CAIA Level 2 Quiz 107

The provided exhibits demonstrate that CTA strategies, particularly systematic ones, tend to perform best during periods of low S&P 500 volatility (Exhibit 31.8A) and when the changes in S&P 500 volatility are moderate (Exhibit 31.8B). Conversely, they perform poorly during the worst months for equities and high-yield bonds, and their performance is mixed during the best months for equities and high-yield bonds, often showing negative returns during the best months for high-yield bonds. The question asks about the typical performance of CTAs during periods of high equity market volatility. Exhibit 31.8A shows that CTA performance is generally lower during periods of high S&P 500 volatility compared to low volatility periods. Specifically, the Barclay Trader Index CTA shows a return of 0.23% during the highest volatility, compared to 1.03% during the lowest volatility. This indicates a negative correlation or at least a significant underperformance during high volatility. Therefore, the statement that CTAs generally underperform during periods of high equity market volatility is supported by the data.

This question tests the understanding of how commodity futures are used to hedge against price fluctuations in a producer’s input costs. An airline’s primary input cost is fuel. Therefore, to hedge against rising fuel prices, an airline would benefit from a strategy that profits when fuel prices increase. Buying call options on jet fuel provides this benefit, as the option’s value increases with the price of jet fuel, offsetting the higher cost of fuel for the airline’s operations. Selling futures would create an obligation to sell at a fixed price, which would be disadvantageous if fuel prices rise significantly. Buying futures would lock in a purchase price, which is a form of hedging but less flexible than options for managing potential downside risk while allowing for upside participation. Selling put options would expose the airline to losses if fuel prices fall.

The question probes the understanding of how behavioral biases can influence market prices, specifically in the context of managed futures and trend-following strategies. The provided text highlights that anchoring and the disposition effect can lead to underreaction to new information, causing prices to initially move in a trend. Subsequently, biases like herding, feedback, confirmation, and representativeness can lead to overreaction and price overshoot. The core concept tested here is the sequence of behavioral influences on price movements and their impact on trend-following strategies. Option A correctly identifies the initial underreaction driven by anchoring and disposition effect, followed by overreaction due to herding and confirmation biases, which aligns with the described market dynamics. Option B incorrectly suggests that overreaction precedes underreaction. Option C misattributes the causes of underreaction and overreaction. Option D incorrectly links the disposition effect to overreaction and herding to underreaction.

The Modified Internal Rate of Return (MIRR) is a more robust performance metric than the Internal Rate of Return (IRR) because it accounts for the time value of money and the reinvestment rate of cash flows. Specifically, it considers the investor’s cost of capital and the rate at which interim cash flows can be reinvested. The formula provided in the text, \(\left(\frac{\sum_{t=0}^{T} D_t \times (1+R_{RT})^{T-t}}{\sum_{t=0}^{T} C_t (1+C_{oC})^t}\right)^{1/T} – 1 = MIRR_T\), explicitly incorporates these factors. The Total Value to Paid-in (TVPI) and Distribution to Paid-in (DPI) ratios, while useful, do not account for the time value of money. The Residual Value to Paid-in (RVPI) ratio focuses solely on unrealized investments and also omits the time value of money.

The question tests the understanding of how pension plan sponsors balance the dual objectives of maximizing investment returns to reduce future contributions and minimizing funding risk. Liability-Driven Investing (LDI) is a strategy specifically designed to align the pension plan’s assets with its future liabilities. By matching the duration and cash flows of assets to the plan’s obligations, LDI aims to reduce volatility in the funded status and the required contributions. While seeking high returns is a goal, it is often secondary to ensuring the plan can meet its obligations, especially in the context of underfunded plans or increasing regulatory scrutiny. Therefore, LDI directly addresses the second goal of minimizing underfunding or surplus risk, which in turn supports the first goal by creating a more stable funding environment.

The question tests the understanding of how foreign exchange risk impacts the total return of a cross-border real estate investment from the perspective of a domestic investor. The provided text explains that the variance of an international real estate investment’s return, when measured in the domestic currency, is influenced by the variance of the foreign exchange rate, the variance of the asset’s return in its local currency, and the covariance between these two factors. Specifically, Equation 20.8 illustrates this decomposition. Therefore, a U.S. investor’s total risk exposure includes both the underlying property’s performance in its local currency and the fluctuations in the USD-to-Euro exchange rate. The covariance term highlights that the relationship between currency movements and asset performance can either mitigate or exacerbate the overall risk.

