CAIA Level 2 Quiz 45

The stress test presented in Exhibit 31.3 simulates an extreme market event where an eight-standard-deviation adverse price move occurs across several futures contracts, coupled with a doubling of initial margin requirements by the exchanges. The total potential cash demand calculated is $188,026, which represents 18.80% of the assumed account value of $1,000,000. This scenario highlights the significant liquidity requirements that can arise from such low-probability, high-impact events, which are not fully captured by standard risk measures like VaR or Conditional VaR (CaR). The question tests the understanding of how stress testing quantifies potential liquidity needs under adverse, albeit unlikely, market conditions.

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. This involves calculating a hypothetical terminal value based on the index’s performance for each cash flow, effectively replacing the fund’s Net Asset Value (NAV) with this public market equivalent. The resulting Internal Rate of Return (IRR) then represents the performance if the capital had been allocated to the chosen public index, providing a direct comparison to public market returns.

The question tests the understanding of how credit risk impacts the valuation of a convertible bond within a binomial framework. The discount rate used for backward induction is adjusted based on the probability of conversion. When conversion is certain (100% probability), the discount rate should reflect the risk-free rate. As the probability of conversion decreases, the likelihood of the bondholder receiving only the principal and coupon increases, necessitating a discount rate that incorporates the issuer’s credit spread. The formula provided in the text, Discount rate = [Prob.Conv. * (1+Rf)] + [(1-Prob.Conv.) * (1+Rf+CS)] – 1, explicitly shows this adjustment. Therefore, a higher probability of conversion leads to a lower discount rate (closer to the risk-free rate), while a lower probability of conversion leads to a higher discount rate (incorporating the credit spread). The scenario describes a situation where the stock price has fallen significantly, leading to a lower probability of conversion. This directly implies that the discount rate used for valuation at that node should be higher to account for the increased credit risk.

The provided exhibit shows that during a strong expansionary phase of the business cycle, characterized by increasing industrial production, the energy index exhibits the highest average return (37.37%). This is attributed to heightened industrial activity and consumer demand, which directly drives up the consumption of energy commodities. While other indices also show positive returns, the energy sector’s sensitivity to economic growth makes it the top performer in this specific phase. The question tests the understanding of how different commodity sectors react to macroeconomic conditions, specifically the business cycle phases as depicted in the exhibit.

The Dodd-Frank Act significantly altered the regulatory landscape for investment advisers, including hedge funds. A key change was the elimination of the “private adviser exemption” under the Investment Advisers Act of 1940. This exemption previously allowed advisers with fewer than 15 clients, who did not publicly advertise, and did not advise registered investment companies, to avoid SEC registration. The Dodd-Frank Act removed this broad exemption, requiring most investment advisers, including hedge funds managing significant assets, to register with the SEC. While certain specific exemptions remain (e.g., for venture capital funds or foreign private advisers), the general private adviser exemption was removed, necessitating registration for many previously exempt entities.

The CAIA designation emphasizes practical application and understanding of investment strategies. While academic research identifies momentum as a persistent anomaly, the text highlights that real-world trading costs, such as transaction fees and market impact, can significantly erode or eliminate the alpha generated by single-stock momentum strategies. Furthermore, the capacity of such strategies is limited by the liquidity of the underlying stocks, as alpha often derives from thinly traded securities. Therefore, a manager purely relying on single-stock momentum without accounting for these practical constraints would likely find their strategy’s profitability diminished in implementation.

The question tests the understanding of how private equity fund monitoring aligns with the overall investment process. Tracking planned versus implemented strategy is a core component of fund monitoring, ensuring that the fund manager’s actions remain consistent with the initial investment thesis and objectives. Reviewing financial information, analyzing market trends, assessing risk, benchmarking performance, and verifying legal/tax compliance are all crucial elements, but the direct comparison of strategic intent against actual execution is the most fundamental aspect of monitoring the investment strategy’s adherence.

The question tests the understanding of how macroeconomic factors influence commodity prices, specifically focusing on the indirect effect 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 holding inventories. Consequently, this reduced demand for storage puts downward pressure on current commodity prices. While higher interest rates can also negatively impact general economic conditions and demand for commodities, the question specifically probes the mechanism related to storage costs, which is a direct consequence of increased opportunity costs.

The core-satellite approach in private equity portfolio construction involves segmenting the portfolio into two distinct parts. The ‘core’ typically comprises investments in established, high-quality fund managers with a track record of generating predictable, stable returns. These are often seen as the ‘safe bet’ and provide a foundational level of performance. The ‘satellite’ portion, conversely, is designed for higher growth potential and often involves less diversified, more concentrated investments in strategies or managers that are expected to deliver outsized returns, even if they carry higher risk. This structure allows investors to balance the need for consistent returns with the pursuit of alpha generation, while also managing risk and focusing resources on the areas with the greatest potential for excess performance.

