaning that as U.S. inflation rises, energy commodity returns tend to increase. While other commodity indices show some correlation with U.S. inflation, the energy sector exhibits the most robust and statistically significant positive association, making it the most direct beneficiary of rising U.S. inflation among the listed commodity categories.

This question tests the understanding of the relationship between farmland returns and macroeconomic factors. The provided text explicitly states that factor modeling of U.S. farmland shows a positive correlation with U.S. inflation, indicating it acts as a real asset. It also shows a negative association with interest rates and a positive association with economic growth. Therefore, an increase in inflation and economic growth, coupled with a decrease in interest rates, would generally lead to higher farmland returns.

The question tests the understanding of the ‘funding bias’ concept as described in the provided text. This bias arises because Funds of Hedge Funds (FoFs) act as a screening mechanism, discouraging or preventing ‘bad’ hedge funds from receiving capital. Consequently, the hedge funds that are observable in databases are likely to be a select group, funded either directly by skilled investors or indirectly through skilled FoFs. This means the observed universe of hedge funds is likely to be of higher quality than the universe that would exist without FoFs, leading to an upward bias in performance metrics derived from these databases. Options B, C, and D describe other potential biases or concepts but do not accurately represent the specific ‘funding bias’ caused by the presence of FoFs.

The S&P GSCI’s weighting methodology is primarily based on the average worldwide production of commodities over a five-year period. This approach inherently leads to a higher concentration in sectors with greater global production volumes, such as energy. In contrast, the Dow Jones-UBS Commodity Index (DJUBSCI) employs a more diversified approach by using a combination of liquidity and production measures, with liquidity having twice the influence of production. Crucially, the DJUBSCI imposes specific caps on individual commodity and sector allocations (15% and 33% respectively) to mitigate concentration risk. The Deutsche Bank Liquid Commodity Index (DBLCI) is characterized by its limited number of components (six), focusing on highly liquid commodities, which can also lead to concentration. The Diapason Commodities Index (DCI), with its 48 components, aims for broader diversification. Therefore, the S&P GSCI’s reliance on production volume as its sole weighting determinant is the primary reason for its significant energy sector concentration.

Limited partners (LPs) in private equity funds face the challenge of monitoring their investments due to the blind-pool nature of these funds and their illiquidity. While LPs cannot easily withdraw commitments like in traditional asset classes, proactive monitoring allows them to identify significant shortcomings early. This early detection can enable the LP to mitigate downside risk, potentially through restructuring the fund’s terms or by exiting the investment via the secondary market. The other options describe less direct or less impactful monitoring outcomes. While understanding the fund’s overall portfolio composition is crucial for diversification, it doesn’t directly address the LP’s ability to mitigate downside risk in an illiquid investment. Similarly, ensuring style discipline is a key monitoring objective, but the primary benefit of identifying style drift is to manage the associated risks, which ties back to protecting against downside. Finally, while LPs can create value through monitoring, the most immediate and critical benefit in the context of risk management is the ability to protect against potential losses by identifying and acting on severe issues.

Gamma measures the rate of change of delta with respect to changes in the underlying stock price. A higher gamma indicates that the delta is more sensitive to stock price movements, necessitating more frequent adjustments to maintain delta neutrality in a convertible arbitrage strategy. Conversely, a lower gamma implies less sensitivity, allowing for less frequent rebalancing. The provided gamma calculation of 0.00794 signifies that for every point change in the underlying stock’s price, the delta is expected to change by 0.00794. This sensitivity is crucial for managing the risk of the equity component of the convertible bond position.

The theory of normal backwardation, as proposed by Keynes, suggests that futures prices should generally be lower than the expected future spot price. This is to incentivize speculators to take long positions, thereby providing a risk premium to hedgers who are typically net short. The risk premium is the difference between the expected future spot price and the current futures price. In the given scenario, the current spot price is $90, and the three-month futures price is $95. The expected future spot price is $105. The risk premium is calculated as the expected future spot price minus the current futures price ($105 – $95 = $10). This positive risk premium indicates that speculators are compensated for taking on the risk of holding long positions, aligning with the concept of normal backwardation.

