CAIA Level 2 Quiz 47

Covered interest rate parity (CIP) posits that the difference in interest rates between two countries is equal to the difference between the forward and spot exchange rates. This relationship is based on the principle of no-arbitrage. If CIP holds, an investor should not be able to earn a risk-free profit by borrowing in one currency, converting it to another, investing in that currency, and hedging the future exchange back to the original currency using a forward contract. The formula to check for CIP is: \( (1 + r_{DCU}) \times S_{spot} = (1 + r_{FCU}) \times F_{forward} \), where \( r_{DCU} \) is the domestic currency interest rate, \( r_{FCU} \) is the foreign currency interest rate, \( S_{spot} \) is the spot exchange rate (domestic currency per unit of foreign currency), and \( F_{forward} \) is the forward exchange rate. In this scenario, we are given the spot rate of USD/JPY as 0.00805 (meaning 1 JPY = 0.00805 USD), the forward rate as 0.00815 USD/JPY, the US interest rate as 2.5%, and the Japanese interest rate as 0.5%. To check if CIP holds, we need to see if the hedged return from investing in USD is equivalent to the hedged return from investing in JPY. Let’s consider investing 1 USD. If invested in the US, it grows to \( 1 imes (1 + 0.025) = 1.025 \) USD. If converted to JPY at the spot rate, it becomes \( 1 / 0.00805 \) JPY. Investing this in Japan yields \( (1 / 0.00805) imes (1 + 0.005) \) JPY. Converting this back to USD using the forward rate gives \( ((1 / 0.00805) imes (1 + 0.005)) / 0.00815 \) USD. Calculating this value: \( (1.005 / 0.00805) / 0.00815 \) \( \approx 124.2236 \) \( / 0.00815 \) \( \approx 15241.97 \) USD. This calculation is incorrect because the spot and forward rates are given as USD/JPY, meaning 1 JPY = 0.00805 USD. Let’s re-evaluate with the correct interpretation: \( S_{spot} = 0.00805 \) USD/JPY and \( F_{forward} = 0.00815 \) USD/JPY. The domestic currency is USD, and the foreign currency is JPY. The formula for CIP is \( (1 + r_{USD}) imes S_{spot} = (1 + r_{JPY}) imes F_{forward} \) if we are expressing the exchange rate as USD per JPY. However, the standard formulation often uses the domestic currency as the base. Let’s rephrase the question to be more standard: If the spot rate is 124.22 JPY/USD (which is \( 1/0.00805 \)) and the forward rate is 122.70 JPY/USD (which is \( 1/0.00815 \)), with US rates at 2.5% and Japanese rates at 0.5%. Then, \( (1 + r_{USD}) imes S_{spot} = (1 + r_{JPY}) imes F_{forward} \) becomes \( (1 + 0.025) imes 124.22 \) vs \( (1 + 0.005) imes 122.70 \). \( 1.025 imes 124.22 \) \( \approx 127.3255 \) and \( 1.005 imes 122.70 \) \( \approx 123.3135 \). Since these are not equal, CIP does not hold. The question as posed uses USD/JPY. Let’s use the provided rates directly. \( S_{spot} = 0.00805 \) USD/JPY, \( F_{forward} = 0.00815 \) USD/JPY. \( r_{USD} = 0.025 \), \( r_{JPY} = 0.005 \). CIP holds if \( (1 + r_{USD}) imes S_{spot} = (1 + r_{JPY}) imes F_{forward} \) is not the correct formulation when the exchange rate is USD/JPY. The correct formulation for CIP when the exchange rate is quoted as Domestic Currency per Foreign Currency (USD/JPY) is: \( (1 + r_{DCU}) imes S_{spot} = (1 + r_{FCU}) imes F_{forward} \) is incorrect. It should be \( (1 + r_{DCU}) = (1 + r_{FCU}) imes (F_{forward} / S_{spot}) \) or \( (1 + r_{FCU}) = (1 + r_{DCU}) imes (S_{spot} / F_{forward}) \). Let’s use the first one, where DCU is USD and FCU is JPY. \( (1 + 0.025) \) vs \( (1 + 0.005) imes (0.00815 / 0.00805) \). \( 1.025 \) vs \( 1.005 imes 1.012422 \) \( \approx 1.017484 \). Since \( 1.025 \) is not equal to \( 1.017484 \), CIP does not hold. The question asks if CIP holds. The calculation shows it does not. Therefore, the correct answer is that it does not hold.

