CAIA Level 2 Quiz 100

This question tests the understanding of how spread trading strategies are initiated and exited based on statistical triggers. The scenario describes a situation where a 100-day statistic for a spread between two commodities has reached a critical entry point. The core concept is that a long entry into a spread occurs when the statistic falls below a negative critical value (e.g., -2.75), indicating the numerator commodity is relatively undervalued compared to the denominator. Conversely, a short entry occurs when the statistic rises above a positive critical value (e.g., 2.75), suggesting the numerator is overvalued. Exiting a long spread position happens when the statistic reverts to a less extreme value, typically crossing zero from below. The question specifically asks about the action taken when the statistic moves from a significantly negative value towards zero, which signifies the convergence of prices and the closing of the profitable spread. Therefore, exiting a long spread position is the appropriate action.

The question tests the understanding of the sources of return in commodity futures investments. According to the provided text, returns on commodity futures contracts are derived from three primary components: the spot return (reflecting changes in the underlying cash commodity’s value), the collateral income or yield (from the return on the cash collateral, typically Treasury bills), and the roll return (arising from the change in the futures basis as contracts are rolled over time). While spot returns are influenced by supply and demand, and collateral yield is a function of prevailing interest rates, the roll return is specifically linked to the shape of the forward curve and the cost of carry. The text emphasizes that roll yield and collateral return are generally the larger contributors to total return, with spot return providing diversification benefits. Therefore, the combination of spot return, collateral yield, and roll return accurately represents the sources of return.

The factor-based approach to hedge fund replication relies on the premise that a significant portion of a hedge fund’s returns can be attributed to underlying asset-based risk factors. The goal is to construct a portfolio using these investable factors that closely tracks the performance of a chosen benchmark, such as a hedge fund index. This involves selecting appropriate factors, determining the estimation period for parameter calibration, and deciding on the number of factors to use. The equation presented illustrates how the excess return of the hedge fund (or benchmark) is modeled as a linear combination of the excess returns of the chosen factors, with coefficients representing the exposure to each factor, plus an error term. The key is that the factors themselves must be investable to create a replicable portfolio.

The question tests the understanding of the primary purpose of an offering document in the context of hedge fund investments. While it serves as a marketing tool and provides a summary of key investment details, it is not the legally binding document that governs the fund’s operations. The subscription agreement, partnership agreement (for U.S. limited partnerships), and investment advisory agreement are the legally operative documents that define the rights and obligations of the parties involved. Therefore, stating that the offering document is the primary legal contract for the investment is incorrect.

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 over $150 million in 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 abolished, necessitating registration for a much larger group of advisers.

The provided text highlights that academic research has identified profitable momentum strategies in commodity futures markets, often linked to inventory levels and term structure. These strategies, which involve tactically allocating capital towards best-performing commodities and away from worst-performing ones, have demonstrated positive returns. The text explicitly contrasts this with a long-only equally weighted portfolio of commodity futures, which experienced negative returns over the same period. Therefore, the core of the profitability for these managed futures strategies, as described, stems from their ability to capture price trends and exploit market inefficiencies, rather than simply holding a diversified basket of commodities.

The bottom-up approach to private equity portfolio design prioritizes the selection of individual fund managers based on their perceived ability to generate alpha, meaning returns uncorrelated with the broader market. This strategy emphasizes rigorous research and due diligence to identify managers with a proven track record and strong investment acumen. While diversification across multiple funds is a component, the core tenet is concentrating capital in what are believed to be the highest-performing funds, driven by manager quality. A top-down approach, conversely, would focus on macroeconomic trends and strategic asset allocation across sectors and geographies before considering specific managers. A mixed approach combines elements of both, but the question specifically asks about the primary driver of the bottom-up method.

The question tests the understanding of the core principle behind cost-averaging in private equity fund commitments. Cost-averaging, also known as vintage-year diversification, advocates for consistent investment amounts across all fund vintages, irrespective of perceived market conditions. This disciplined approach aims to mitigate the risk of over-allocating capital during periods of high valuations or unfavorable exit environments. Market timing, conversely, involves adjusting investment levels based on predictions of future market performance, which is inherently speculative and prone to emotional biases and market overreactions. Therefore, sticking to a predetermined annual investment target for each fund type, regardless of market sentiment, is the hallmark of a cost-averaging strategy.

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 SAA principle. TAA would involve actively adjusting allocations based on short-term forecasts, not simply returning to a predetermined target. While managing risk is a goal for both, the method described (returning to target weights) is characteristic of SAA, not TAA’s active deviation strategy. Derivatives are tools that can be used for rebalancing, but the core decision described is about the strategy itself.