The core issue with appraisal-based real estate indices like the NCREIF NPI, as described in the text, is that appraisals tend to lag behind actual market price movements. This smoothing effect reduces the observed volatility and can mask true market dynamics. When a market experiences a sharp decline, as the REIT index did in late 2008 and early 2009, the appraisal-based index will likely show a less severe decline because the appraisals are based on older, higher valuations. Conversely, during a market recovery, appraisals may lag behind the rapid price increases. The question tests the understanding of this inherent characteristic of appraisal-based returns and how it manifests during periods of market stress and recovery, contrasting it with more market-driven indices.

Winsorizing is a statistical technique used to mitigate the impact of extreme values (outliers) in a dataset. In the context of quantitative equity strategies, where data like price-to-earnings ratios are normalized using z-scoring, outliers can disproportionately influence the final ranking or score. By setting extreme z-scores to a predefined threshold (e.g., capping values above 3 at 3 and below -3 at -3), Winsorizing ensures that these extreme data points do not unduly skew the results, leading to a more robust and reliable ranking of stocks. This is crucial for quantitative managers who rely on the aggregation of multiple standardized factors to construct their investment models.

The provided exhibit illustrates that while increasing the number of CTAs in a portfolio generally reduces the dispersion of returns around a benchmark index, the most significant gains in reducing the probability of underperforming a positive-returning industry are achieved with a smaller number of managers. Specifically, the exhibit suggests that by the time a portfolio includes five or six CTAs, the likelihood of experiencing a loss when the overall industry is profitable is substantially diminished. Beyond this point, further diversification yields diminishing marginal benefits in terms of reducing this specific risk, although it continues to contribute to overall portfolio stability and risk-adjusted returns.

The question tests the understanding of how macroeconomic factors influence commodity prices, specifically focusing on the indirect impact of interest rates through storage costs. Higher interest rates increase the opportunity cost of holding inventories for storable commodities. This increased cost makes it less attractive for market participants to hold physical commodities, leading to a decrease in demand for storage and consequently a downward pressure on current commodity prices. While higher interest rates can also negatively impact overall economic conditions and demand for commodities, the direct link through storage costs is a key mechanism discussed in the context of storable goods.

The question tests the understanding of how different types of inflation impact commodity returns, specifically focusing on the hedging properties. The provided text indicates that unexpected inflation generally has a larger positive impact on commodity returns than expected inflation, suggesting a stronger hedging capability against unexpected price changes. The research cited suggests that commodities with limited storability, like copper and heating oil, offer a better hedge against unexpected inflation because increased demand directly drives up their prices. Conversely, for highly storable commodities, price increases might only occur after inventories are depleted, diluting the immediate hedging effect. Therefore, the most accurate statement is that the hedging effectiveness of commodities against inflation is more pronounced when inflation is unanticipated.

The question tests the understanding of how different generations of commodity indices handle contract selection and weighting. First-generation indices typically use fixed weights and contract months. Second-generation indices, like the DBLCI Optimum Yield, introduce a dynamic element by selecting contracts based on implied roll yield within a defined window (13 months in this case), aiming to optimize roll returns. Third-generation indices, such as the UBS Bloomberg CMCI Active, go further by incorporating active commodity selection, which can be algorithmic or discretionary, and adjusting weights and tenors based on performance expectations. Therefore, the DBLCI Optimum Yield’s strategy of selecting contracts based on the highest implied roll yield from the next 13 months aligns with the characteristics of a second-generation index.

The question tests the understanding of how different asset allocation strategies, specifically momentum and mean reversion, are applied to commodities. Momentum strategies overweight commodities with rising prices and underweight those with falling prices. Mean reversion strategies do the opposite, reducing exposure to rising commodities and increasing exposure to falling ones. The prompt states that momentum models are typically short-term, while mean reversion strategies are based on longer time horizons (greater than one year). Therefore, a strategy that reduces exposure to commodities that have recently increased in price and increases exposure to those that have recently declined in price aligns with the principles of mean reversion, especially if the time horizon for price changes is considered to be longer than a typical momentum window.