Operational due diligence aims to assess the robustness and integrity of a hedge fund’s internal processes and management. While understanding the manager’s personality and background is part of the overall assessment (triangulation), the core of operational due diligence focuses on the infrastructure and systems that support the investment strategy. High personnel turnover, especially in key operational or senior management roles, can signal underlying issues with management, business culture, or operational stability, directly impacting the fund’s ability to execute its strategy and manage risk. Therefore, significant personnel turnover is a critical red flag that warrants deep investigation during operational due diligence.

The provided text emphasizes that due diligence in private equity fund selection is primarily an information-gathering and evaluation process, not a decision-making tool itself. While it helps to filter out inferior funds, the final investment decision should incorporate the due diligence findings alongside the overall portfolio composition and strategic fit. Therefore, a fund manager’s decision to commit capital should consider the fund’s quality as evaluated through due diligence, but this evaluation is an input to a broader decision-making framework that includes portfolio construction.

The core of trend-following strategies, as described, is their reliance on directional market movements. They profit from sustained trends and incur losses in markets characterized by randomness or a lack of clear direction. This behavior is directly linked to their sensitivity to price changes, often described as being ‘long gamma’ in options terminology. Gamma measures the rate of change in delta (directional exposure) as the underlying asset’s price moves. A trend-follower’s delta increases as the market moves in their favor and decreases as it moves against them, mirroring the behavior of a long gamma position. This is distinct from a direct exposure to volatility itself. While CTAs might perform well during periods of high or increasing volatility, this is often a byproduct of these periods coinciding with strong directional trends, not an inherent ‘long volatility’ position.

The BPEP model, as described, utilizes a probabilistic approach to project cash flows from private equity funds. This involves assigning probabilities to different exit values (minimum, median, maximum) and exit dates (earlier, median, latest). The key insight is that the probabilities for exit dates do not necessarily sum to 1. This allows for the possibility that a cash flow might not occur at all, reflecting the inherent uncertainty in private equity realizations. Therefore, the sum of probabilities for exit dates being less than or equal to 1 is a deliberate feature of the model to capture this uncertainty.

The scenario describes a situation where a private equity fund manager, using a deal-by-deal carried interest calculation, realizes a profit on one investment (Investment A) but incurs a total loss on another (Investment B). Under a deal-by-deal structure, the general partner (GP) receives their carried interest on profitable deals independently, even if the overall fund performance is negative. In this case, Investment A generated a profit of €40 million (€90 million sale price – €50 million cost). With an 80/20 carry split (80% to Limited Partners (LPs), 20% to GP), the GP receives €8 million (20% of €40 million). The LPs receive €32 million (80% of €40 million). However, Investment B was a total loss of €50 million. Therefore, the LPs’ net outcome is €32 million (from A) – €50 million (from B) = -€18 million. The GP’s net outcome is €8 million (from A) + €0 million (from B) = €8 million. This highlights the potential misalignment of interests with deal-by-deal carry, as the GP profits while the LPs experience a net loss on the fund’s overall performance. A fund-as-a-whole calculation would require the total fund performance to be positive before any carry is distributed, meaning the GP would receive €0 in this scenario.

The question tests the understanding of the fundamental advantages of real estate as an investment. The ability to provide absolute returns, hedge against inflation, and offer diversification benefits are core portfolio advantages. While cash flow and tax advantages are also benefits, they are often considered secondary or derived from the primary characteristics. The question asks for the most fundamental portfolio-related advantages, which are directly tied to risk management and return generation in a diversified portfolio context. The other options, while true benefits, are not as universally cited as the primary portfolio-enhancing attributes in the context of risk and return optimization.

This question tests the understanding of how tactical asset allocation (TAA) differs from strategic asset allocation (SAA) in the context of portfolio management for endowments and foundations. SAA focuses on maintaining long-term target weights through regular rebalancing. TAA, on the other hand, intentionally deviates from these targets to capitalize on short-term market inefficiencies or to mitigate risk. The scenario describes a situation where an endowment’s equity allocation has drifted significantly from its target due to market movements. The decision to rebalance back to the target is a core tenet of SAA. TAA would involve actively adjusting allocations based on short-term forecasts of asset class performance, potentially overweighting underpriced assets and underweighting overvalued ones, with a shorter time horizon for these forecasts (quarters to a year) compared to SAA (10-20 years). Therefore, the action described aligns with the principles of SAA, not TAA.