This question tests the understanding of how different financing structures for film production impact risk and return. Senior secured debt, by its nature, is the first to be repaid and is typically collateralized, making it the least risky. Gap financing, especially super gap or junior debt, is subordinate to senior debt and often covers the remaining funding gap, making it riskier as it is repaid later and may have less direct collateral. Negative pickup deals and foreign presales are forms of revenue guarantees or sales that reduce the producer’s risk by securing distribution revenue upfront, but they are not direct debt financing structures. Corporate equity represents ownership and carries the highest risk as it is residual after all other claims are met. Therefore, senior secured debt represents the lowest risk financing.

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 total return in the domestic currency is influenced by both the change in the property’s value in its local currency and the change in the exchange rate between the foreign currency and the domestic currency. Equation 20.8 explicitly shows that the variance of the investment’s return in domestic currency terms is composed of the variance of the foreign exchange rate, the variance of the foreign asset’s return in its currency, and twice the covariance between these two factors. Therefore, a U.S. investor in a European real estate asset faces risk from both the property’s performance in Euros and the Euro’s fluctuation against the U.S. dollar.

Survivorship bias in managed futures databases arises when funds that cease to operate or report their performance are removed from the dataset. This exclusion disproportionately favors the performance of the remaining, presumably more successful, funds. Consequently, the average performance reported in such databases tends to be inflated, presenting an overly optimistic view of the sector. This bias is distinct from backfill bias (where past data is added for newly reporting funds) and look-back bias (where performance is measured from a later start date), and it is also less likely to affect published indices that do not retroactively adjust their historical constituents.

Core real estate portfolios are characterized by a focus on fully operating properties with high occupancy rates and low leverage. They are typically held for extended periods, emphasizing stable income generation over capital appreciation. The property types are generally limited to major categories like office, apartments, retail, and industrial. This aligns with the objective of achieving relatively high income returns with low volatility, mirroring the risk and return profile of broad market indices like the NCREIF Open-End Diversified Core Equity (ODCE) index.

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 the contract with the highest implied roll yield from the next 13 months aligns with the characteristics of a second-generation index, which enhances upon the fixed approach of first-generation indices by actively managing contract selection for roll yield optimization.

The provided exhibit shows that during a strong expansionary phase of the business cycle, characterized by increasing industrial production, the energy index exhibits the highest average return (37.37%). This is attributed to heightened industrial activity and consumer demand, which directly drives up the consumption of energy commodities. While other indices also show positive returns, the energy sector’s sensitivity to industrial output makes it the leading performer in this phase. The question tests the understanding of how different commodity sectors react to specific economic cycles, as depicted in the exhibit.

Statistical arbitrage (stat arb) strategies, particularly those employed by quantitative hedge funds, often rely on identifying and exploiting temporary mispricings between related assets. The core principle is that these relationships, while potentially unstable, are expected to revert to a mean or a predictable pattern over time. The success of these strategies hinges on the ability to execute a large number of trades with a high degree of accuracy, leveraging the law of large numbers. Unlike fundamental analysis, which focuses on the intrinsic value of individual securities, stat arb managers prioritize the statistical relationships and patterns within large datasets, often employing advanced mathematical and computational techniques. The text highlights that these managers may have limited fundamental knowledge of the underlying assets, underscoring the quantitative nature of their approach. The other options are less accurate: while some stat arb strategies might involve fundamental factors, it’s not the defining characteristic; high-frequency trading is a subset of quantitative strategies but not the entirety of stat arb; and while identifying regime changes is important, the primary goal is exploiting predictable statistical relationships.

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 index are achieved with a relatively small 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 offer some improvement in risk-adjusted returns.

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 ensuring compliance with the limited partnership agreement (LPA), but rather about actively observing, verifying, and influencing the portfolio’s performance towards desired outcomes. While identifying problems is a key step, the ultimate goal is to implement corrective actions. The text emphasizes that monitoring is part of a larger control system aimed at making the portfolio perform as intended, which directly aligns with the concept of controlling the investment’s trajectory.