The core issue with appraisal-based real estate indices like NCREIF is that their reported returns are smoothed due to the infrequent nature of appraisals. This smoothing artificially reduces the observed volatility and autocorrelation. The unsmoothing process, using a formula like $R_{t,true} = (R_{t,reported} – \rho R_{t-1,reported}) / (1 – \rho)$, where $\rho$ is the autocorrelation coefficient, aims to reveal the underlying, more volatile true returns. A high autocorrelation coefficient (like 83.1% for NCREIF) indicates that past returns have a strong influence on current reported returns, a hallmark of smoothing. When this smoothing is not accounted for, particularly in mean-variance optimization, it can lead to a significant misallocation of capital, over-weighting assets that appear less volatile than they truly are. The REIT index, being market-price-based, generally exhibits lower autocorrelation and higher observed volatility, reflecting more frequent price discovery.

The commitment-weighted benchmark is constructed by aggregating the benchmark performance of individual funds within a portfolio, weighted by their respective commitments. This method ensures that the benchmark accurately reflects the portfolio’s structure and investment strategy, allowing for a more meaningful comparison of the portfolio manager’s skill in selecting funds within specific asset classes, vintage years, or geographic regions. A simple average would not account for the differing capital allocations, potentially misrepresenting the performance relative to the intended investment strategy.

The question tests the understanding of how the smoothing parameter (lambda) in exponential smoothing affects the weighting of recent versus older data. A higher lambda gives more weight to recent observations, making the volatility estimate more responsive to recent price changes. Conversely, a lower lambda gives more weight to older data, resulting in a smoother, less reactive volatility estimate. The question asks about the impact of a higher lambda on the volatility estimate. A higher lambda means recent returns have a greater influence, leading to a volatility estimate that is more sensitive to recent fluctuations. Therefore, if recent returns have been higher than historical averages, a higher lambda will result in a higher estimated volatility. The other options describe incorrect relationships or outcomes.

The scenario describes a situation where a fund held a short position on spark spreads, which are the profit margins for power generation from natural gas. Hurricane Katrina caused an unexpected breakdown in the correlation between natural gas prices (NYMEX Henry Hub) and power prices (PJM Western Hub). This breakdown meant that the usual relationship where power prices moved in tandem with natural gas prices did not hold. Consequently, the spread widened unexpectedly, leading to a significant loss for the fund, even though the individual commodity prices might have moved in a predictable way in isolation. The question tests the understanding of how event risk can disrupt established correlations and impact derivative positions, specifically spark spreads, which are sensitive to the price differential between natural gas and electricity.

The provided exhibit indicates that commodity futures exhibit positive returns during the early phase of a recession, while stocks and bonds typically show negative returns during this period. This divergence in performance highlights commodities’ potential as a diversifier against systematic risk, particularly during economic downturns. The question tests the understanding of how commodities behave relative to other asset classes during specific economic cycles, as supported by the research cited.

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 DJUBSCI employs a combination of liquidity and production, with liquidity having double the influence of production, and also imposes sector and individual commodity caps to ensure broader diversification. The Deutsche Bank Liquid Commodity Index (DBLCI) is characterized by its limited number of components, focusing on highly liquid commodities, which implies a potentially less diversified portfolio compared to broader indices. The Diapason Commodities Index (DCI), conversely, is noted for its extensive list of components, suggesting a higher degree of diversification.

This question tests the understanding of how technical analysis, specifically trend-following strategies, can generate profits in futures markets. The core concept is that market inefficiencies, such as the delayed price adjustment to new information and the limitations faced by arbitrageurs (risk aversion, capital constraints), create predictable price trends. Moving average and price channel systems are designed to capture these trends by identifying sustained price movements. While other technical indicators exist, the question focuses on the fundamental principle that these systematic approaches can exploit temporary deviations from equilibrium, which is a key tenet in the empirical evidence supporting technical trading rules in certain markets like commodities and currencies.