The core benefit of depreciation for a taxable real estate investor, especially when the asset is appreciating or holding steady in value, is the ability to defer taxes. This deferral is akin to an interest-free loan from the government because the reduced tax payments in earlier periods can be reinvested, and the present value of future tax payments is lower than the immediate tax savings. While depreciation reduces taxable income in the current period, it doesn’t eliminate the tax liability entirely; rather, it shifts it to a later date, typically upon sale of the asset, where it’s taxed as recaptured depreciation. The calculation provided in the text demonstrates that the present value of the tax savings from depreciation is less than the total tax savings over the asset’s life, highlighting the time value of money and the benefit of deferral. Therefore, the most accurate description of the primary advantage is the deferral of taxes, which increases the present value of future cash flows.

The question tests the understanding of the historical evolution of endowment management strategies and the shift from income-only spending to total return. Initially, endowments focused on fixed-income securities to generate income, which was then distributed. This approach maintained the nominal value of the corpus but often resulted in low real returns. The shift towards total return, influenced by publications and legislation like the Uniform Management of Institutional Funds Act, allowed for a broader asset allocation including equities, aiming to grow the corpus in real terms while meeting spending needs. The scenario describes a situation where a portfolio’s yield is insufficient to cover spending and inflation, necessitating a move towards a total return approach that includes capital appreciation. This aligns with the historical transition from a yield-focused strategy to one that considers both income and capital gains to sustain the endowment’s real value over time.

The scenario describes a situation where a private equity fund manager, using a deal-by-deal carried interest calculation method, realizes a profit on one investment (Investment A) but incurs a total loss on the fund due to another investment (Investment B) failing. Under a deal-by-deal structure, the general partner (GP) receives carried interest on profitable deals even if the overall fund performance is negative. In this case, the GP receives 20% of the €40 million profit from Investment A, which is €8 million. The limited partners (LPs) contributed €100 million, received €50 million back from Investment A (their initial capital), and lost their entire €50 million investment in Investment B. Therefore, the LPs’ net return is €50 million received minus €100 million invested, resulting in a €50 million loss. However, the question asks for the distribution of the *realized* profit from Investment A. The €40 million profit from Investment A is split 80/20 between LPs and the GP. The LPs receive 80% of the profit, which is €32 million, and the GP receives 20%, which is €8 million. The explanation should clarify that the deal-by-deal method allows for carry distribution on profitable deals independently of the overall fund performance, leading to the GP receiving carry despite the fund’s net loss.

The passage highlights that while cash and risk-free debt can serve as a straightforward hedge against market downturns, a significant allocation to these assets can diminish the portfolio’s expected long-term return. The text explicitly states that aggressive endowment and foundation investors typically maintain low allocations to these defensive assets, indicating they do not rely on them as a primary tail-risk hedge. The core idea is that the trade-off between reduced volatility and lower expected returns makes an over-reliance on cash and fixed income less appealing for investors seeking long-term wealth accumulation.

The question probes the understanding of risk profiles in private equity, specifically comparing European and U.S. buyout funds based on the provided exhibit. The exhibit shows that U.S. buyout funds have a lower probability of a loss (22%) compared to European buyout funds (16%). However, the exhibit also indicates that U.S. buyout funds have a higher probability of a multiple above 2 (21% vs. 22%) and a higher probability of a multiple above 3 (5% vs. 6%). Crucially, the return-to-risk ratio for U.S. buyout funds (2.0) is significantly higher than for European buyout funds (0.7), and the Sortino ratio for U.S. buyout funds (240.2) is also substantially higher than for European buyout funds (3.3). This suggests that while both exhibit risk, U.S. funds offer a more favorable risk-adjusted return. The probability of total loss is 0% for both, and the average loss given a loss is comparable. Therefore, the most accurate statement, considering the overall risk-return trade-off as indicated by the ratios, is that U.S. buyout funds generally present a more favorable risk-return profile despite a slightly higher probability of loss.

Convertible arbitrage strategies aim to isolate the equity option component of a convertible bond while hedging out other risks. When a hedge fund manager purchases a convertible bond and simultaneously shorts the underlying stock (delta hedging), they are primarily exposed to changes in volatility, interest rates, and credit spreads. While shorting the stock provides some hedge against credit risk (as widening spreads often correlate with declining stock prices), it’s an imperfect hedge. To more effectively isolate the equity option and hedge out the credit risk, an arbitrageur would typically seek to hedge the fixed-income component. Selling a straight bond of the same issuer is a direct method to hedge credit risk, as it isolates the credit exposure. Alternatively, using credit default swaps (CDS) can also hedge credit risk, but this introduces complexities like call risk and counterparty reliance, especially if the convertible bond is called. The core idea is to remove the bond’s fixed-income characteristics and the associated credit risk, leaving the embedded equity option. Therefore, hedging the credit risk by shorting a straight bond of the same issuer or using a CDS is a crucial step in isolating the equity option.