The passage discusses that the profitability of carry and momentum currency strategies is often attributed to several factors. Option A suggests it’s fair compensation for systematic risk, which is a common economic explanation for persistent market anomalies. Option B posits that the profitability is illusory due to transaction costs, a valid concern in trading but not the primary explanation for these strategies’ observed historical success. Option C suggests unpredictability limits arbitrage, which is partially true but doesn’t fully explain the consistent profitability. Option D proposes trading against central banks, which can be a component of some strategies but isn’t the overarching explanation for the profitability of both carry and momentum strategies as presented in the text. The core idea is that these strategies capture systematic risk premia.

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 these two commodities. When the correlation is -1, it implies that the prices of heating oil and unleaded gasoline move in perfectly opposite directions. The calculation shows that a $3.00 price change in heating oil, combined with a -$11,882 position in unleaded gasoline, results in a change in NAV of -$207 under a specific correlation assumption. The question asks to identify the impact on NAV when the correlation is -1, given the provided price changes and positions. The table in Exhibit 28.6 explicitly states that with a correlation of -1, the change in net asset value is -$71,085. This value is derived from the product of the position sizes and the price changes, adjusted by the correlation factor. Specifically, for heating oil, the impact is 11,813 bbl * $3.00/bbl = $35,439. For unleaded gasoline, the impact is -11,882 bbl * $3.00/bbl = -$35,646. With a correlation of -1, the total impact is $35,439 * (-1) + (-$35,646) * (-1) = -$35,439 + $35,646 = $207. However, the exhibit shows a change in NAV of -$71,085 for a correlation of -1. This discrepancy suggests that the exhibit might be using different price changes or position sizes for the correlation of -1 scenario, or there’s a misunderstanding of how the calculation is presented. Re-examining the exhibit, the ‘Correlation -1’ column shows the *impact* of the price change on the NAV, not the price change itself. The calculation for the -$71,085 figure is likely derived from the positions and the price changes, but the exact calculation isn’t fully detailed for the -1 correlation scenario. However, the exhibit directly states the outcome for a correlation of -1. The question asks for the impact on NAV when correlation is -1. The exhibit clearly states this value is -$71,085. The other options are derived from misinterpreting the table or performing incorrect calculations.

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). This strongly suggests that U.S. farmland returns have historically served as a robust hedge against inflation. The other options are not supported by the provided data: while industrial production has a positive coefficient, its impact on farmland returns is less pronounced than inflation. The yield to worst, representing interest rates, has a negative coefficient, indicating an inverse relationship, not a direct hedge. Commodity prices like oil, corn, and wheat were found to be statistically insignificant determinants in this specific model.

The shift from an income-only spending model to a total return approach for endowments, as facilitated by the Uniform Management of Institutional Funds Act of 1972, allowed for a more dynamic management strategy. This enabled endowments to consider both income and capital appreciation to meet spending needs while preserving or growing the real value of the corpus. A portfolio with a 3% yield and 7.5% total return can support a 5% spending rate by reinvesting the excess 2.5% to offset inflation, thus maintaining the real value of the endowment. Conversely, a portfolio solely reliant on income would need a 5% yield to support a 5% spending rate, leaving no room for inflation adjustment or corpus growth. The question tests the understanding of how total return investing allows for greater flexibility in meeting spending needs and preserving purchasing power compared to an income-only approach.

The question asks to evaluate the performance of PE Fund 1 relative to its benchmark. The provided data shows that PE Fund 1’s IRR (Internal Rate of Return) was 17%. The benchmark analysis indicates that for vintage year 2000 buyout funds, the IRR range from inception to December 31, 2006, had a maximum of 34.8%, an upper quartile of 13.2%, a median of 6.5%, and a minimum of 0.0%. Since PE Fund 1’s IRR of 17% falls between the upper quartile (13.2%) and the maximum (34.8%), it signifies performance that is better than the median and upper quartile, placing it in the top tier of its peer group. Therefore, PE Fund 1 demonstrated superior performance compared to its benchmark.

This question tests the understanding of how non-financial benefits, such as aesthetic enjoyment, can influence the pricing and expected financial returns of assets like artwork. When an asset offers significant non-financial utility, demand for it can increase beyond what purely financial considerations would dictate. This increased demand can drive up prices, which in turn lowers the potential future financial returns for investors, assuming a reversion to a risk-adjusted equilibrium. The trade-off described is between the financial return and the aesthetic benefit; higher aesthetic benefit leads to higher prices and lower expected financial returns.

The core principle of futures markets, as outlined in the CAIA curriculum, is the daily settlement of gains and losses in cash. This daily cash flow, while ensuring counterparty risk is managed, creates ongoing transaction costs. These costs arise from the need to settle profits and losses each day, impacting the net return to the investor. The absence of a net liquidating value for futures contracts and the requirement for collateral are also key features, but the daily cash settlement is the direct driver of these recurring transaction costs.