The CAIA curriculum emphasizes a holistic approach to fund manager selection, recognizing that while quantitative metrics are important, qualitative factors are equally, if not more, critical, especially in less liquid asset classes like private equity. The “team, team, team” adage highlights the paramount importance of the management team’s experience, cohesion, and alignment of interests. While track record analysis (quantitative) and understanding the organizational structure are vital, the subjective assessment of team dynamics, personality, and the LP’s comfort level with the manager over a long-term relationship are crucial qualitative elements that cannot be overlooked. These subjective elements, often referred to as “soft skills” or “intangibles,” are essential for navigating the inherent uncertainties and potential conflicts within private equity investments, and are a key differentiator in successful fund selection.

This question tests the understanding of how different asset classes, specifically commodities, are viewed in the context of portfolio construction and their relationship with traditional assets like equities. The reference to “Commodities and Real-Return Strategies in the Investment Mix” by Burkart (2006) and “The Role of Institutional Investors in Rising Commodity Prices” by Black (2009) suggests a focus on the diversification and inflation-hedging properties of commodities. Commodities are often considered for their low correlation with equities and their potential to provide a hedge against unexpected inflation, thereby enhancing portfolio diversification and real returns. While commodities can exhibit volatility, their inclusion is typically justified by these diversification and inflation-hedging benefits, rather than solely for their direct correlation with equity market movements or their inherent liquidity, which can be variable.

The endowment model, as described, emphasizes a significant allocation to alternative investments to achieve long-term return targets that outpace inflation and cover spending needs. While asset allocation is a key component, the text explicitly states that simply replicating the asset allocations of successful endowments does not guarantee similar performance. This is because top endowments have historically generated substantial value through superior manager selection and astute market timing, which are active management strategies that go beyond passive asset allocation.

Operational due diligence for hedge funds, particularly those employing strategies like convertible arbitrage, necessitates a thorough examination of the valuation process. The Lipper Convertible Fund case highlights the critical importance of verifying the quality and source of pricing inputs. In convertible bond pricing, unobservable inputs like equity volatility require careful estimation. A robust due diligence process would scrutinize how these estimates are derived, the assumptions underpinning them, and whether they are consistently applied and documented. Overreliance on a flawed or unverified valuation methodology, as seen with the external auditor in the Lipper case, can lead to material misstatements and significant investor losses. Therefore, assessing the quality of pricing inputs is a fundamental aspect of operational due diligence.

Operational due diligence for hedge funds involves a thorough review of the fund’s internal processes and external relationships to ensure operational robustness and mitigate risks. A key aspect is evaluating the fund’s reliance on third-party administrators. While a reputable administrator can enhance investor confidence, it’s crucial for investors to scrutinize the administrator’s capabilities, especially if the administrator is less established. This scrutiny should focus on the accuracy of Net Asset Value (NAV) calculations, the strength of accounting controls, and the overall efficiency of operational procedures. The question tests the understanding that while a good administrator is beneficial, the investor’s responsibility is to verify the administrator’s quality, particularly when the administrator is not widely recognized, to ensure the integrity of the fund’s operations and reporting.

The Chief Compliance Officer (CCO) is tasked with ensuring that an investment adviser’s policies and procedures are not only designed but also effectively implemented and tested. This includes a proactive approach to identifying potential conflicts of interest and compliance risks specific to the firm’s operations. The CCO’s role extends to overseeing the testing of these policies and procedures to gauge their effectiveness and reporting the findings to senior management. This continuous review and testing process is crucial for maintaining a robust compliance framework and addressing emerging issues. Therefore, the CCO’s responsibility encompasses the ongoing assessment of the efficacy of the firm’s compliance program.

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 lack the liquidity and transparency assumed by CAPM, making it difficult to obtain reliable data for beta estimation and market risk premium calculations. The question tests the understanding of CAPM’s limitations in the context of PE, highlighting the divergence between theoretical assumptions and the practical realities of PE investing.