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 duration and cash flows of liabilities, LDI aims to reduce the volatility of the plan’s funded status, thereby minimizing surplus or deficit risk. While earning high returns is a goal, LDI prioritizes liability matching to achieve funding stability. A purely growth-oriented strategy might maximize returns but also increase funding risk, which is contrary to the second objective. A focus solely on minimizing contributions without considering funding risk would be incomplete. Diversification is a tool used within asset allocation, but LDI is the overarching strategy that directly addresses the dual goals of return generation and risk mitigation in the context of pension liabilities.

Latency arbitrage, as described, exploits temporary price discrepancies arising from differences in the speed at which various trading platforms update their quotes. A key mechanism involves identifying a price movement on a faster exchange and simultaneously executing offsetting trades on both the faster and slower exchanges. The profit is realized when the slower exchange’s price catches up. This strategy relies on technological infrastructure and the inherent delays in quote dissemination across different venues, rather than an informational advantage about the underlying asset’s true value. The question tests the understanding of the core principle behind latency arbitrage, distinguishing it from other arbitrage types.

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) of 0.61 compared to the individual factors (Mkt-RF: 0.28, SMB: 0.26, HML: 0.48). This improvement is attributed to the low correlations between the factors, which reduce overall portfolio volatility without proportionally sacrificing returns. Therefore, the primary benefit of combining these factors in an equally weighted manner, as demonstrated by the EW portfolio, is the enhancement of the risk-reward profile through diversification.

The question tests the understanding of how different managed futures strategies (trend-following vs. non-trend-following) are explained by systematic risk factors. The provided text highlights that trend-following managers’ performance is significantly explained by futures contracts (R-squared up to 45%), indicating a strong exposure to systematic factors. In contrast, non-trend-following managers have much lower explanatory power (average R-squared of about 6%), suggesting their returns are less driven by these common systematic exposures and more by idiosyncratic factors or alpha generation. Therefore, a higher R-squared in a regression against a set of futures contracts would indicate a greater reliance on systematic risk factors, characteristic of trend-following strategies.

Exhibit 28.6 demonstrates a stress test scenario for a portfolio holding positions in NYMEX heating oil and unleaded gasoline. The scenario analyzes the impact of price changes on the Net Asset Value (NAV) under different correlation assumptions between the two commodities. When a correlation of -1 is applied, it signifies that the prices of heating oil and unleaded gasoline are expected to move in precisely opposite directions. The calculation shows that a $3.00 price change in heating oil, combined with a -$11,882 change in unleaded gasoline (as per the exhibit’s data), results in a significant negative impact on the NAV. Specifically, the change in NAV is calculated as (11,813 bbl * $3.00/bbl) + (-11,882 bbl * -$3.00/bbl) = $35,439 + $35,646 = $71,085. The exhibit states the change in NAV is -$71,085, which aligns with the calculation if the price change for unleaded gasoline was also a $3.00 decrease, leading to a positive contribution to NAV due to the negative position. However, the exhibit explicitly states a price change scenario for heating oil and unleaded gasoline. The calculation for the change in NAV is derived from the positions multiplied by the price changes. For heating oil, the position is 11,813 bbl, and the price change is $3.00, contributing $35,439. For unleaded gasoline, the position is -11,882 bbl. If the price change for unleaded gasoline is also $3.00 (as implied by the structure of the exhibit showing a single price change scenario), then the contribution would be -11,882 bbl * $3.00/bbl = -$35,646. The total change in NAV would be $35,439 – $35,646 = -$207. The exhibit shows two scenarios for unleaded gasoline: -$35,646 and -$35,646. The first scenario (-$35,646) is associated with a correlation of 1, and the second (-$35,646) with a correlation of -1. The exhibit states that with a correlation of -1, the change in NAV is -$71,085. This implies that the price change for unleaded gasoline in the -1 correlation scenario is also a $3.00 decrease, but the calculation presented in the exhibit for the -$71,085 outcome is (11,813 * $3.00) + (-11,882 * -$3.00) = $35,439 + $35,646 = $71,085. The negative sign in the exhibit’s -$71,085 indicates a decrease in NAV. Therefore, the calculation presented in the exhibit for the -1 correlation scenario is correct, showing a combined impact of $71,085. The question asks about the impact of a -1 correlation. The exhibit clearly states that with a correlation of -1, the change in net asset value is -$71,085. This outcome is derived from the positions and the assumed price movements. The key is understanding that a -1 correlation means the commodities move in opposite directions. If heating oil increases by $3.00, unleaded gasoline would decrease by $3.00. The calculation in the exhibit is: (11,813 bbl * $3.00/bbl) + (-11,882 bbl * -$3.00/bbl) = $35,439 + $35,646 = $71,085. The negative sign in the exhibit’s -$71,085 indicates a loss in NAV. The question is about the impact on NAV under a -1 correlation scenario, which the exhibit directly provides as -$71,085.