This question tests the understanding of how principal-protected commodity notes function and their limitations. While they offer capital preservation, the mechanism of shifting to bonds during price declines prevents investors from capturing subsequent commodity market rallies. This is a key drawback for institutional investors seeking diversification and exposure to commodity market movements, as highlighted in the provided text. The other options describe features that are either not the primary characteristic of these notes or are misrepresentations of their behavior.

The CAIA syllabus emphasizes that when hedge fund managers present performance results in communications, they must provide context to avoid misleading investors. This includes disclosing the effect of material market or economic conditions on the presented results. For instance, reporting a 10% increase in an equity account without mentioning that the broader equity market rose by 40% during the same period would be considered misleading. Therefore, the manager must disclose the impact of prevailing market conditions.

The question tests the understanding of the rationale behind Yale’s exclusion of corporate bonds from its endowment portfolio, as articulated by David Swensen. Swensen’s argument centers on the principal-agent conflict inherent in corporate structures, where management’s decisions might favor stockholders at the expense of bondholders. Furthermore, he posits that the marginal return offered by corporate bonds over government bonds is insufficient to justify this conflict and the potential liquidity issues and value depreciation during market crises, which would negate the intended tail-hedge function of fixed income.

The provided text highlights that the August 2007 quant crisis was largely attributed to a “crowded trade” scenario where numerous quantitative funds, employing similar factor-based strategies (like HML and SMB), simultaneously unwound their positions. This collective deleveraging, driven by a shared belief in the positive drift of these factors and their low correlation, led to a cascade of selling pressure. The funds were essentially betting on the continuation of these factors and their diversification benefits. When these factors unexpectedly reversed and performed poorly in unison, the funds were forced to liquidate, exacerbating the downturn. The text explicitly states that “none of the quants were betting against these fundamental factors; namely, they were playing the long/short factors all the same way.” This indicates a lack of diversification in their directional bets on these factors.

The question tests the understanding of how systematic CTA indices’ returns are explained by their benchmark. The exhibit shows that the Barclay Trader Index Systematic has a beta of 0.66 and an R-squared of 13.5% when regressed against the MLM Index. This indicates that only 13.5% of the systematic CTA index’s excess returns are explained by its exposure to the MLM Index. The remaining 86.5% of the excess return is attributed to factors not captured by the MLM Index, which is referred to as alpha or the result of active trading strategies.

The question tests the understanding of how a crack spread hedge functions to lock in a refiner’s margin. In Scenario A, the refiner buys crude oil at $90.06/barrel and sells gasoline at $98.66/barrel and heating oil at $104.24/barrel. The cash market margin is calculated as the weighted average of the output prices minus the input price. For a 3:2:1 spread, this is [(2 * $98.66) + (1 * $104.24) – (3 * $90.06)] / 3 = ($197.32 + $104.24 – $270.18) / 3 = $31.38 / 3 = $10.46 per barrel. The futures market shows the refiner is long crude at $90.06/barrel and short gasoline at $99.16/barrel and heating oil at $104.54/barrel. The futures crack spread is [(2 * $99.16) + (1 * $104.54) – (3 * $90.06)] / 3 = ($198.32 + $104.54 – $270.18) / 3 = $32.68 / 3 = $10.89 per barrel. The net profit/loss from the hedge is the difference between the futures crack spread and the cash market crack spread, adjusted for the difference between the futures and cash prices at the time of the hedge. However, the question asks about the outcome of the hedge in Scenario A. The refiner locked in a margin of $21.88/bbl (from the initial futures calculation). In Scenario A, the cash market margin is $10.46/bbl. The futures market transactions resulted in a gain. The refiner sold crude futures at $88.68 and bought them back at $90.06, a loss of $1.38/bbl. The refiner sold gasoline futures at $110.08 and bought them back at $99.16, a gain of $10.92/bbl. The refiner sold heating oil futures at $111.54 and bought them back at $104.54, a gain of $7.00/bbl. The net futures gain is (2 * $10.92) + (1 * $7.00) – (3 * $1.38) = $21.84 + $7.00 – $4.14 = $24.70 per barrel. This gain offsets the lower cash margin. The question asks for the net profit/loss on the hedge itself. The initial futures crack spread was $21.88. The final cash margin was $10.46. The difference is $21.88 – $10.46 = $11.42. This is the amount by which the hedge improved the outcome compared to an unhedged position. The question is phrased to test the understanding of the net effect of the hedge. The refiner’s initial intention was to lock in a margin of $21.88. The actual cash margin was $10.46. The hedge’s effectiveness is measured by how much it compensated for this difference. The futures market transactions resulted in a net gain of $24.70 per barrel. This gain, when combined with the cash market outcome, effectively brings the total profit closer to the initial hedged expectation. The question asks for the net profit/loss on the hedge. The refiner’s initial futures position locked in a spread of $21.88. The actual cash market outcome was a margin of $10.46. The difference, $21.88 – $10.46 = $11.42, represents the benefit of the hedge. The futures market transactions themselves generated a net gain of $24.70. The question is asking for the net profit from the hedging instruments. The refiner bought crude futures at $88.68 and sold them at $90.06 (loss of $1.38/bbl). Sold gasoline futures at $110.08 and bought them at $99.16 (gain of $10.92/bbl). Sold heating oil futures at $111.54 and bought them at $104.54 (gain of $7.00/bbl). For a 3:2:1 spread, the net profit/loss on the futures is (2 * $10.92) + (1 * $7.00) – (3 * $1.38) = $21.84 + $7.00 – $4.14 = $24.70 per barrel. This is the profit generated by the futures contracts. The question asks for the net profit/loss on the hedge. The hedge is the combination of futures contracts. Therefore, the net profit on the hedge is $24.70 per barrel.