The question tests the understanding of how leverage can exacerbate liquidity risk, particularly during market stress. When an investor uses leverage, they are obligated to repay borrowed funds. In times of crisis, prime brokers may reduce or withdraw credit lines, forcing leveraged investors to liquidate assets quickly. This forced selling, especially when multiple investors face similar pressures, can lead to a precipitous decline in asset prices, as buyers anticipate further price drops. The scenario highlights that a reduction in leverage from eight times to four times necessitated the sale of half the portfolio, demonstrating the direct impact of leverage reduction on asset holdings and the potential for amplified losses due to market dynamics.

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 smaller number of managers. Specifically, the exhibit suggests that approximately five to six CTAs are sufficient to nearly eliminate the chance of losing money when the industry is profitable. Beyond this point, the marginal benefit of diversification in narrowing the return range diminishes. Therefore, an investor seeking to minimize the risk of significant underperformance while still capturing a reasonable portion of the index’s return would find a portfolio of five to six CTAs to be an efficient point.

The question tests the understanding of how rising global incomes influence agricultural land demand. As per capita incomes increase, dietary habits shift towards higher consumption of meat proteins. This dietary shift, in turn, drives up the demand for animal feed grains like corn and soybeans. Producing these feed grains requires significantly more land than producing the equivalent calories from vegetable sources. Therefore, an increase in meat consumption, driven by rising incomes, directly translates to increased pressure for agricultural land expansion to meet the higher demand for feed crops.

This question tests the understanding of how quantitative equity market neutral strategies leverage statistical relationships between securities. Co-integration identifies pairs of assets whose price movements are statistically linked over the long term, despite short-term divergences. A strategy based on co-integration would involve taking a long position in the underperforming asset and a short position in the outperforming asset within a co-integrated pair, anticipating a reversion to their historical relationship. This is a core concept in pairs trading, a common quantitative equity market neutral strategy.

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. While transaction-based indices use actual sale prices, appraisal-based indices use estimated values. Indices that rely solely on actual sale prices would be considered transaction-based. Indices that incorporate leverage would reflect debt financing, which the NPI explicitly excludes by being unleveraged. Indices that are solely based on income streams would not capture capital appreciation or depreciation.

The core challenge in managing private equity liquidity stems from the inherent unpredictability of capital calls and exit distributions. General partners (GPs) control the timing and size of capital calls, which are dependent on investment opportunities and fund expenses, not on the limited partner’s (LP’s) liquidity needs. Similarly, exit timing and value are influenced by company performance, value creation by the GP, and prevailing market conditions, making them highly uncertain. Therefore, an LP’s ability to precisely manage its private equity allocation to a specific percentage, like 5%, is hindered by these unpredictable cash flow dynamics. Building a diversified portfolio across multiple vintage years and strategies is crucial to mitigate this, creating a more stable and predictable overall cash flow profile.

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 need for unexpected contribution increases. 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 those facing regulatory scrutiny. Diversification into alternatives is a tactic, but LDI is the overarching strategy that directly addresses the liability matching objective.

Capital at Risk (CaR) in managed futures is defined as the total potential loss if every position in the portfolio hits its predetermined stop-loss level on a given day. The provided exhibit calculates this by taking the notional value of each contract and multiplying it by the assumed adverse price movement (1% in this case). For example, the S&P 500 E-mini futures contract has a notional value of $207,250, and a 1% adverse move results in a potential loss of $2,073. The total CaR is the sum of these individual potential losses across all contracts in the portfolio. The margin-to-equity ratio, on the other hand, measures the amount of initial margin required by exchanges as a percentage of the account’s net asset value, indicating the degree of leverage employed. Value at Risk (VaR) quantifies potential portfolio losses over a specific period at a given confidence level, assuming no portfolio changes. Therefore, CaR specifically addresses the risk associated with stop-loss orders being triggered.