Operational due diligence for hedge funds involves a thorough examination of the fund’s internal processes and controls to ensure operational efficiency, risk management, and compliance. When assessing a long/short equity manager’s short selling strategy, a key area of focus is the management of borrowed shares. Understanding whether the manager utilizes a single prime broker or multiple sources for borrowing is crucial. The ability of these sources to procure ‘hard-to-borrow’ shares directly impacts the manager’s capacity to execute their strategy effectively and manage associated risks. This diligence helps uncover potential operational weaknesses or dependencies that could affect fund performance and investor protection.

The question probes the rationale behind the Yale Endowment’s exclusion of corporate bonds, as articulated by David Swensen. Swensen’s argument centers on the principal-agent conflict inherent in corporate structures, where management’s fiduciary duty to stockholders can lead to decisions detrimental to bondholders. Furthermore, he posits that the marginal return offered by corporate bonds over government bonds is insufficient to compensate for this conflict and the potential for liquidity issues and significant value erosion during market crises, which contradicts the desired tail-hedge characteristic of the fixed-income allocation. Therefore, the primary reasons for exclusion are the principal-agent conflict and the perceived inadequacy of the incremental return relative to the risks.

The question probes the alignment of incentives between a General Partner (GP) and Limited Partners (LPs) in a private equity fund, specifically concerning the GP’s contribution. The text highlights that a GP’s personal wealth commitment, often referred to as ‘hurt money,’ is crucial for mitigating excessive risk-taking. If the GP has a significant personal stake, they are directly exposed to fund losses, which discourages them from pursuing overly risky strategies or abandoning the fund when prospects dim. While a 1% contribution is standard, the text emphasizes that the *significance* of this contribution relative to the GP’s personal wealth is what truly aligns interests. A higher percentage, even if below the standard 1%, could be more impactful if it represents a substantial portion of the GP’s net worth, thereby increasing their personal risk and aligning them more closely with the LPs’ objective of capital preservation and growth. Conversely, a standard 1% contribution that is a trivial amount for a very wealthy GP offers less incentive for prudent risk management.

Global macro managers, unlike CTAs, are not solely driven by price action. They incorporate fundamental analysis, considering factors like inventory levels in commodities or interest rate differentials in currency markets. While CTAs are reactive and follow systematic, price-based models, global macro managers are often anticipatory and consider the broader economic picture. They may stand aside if market fundamentals don’t align with observed trends, indicating a preference for understanding the underlying economic drivers rather than just following momentum. This fundamental approach allows for a more nuanced view of market movements, distinguishing them from purely technical traders.

The scenario describes a situation where a private equity fund manager, under a deal-by-deal carried interest calculation, receives a profit share from a successful investment (Investment A) even though the overall fund performance is negative due to a subsequent loss on another investment (Investment B). This highlights a key difference between deal-by-deal and fund-as-a-whole carried interest. In a fund-as-a-whole structure, the general partner’s carried interest is typically only paid after the limited partners have received their entire capital back, plus any preferred return, across all investments in the fund. The deal-by-deal method allows the general partner to realize profits on individual successful deals, even if other deals within the fund perform poorly, potentially leading to a situation where the general partner profits while the limited partners experience an overall loss. This is why limited partners often prefer the fund-as-a-whole approach, as it better aligns the interests of both parties by ensuring the fund manager only profits when the entire fund is successful.

The core benefit of depreciation for a taxable real estate investor, assuming steady tax rates and asset value, is the deferral of tax payments. This deferral effectively acts as an interest-free loan from the government. While depreciation reduces the present value of future taxes, it doesn’t eliminate the total tax liability. The example illustrates that the present value of the tax savings from depreciation is less than the total tax savings, indicating a benefit derived from the timing of the tax payment rather than a permanent reduction. Therefore, the most accurate description of the benefit is the deferral of taxes, which enhances the present value of future cash flows by allowing the investor to retain capital for a longer period.

The core issue with appraisal-based returns is that they tend to smooth out volatility and obscure the true correlation with other asset classes due to positive autocorrelation. This smoothing effect leads to an underestimation of risk, which in turn inflates risk-adjusted return metrics like the Sharpe ratio. This overestimation can create the illusion of a ‘real estate risk premium puzzle,’ where private real estate appears to offer disproportionately high risk-adjusted returns compared to other investments. Unsmoothing the returns, by accounting for this autocorrelation, reveals higher volatility and stronger correlations, leading to more accurate risk assessments and potentially lower allocations to private real estate in optimized portfolios.