The CAIA designation emphasizes practical application and understanding of concepts rather than rote memorization of specific data points or exhibit details. While Exhibits 14.5, 14.6, and 14.7 provide historical data on VC fund performance, the core principle being tested here is the inherent uncertainty in long-term forecasting, particularly within dynamic sectors like venture capital. The text explicitly states that ‘long-term projections in particular are fraught with considerable uncertainty, regardless of the kind of forecasting problem.’ It further highlights that ‘statistical extrapolation techniques fail’ when the environment changes radically and that ‘forecasts do not communicate uncertainty.’ Scenarios are presented as a superior tool for navigating this uncertainty by outlining ‘reasonably plausible but structurally different futures.’ Therefore, understanding the limitations of statistical forecasting and the utility of scenario planning in the face of such uncertainty is the key takeaway, not the specific percentages or vintage years shown in the exhibits.

The bottom-up approach to private equity portfolio design prioritizes the selection of individual fund managers based on their perceived ability to generate alpha, meaning returns uncorrelated with the broader market. This strategy acknowledges the significant dispersion in performance between top-tier and lower-tier private equity funds. While diversification across multiple funds is a risk management tool, the core tenet of the bottom-up method is the intensive research and due diligence focused on identifying and investing with the most skilled managers, believing their expertise is the primary driver of superior returns.

Rho measures the sensitivity of a convertible bond’s value to changes in interest rates. A positive rho indicates that the convertible’s value increases as interest rates rise, which is typically observed when the convertible is trading closer to its straight bond value (i.e., further from parity). Conversely, a negative rho suggests the convertible’s value decreases as interest rates rise, common when the convertible is trading more like equity. The question describes a scenario where a convertible’s value is highly sensitive to interest rate changes, implying a significant rho. The explanation that rho is the derivative of the convertible’s value with respect to interest rates is the fundamental definition. The other options describe different Greeks: Delta (sensitivity to underlying stock price), Gamma (sensitivity of delta to stock price changes), and Vega (sensitivity to implied volatility).

The question tests the understanding of surplus risk in pension plans, which is defined as the tracking error between the plan’s assets and its liabilities. Surplus risk arises from the volatility of both asset returns and liability values, and is exacerbated by a low correlation between them. Exhibit 4.3 illustrates this by showing that even with a negative correlation (-0.26) between assets and liabilities, the volatility of the surplus (17.4%) was higher than the volatility of either assets (11.9%) or liabilities (9.9%). Therefore, a plan sponsor aiming to minimize surplus risk would seek to align the characteristics of their assets with those of their liabilities, particularly their duration and sensitivity to interest rate changes, to reduce the tracking error.

The question asks to identify the most appropriate benchmark for a private equity fund’s performance, considering its potential impact on an investor’s public equity allocation. The provided text highlights that private equity allocations are often made at the expense of public equity, implying a perceived opportunity cost. Therefore, comparing the private equity fund’s returns to those of public securities, such as a relevant stock market index like the CAC 40, is a logical approach to assess this opportunity cost. This comparison, often referred to as a Public Market Equivalent (PME), helps investors understand if the illiquidity and risk premium associated with private equity are adequately compensated compared to simply investing in public markets. Benchmarking against absolute returns of peer groups or vintage years is also valid but doesn’t directly address the opportunity cost relative to public markets. The IIRR itself is a performance metric, not a benchmark.

Capital at Risk (CaR) in managed futures, as described, represents the maximum potential loss if all individual positions in a portfolio simultaneously hit their predetermined stop-loss levels. The provided example calculates the loss for each contract based on a 1% adverse price movement of its notional value. The total CaR is the sum of these individual potential losses. Therefore, to determine the total CaR for the portfolio, one must sum the potential losses from each contract, assuming each stop-loss is triggered. In this scenario, the individual losses are $2,073 (S&P 500), $179 (Corn), $475 (Soybeans), $9,877 (Eurodollars), $1,266 (U.S. long bond), $403 (Crude oil), $902 (Gold), and $1,396 (Japanese yen). Summing these values yields $16,571. The question asks for the total Capital at Risk, which is this sum.