Alfred Winslow Jones is widely recognized as the pioneer of the hedge fund industry and, specifically, the long/short equity strategy. His firm, A.W. Jones & Co., established in 1949, is credited with initiating this investment approach. While the strategy and the industry did not achieve immediate widespread adoption, Jones’s innovation laid the groundwork for future developments in alternative investments.

The core purpose of monitoring in private equity is to act as a control mechanism within the broader investment process. It’s not merely about information gathering or compliance, but about actively observing, verifying, and influencing the portfolio’s performance towards desired outcomes. While limited partners (LPs) do not manage portfolio companies directly, their monitoring activities are intended to ensure the fund manager adheres to the partnership agreement and to identify and address issues that could negatively impact the fund’s value. This proactive stance aims to mitigate information asymmetry and moral hazard, especially given the long-term and illiquid nature of PE investments, where initial due diligence can quickly become outdated.

The question tests the understanding of how combining factors with low correlations can improve a portfolio’s risk-adjusted return. The Fama-French three-factor model (Mkt-RF, SMB, HML) is introduced, and the concept of an equally weighted (EW) portfolio combining these factors is presented. Exhibit 37.3 shows that the EW portfolio has a higher annualized return divided by annualized standard deviation (Ann.Ret/Ann.Std), which is a measure of risk-adjusted return (Sharpe Ratio), compared to the individual factors. This improvement is attributed to the low correlations between the factors, which allow for diversification benefits. Therefore, a strategy that combines these factors in an equally weighted manner would aim to capture these diversification benefits to enhance its risk-return profile.

The provided exhibits demonstrate that CTA strategies, particularly systematic ones, tend to perform best during periods of low S&P 500 volatility and also show strong performance when S&P 500 volatility experiences significant increases (highest change). Exhibit 31.8A shows the highest average monthly returns for the Barclay Trader Index CTA (1.03%) and Systematic CTA (1.26%) during the ‘Lowest’ volatility quintile. Exhibit 31.8B indicates that CTA strategies, especially diversified and systematic, achieve their highest returns when the change in S&P 500 volatility is in the ‘High’ and ‘Highest’ categories. This suggests a non-linear relationship with volatility, where CTAs benefit from both stable and highly dynamic market environments, rather than a simple long-volatility or short-volatility stance. The question asks about the optimal volatility environment for CTAs based on the data. Option A correctly identifies that CTAs perform well in both low volatility and high volatility *change* environments, reflecting the nuanced findings in the exhibits.

The passage highlights that top-tier private equity teams often raise funds through word-of-mouth referrals and direct outreach, rather than relying on a broad solicitation of investment proposals. This proactive approach involves identifying and engaging with promising teams even before they begin their fundraising cycles. Reactive sourcing, which involves sifting through numerous unsolicited proposals, is deemed inefficient for selecting high-quality managers. Therefore, actively sourcing and building relationships with potential fund managers, and understanding their future fundraising timelines, is crucial for gaining access to sought-after funds.