The core challenge in managing private equity liquidity stems from the inherent unpredictability of capital calls and distributions. General partners (GPs) control the timing and size of capital requests from limited partners (LPs) based on investment opportunities, management fees, and expenses. Similarly, the timing and value of exits (distributions) are highly uncertain, influenced by company growth, value-add by the GP, and prevailing market conditions. This lack of control over cash flow timing makes precise portfolio allocation difficult. Therefore, investors must build their private equity exposure across multiple vintage years and maintain an allocation range rather than a fixed target to manage this inherent illiquidity and timing risk.

The provided text emphasizes the strong persistence of top-quartile performance in private equity, citing studies that indicate a significant correlation between a fund manager’s past performance and their future success. This persistence suggests that investors should prioritize managers with a proven track record, as this historical data is a strong predictor of future outperformance. While other factors like network access and team restructuring are mentioned, the core tenet for identifying successful managers, according to the research presented, is the demonstrable consistency of high returns across multiple funds.

The question tests the understanding of the risk-return spectrum within real estate investment styles as defined by NCREIF. Core properties are characterized by stable income streams, low volatility, and minimal leverage, making them the least risky and most bond-like. Value-added properties involve a moderate level of risk, aiming for returns through a combination of income and capital appreciation, often requiring active management such as renovations or repositioning. Opportunistic properties, while not explicitly detailed in the provided text for this question, represent the highest risk category, often involving development, distressed assets, or significant repositioning with a primary focus on capital appreciation and higher leverage. Therefore, a property with a substantial portion of its return expected from capital appreciation, moderate volatility, and less reliable income than core properties aligns with the definition of a value-added investment.

The question tests the understanding of how hedge fund strategy allocations can shift over time and the challenges associated with tracking these shifts. The provided text highlights that macro funds, which had the largest allocation in 1990, saw their allocation significantly decrease by Q4 2011, becoming the smallest. Conversely, event-driven funds, initially the smallest, increased their allocation. This dynamic nature, coupled with potential data vendor biases and differing strategy definitions, makes relying solely on historical AUM weights for allocation problematic. Therefore, a fund of funds manager must consider the evolving landscape of hedge fund strategies and the potential for style drift within individual funds when constructing a portfolio.

The question tests the understanding of the sources of return in commodity futures investments. According to the provided text, returns are derived from three primary components: the spot return (reflecting changes in the underlying commodity’s value), the collateral income or yield (from the return on the cash collateral, typically Treasury bills), and the roll return (arising from changes in the futures basis). While spot returns are influenced by supply and demand, and collateral yield is a function of interest rates, the roll return is specifically linked to the shape of the forward curve and the cost of carry. The text explicitly states that roll yield and collateral return are responsible for the bulk of a commodity investment’s total return, with spot return providing diversification benefits. Therefore, the most accurate description of the primary drivers of return, excluding diversification, would encompass the collateral yield and the roll return.

The question tests the understanding of the cost of carry model and its components, specifically how convenience yield impacts the relationship between spot and futures prices. The cost of carry model states that the difference between the futures price (F) and the spot price (P) is influenced by the cost of funding (r), storage costs (s), and the benefits of holding the commodity, including convenience yield (c). The formula is F – P = P * (r + s – c) * (T – t). In the given scenario, the futures price ($98) is lower than the spot price ($100), indicating a negative basis. This implies that the term (r + s – c) must be negative, meaning the convenience yield (c) is greater than the sum of the funding and storage costs (r + s). The calculation shows that c = 17%, while r + s = 9%. Since 17% > 9%, the convenience yield is indeed higher than the combined costs, leading to a situation where the spot price is greater than the futures price, and the market is in backwardation. Option A correctly identifies that a higher convenience yield than the sum of funding and storage costs leads to the spot price exceeding the futures price and backwardation.

The theory of normal backwardation, as proposed by Keynes, suggests that futures prices should generally be lower than the expected future spot prices. This price differential, known as the risk premium, incentivizes speculators to take long positions in futures contracts. Hedgers, typically producers with short positions, are willing to pay this premium to transfer price risk. The risk premium compensates speculators for bearing this risk. Therefore, when inventories are low and volatility is high, the convenience yield is expected to be high, leading to a situation where futures prices are typically below expected future spot prices, a condition termed normal backwardation.