This question tests the understanding of how spread trading strategies are initiated and exited based on statistical deviations from a mean. The scenario describes a situation where a 100-day statistic for a spread has moved significantly away from its historical average. A long entry into a spread is triggered when the statistic falls below a critical negative value (e.g., -2.75), indicating the numerator commodity has become relatively cheap. Conversely, a short entry occurs when the statistic rises above a critical positive value (e.g., 2.75), suggesting the numerator commodity is relatively expensive. Exiting a long spread position happens when the statistic reverts towards the mean (e.g., rises above zero), and exiting a short spread position occurs when the statistic reverts towards the mean (e.g., falls below zero). Therefore, if the 100-day statistic is at -3.5, it has fallen below the critical entry value of -2.75, triggering a long position in the spread.

The question probes the challenges associated with replicating non-investable hedge fund indices. The provided text highlights several key difficulties, including a lack of transparency in index construction, the presence of closed funds or funds nearing capacity, liquidity constraints due to lock-ups and redemption periods, and the inherent tracking error when attempting to replicate actively managed portfolios with traditional assets. The delay in reporting Net Asset Values (NAVs) also contributes to rebalancing lags. Option A correctly identifies the lack of transparency, the presence of closed or capacity-constrained funds, and the liquidity issues as primary obstacles. Option B is incorrect because while some indices might have a delay in reporting, it’s not the sole or primary reason for replication difficulty compared to the structural issues mentioned. Option C is incorrect as the difficulty isn’t primarily about the cost of replication but rather the feasibility and accuracy due to the nature of the underlying funds and index construction. Option D is incorrect because while tracking error is a consequence, the fundamental reasons for it stem from the underlying characteristics of the non-investable indices and their components, not just the choice of replication strategy itself.

The NCREIF National Property Index (NPI) is a prime example of an appraisal-based real estate index. Appraisal-based indices rely on professional valuations of properties, typically conducted periodically (e.g., quarterly or annually). These appraisals provide an estimate of the property’s market value, which is then used to calculate returns. The NPI specifically uses appraisals to overcome the illiquidity of real estate, where frequent market transactions are not available to accurately capture short-term price movements. Transaction-based indices, on the other hand, derive their data from actual property sales, which can be more volatile and may not reflect the true underlying value between transactions.

The question probes the understanding of how diversification benefits are achieved in managed futures strategies, specifically referencing the provided exhibit. Exhibit 31.5C details the exposures of CTA indices to various risk factors. The explanation highlights that the low correlation of CTA strategies with traditional asset classes, as indicated by the correlation figures in the exhibit (e.g., low correlation with equities and bonds), is the primary driver of diversification benefits. While low factor exposures (betas) are also important, the exhibit explicitly states that these low exposures are the *basis* for diversification benefits, implying that the low correlations are the direct manifestation of this benefit in a portfolio context. The low volatility of discretionary CTA indices, as mentioned in the text, is a characteristic but not the primary source of diversification from a portfolio perspective. The presence of stop-loss orders is a risk management technique, not a direct driver of diversification benefits.

The question tests the understanding of how short selling costs and rebates impact the overall return of a long/short equity hedge fund. In the provided scenario, the fund shorts $50 of Company ABC, which pays a $1 dividend. The short rebate is 1.5%, composed of a 2% interest earned on proceeds and a 0.50% cost to borrow. The net effect of the short position on the fund’s return is calculated by considering the price change of the shorted security, the interest earned on the proceeds, the cost of borrowing, and the dividend paid to the buyer of the borrowed shares. The price appreciation of the shorted security (ABC declining 5%) contributes positively to the short seller’s return. The interest earned on the proceeds of the short sale is 2% of $50, which is $1. The cost of borrowing the shares is 0.50% of $50, which is $0.25. The dividend paid to the buyer of the borrowed shares is $1. Therefore, the net return from the short position is the price appreciation ($2.50), minus the dividend paid (-$1.00), plus the interest earned on proceeds ($1.00), minus the cost of borrowing (-$0.25), resulting in a net gain of $2.25. The question asks for the net impact of the short position on the fund’s performance, which is this $2.25 gain.