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, and their capability to source difficult-to-borrow securities, is crucial. This directly impacts the manager’s ability to execute their strategy effectively and manage settlement risk. Naked short selling, where borrowing arrangements are not in place before selling, is a significantly riskier practice and often restricted, making the distinction between naked and covered short selling a vital part of due diligence. The gross and net exposure figures provide insight into the fund’s directional market risk and overall investment style, while the sources of return (static, market-linked, and alpha) help investors understand how the fund generates profits and whether short positions are primarily for hedging or for generating returns. Liquidity of underlying assets is paramount, as illiquid positions can lead to unrealized gains and difficulties in exiting positions without significant price impact. The manager’s policy on non-listed stocks, potential investment restrictions due to insider information, and the use of derivatives (which can introduce leverage, liabilities, and counterparty risk) are also critical components of operational due diligence. Finally, how foreign exchange risk is managed is important for funds with international investments.

The question probes the understanding of why momentum strategies might be less robust in equity futures compared to other asset classes like commodities or currencies. The provided text highlights that commodities have natural hedgers (producers/consumers) willing to pay a premium to mitigate inventory or supply chain risks, creating a demand for hedging that can support momentum. Equity futures, however, are primarily driven by expected cash payoffs. A fully hedged equity position would yield the risk-free rate. The text explicitly states that a significant and natural hedging demand does not exist in equity futures markets, weakening the theoretical basis for momentum in this asset class. Option B is incorrect because while behavioral biases are discussed, the core difference lies in the hedging demand. Option C is incorrect as the text suggests the opposite for commodities. Option D is incorrect because the text implies that while momentum exists in currencies, the hedging argument for equities is fundamentally weaker.

The question tests the understanding of how credit risk impacts the valuation of a convertible bond within a binomial framework. The provided text explains that the discount rate used for backward induction is adjusted based on the probability of conversion. Specifically, the discount rate is a blend of the risk-free rate (when conversion is certain) and a rate reflecting the issuer’s credit spread (when conversion is unlikely). The formula provided is: Discount rate = [Prob.Conv. * (1 + R_f)] + [(1 – Prob.Conv.) * (1 + R_f + CS)] – 1. This means that as the probability of conversion decreases (i.e., the stock price falls to lower nodes), the weight given to the credit-inclusive rate increases, thus raising the discount rate. This higher discount rate, when applied in the backward induction process, will reduce the calculated value of the convertible bond at earlier nodes. Therefore, a lower probability of conversion leads to a higher discount rate and a lower bond valuation.

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.

Venture capital (VC) fund partners typically engage deeply with the companies they invest in, often taking active roles on the board of directors and contributing to strategic planning. This hands-on approach is characteristic of VC investments, which focus on early-stage, high-growth potential companies. Buyout funds, conversely, target more mature businesses and their primary strategies often revolve around financial engineering (modifying capital structure) and operational enhancements, rather than the fundamental business model innovation that VCs pursue. Funds of funds offer diversification but are not directly involved in the strategic direction of underlying portfolio companies. A GP’s life cycle describes the fund manager’s progression, not their investment strategy’s focus.

The question tests the understanding of how combining factors with low correlations can improve a portfolio’s risk-adjusted return. Exhibit 37.3 shows that the Equally Weighted (EW) portfolio, which represents a neutral exposure to Mkt-RF, SMB, and HML, has a significantly higher annualized return-to-standard deviation ratio (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, as indicated in the exhibit (SMB-Mkt-RF: 0.06, HML-Mkt-RF: -0.31, HML-SMB: -0.13). Diversification benefits arise from combining assets or strategies that are not perfectly correlated, leading to a smoother return profile and a higher Sharpe ratio. Therefore, the primary benefit of combining these factors into an equally weighted portfolio, as demonstrated, is the enhancement of the risk-adjusted return.

This question tests the understanding of calendar spreads and their relationship to market conditions like contango and backwardation, as well as the different types of spreads (bull vs. bear). A bull spread involves being long the near-term contract and short the distant contract. In a contango market, where future prices are higher than spot prices, the expectation for a bull spread is that the spread will narrow (i.e., the distant contract price will fall relative to the near-term contract price). This narrowing is beneficial for the bull spread investor who is short the distant contract. Conversely, in backwardation, where future prices are lower than spot prices, a bull spread investor would hope for the spread to widen, which would also be profitable as they are short the more expensive distant contract. The scenario describes a contango market and an anticipation of the curve flattening, which means the spread is expected to narrow. Therefore, a bull spread position (long near, short far) would profit from this expected narrowing.

This question tests the understanding of how non-financial benefits, such as aesthetic enjoyment, can influence the pricing and expected financial returns of assets like artwork. When an asset offers significant non-financial utility, demand for it can increase beyond what purely financial considerations would dictate. This increased demand can drive up prices, which in turn lowers the expected financial return for investors, assuming the underlying value drivers remain constant. The trade-off described is between the financial return and the aesthetic benefit, where higher aesthetic appeal can lead to lower financial returns due to increased purchase prices.