The question probes the understanding of how different structures for allocating capital to Commodity Trading Advisors (CTAs) handle inter-manager margining and performance separation. Example 2 describes a structure where a single SPV sets up subaccounts for different CTA managers under one legal entity. This setup allows for net margining across these subaccounts because they are all owned by the same legal entity, and there are no firewalls between them. Consequently, the performance and values of these accounts are netted together. Example 3, conversely, uses separate SPVs for each manager, creating distinct legal entities and thus separating performance and preventing net margining across managers. Example 4, the Protected Cell Company (PCC), also provides firewalls between managers, separating performance. Platforms, while offering consolidated reporting, also maintain separation between individual manager accounts. Therefore, the structure described in Example 2 is the only one that explicitly allows for net margining across different trading managers due to the single legal entity ownership and lack of internal firewalls.

The provided exhibit indicates that larger endowments ($1 billion to $100 million) generally outperformed smaller endowments ($50 million) across most asset classes, including non-U.S. equity, public real estate, private real estate, hedge funds, private equity, and natural resources. The exhibit also shows a significantly lower standard deviation of annual returns for larger endowments (3.7%) compared to smaller ones (6.4%), suggesting better risk management or diversification. The text further elaborates on the ‘first-mover advantage’ and superior manager selection skills of large endowments, which often gain access to top-performing managers in alternative investments that may be closed to newer investors. This suggests that the superior performance of larger endowments is not solely due to a higher allocation to a single asset class but rather a combination of strategic allocation, superior manager selection, and potentially better risk-adjusted returns derived from these factors.

The Public Market Equivalent (PME) methodology aims to assess private equity fund performance by simulating an investment in a public market index using the fund’s actual cash flow schedule. By substituting the fund’s Net Asset Value (NAV) with the calculated terminal value from this public market simulation, a private equity equivalent public index Internal Rate of Return (IRR) is derived. This resulting IRR represents the hypothetical performance if the capital had been allocated to the chosen public index instead of the private equity fund, providing a cash-weighted comparison.

The regression analysis presented in Exhibit 21.4 indicates that U.S. CPI has a statistically significant positive coefficient (3.203890) and a very low probability (0.0000), signifying that farmland returns act as a strong hedge against inflation. This means that as the general price level rises, the value of farmland tends to increase, preserving the purchasing power of the investment. While industrial production also shows a positive relationship, its coefficient is smaller, and the significance of CPI highlights its primary role as an inflation hedge. Yield to worst, representing interest rates, has a negative coefficient, suggesting that higher interest rates are associated with lower farmland returns, likely due to increased discount rates impacting present values. The U.S. Dollar Index (DXY) also shows a positive association, but the CPI’s impact is more directly linked to the concept of inflation hedging.

The Capital Asset Pricing Model (CAPM) posits that the expected return of an asset is determined by the risk-free rate, the asset’s systematic risk (beta), and the market risk premium. While CAPM is theoretically applicable to real assets, its practical application to private equity (PE) funds faces challenges due to the model’s underlying assumptions. Specifically, CAPM assumes investors hold well-diversified portfolios, which may not accurately reflect the investment strategies of all PE investors. Furthermore, PE investments often involve illiquidity, lack of transparency, and transaction costs, which deviate from CAPM’s assumptions of tradable assets and efficient markets. Therefore, while the CAPM provides a theoretical framework, its direct application to PE requires careful consideration and potential adjustments to account for these market specificities. The question asks about the primary challenge in applying CAPM to PE, and the difficulty in accurately estimating the required inputs, particularly beta, due to the unique characteristics of PE investments, is a significant hurdle.