CAIA Level 2 Quiz 97

The CAIA Level I curriculum emphasizes that while absolute performance levels of CTAs may not exhibit strong persistence, certain characteristics of their return distributions do. Specifically, funds that are volatile tend to remain volatile over time, and those with low correlation to other trend followers tend to maintain that low correlation. This suggests that while predicting future returns based on past returns is challenging due to luck dominating skill over shorter horizons, other statistical properties of their performance are more stable and can be used for quantitative analysis and portfolio construction.

The question asks to identify the index that exhibited the highest volatility relative to its return during the 2000-2011 subperiod, as indicated by its Sharpe ratio. A lower Sharpe ratio signifies poorer risk-adjusted performance, meaning higher volatility for a given level of return, or lower returns for a given level of volatility. Examining Exhibit 38.5, the Sharpe ratios for the 2000-2011 period are: HFRI FOF Conservative Index (0.063), HFRI FOF Diversified Index (0.094), HFRI Fund of Funds Composite Index (0.102), CISDM Fund of Funds Index (0.315), CISDM Fund of Funds Diversified Index (0.308), HFRI Fund of Funds Index ( -0.106), and S&P 500 (0.000). The HFRI Fund of Funds Index has the lowest (most negative) Sharpe ratio (-0.106), indicating the worst risk-adjusted performance and thus the highest volatility relative to its return during this specific subperiod.

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%) alone. Therefore, a higher surplus risk implies a greater potential for the plan’s assets to deviate from its liabilities, leading to potential underfunding or overfunding.

Liability-Driven Investing (LDI) aims to minimize the volatility of a pension plan’s surplus (the difference between assets and liabilities). This is achieved by constructing a portfolio whose returns are closely aligned with the changes in the plan’s liabilities. A key strategy within LDI is to match the duration of the asset portfolio to the duration of the pension liabilities. By doing so, if interest rates change, both the assets and liabilities will move in value by a similar amount, thus stabilizing the surplus. While investing in fixed income with a matching duration is a primary method, other techniques like derivatives overlays (e.g., interest rate swaps) can also be employed to achieve this correlation. The other options describe strategies that might increase or decrease surplus risk in different ways, but they do not directly represent the core principle of LDI as described.

The core of managed futures strategies, particularly trend-following, relies on the observation that certain futures markets, like commodities, can exhibit persistent price movements (momentum). This persistence is often attributed to the time it takes for inventory levels to adjust to supply or demand shocks. When inventories deviate from optimal levels, it signals past shocks and potential risk premium changes, leading to predictable future price movements. Strategies that capitalize on these trends, by buying assets that have recently performed well and selling those that have performed poorly, aim to capture these predictable returns. The concept of a zero-sum game in futures trading is generally true when considering only speculators trading amongst themselves. However, the presence of hedgers, who use futures to manage business risks, introduces a non-zero-sum element, creating opportunities for speculators to profit from the risk premiums these hedgers are willing to pay.

The core of actionable risk management, as highlighted in the provided text, is the ability and willingness of the risk manager (or designated authority) to actively reduce risk exposure. This involves having the mandate to override portfolio managers and a demonstrated history of doing so when risk parameters are breached. Simply having access to risk reports or understanding quantitative measures like VaR is insufficient if the risk manager cannot or will not enforce risk reduction measures. The ability to cut risk, coupled with the authority to do so, is paramount for effective risk management.

Defined benefit (DB) plans are characterized by the employer assuming the full investment risk. The employer is obligated to provide a predetermined income stream to retirees, regardless of the actual investment performance of the plan’s assets. This means that if the portfolio underperforms, the employer must still meet the promised benefit obligations, potentially requiring additional contributions. In contrast, defined contribution (DC) plans shift the investment risk to the employee, who receives whatever balance has accumulated from contributions and investment returns. Governmental social security plans often involve a mix of funding sources and risk-sharing mechanisms, but the core distinction of a DB plan is the employer’s guarantee of the benefit amount.

The payoff-distribution approach to hedge fund replication aims to construct a portfolio using liquid assets that mimics the return distribution of a target hedge fund. This involves using the inverse of the hedge fund’s cumulative distribution function (CDF) and the CDF of a reserve asset to derive a payoff function. The core idea is to transform the returns of the reserve asset into a payoff stream that matches the statistical properties of the hedge fund’s returns. While this method focuses on matching the distribution, it does not inherently guarantee the replication of other crucial hedge fund characteristics like diversification benefits (correlations with other assets) or the mean return (first moment). Empirical studies have shown that while volatility can be reasonably matched, replicating skewness and kurtosis, and especially correlations, remains challenging, and the success of matching the overall distribution can vary significantly by strategy and requires long out-of-sample periods.

The core challenge in applying Modern Portfolio Theory (MPT) to private equity lies in accurately estimating the risk and return characteristics, particularly the correlation with other asset classes. Private equity valuations are often infrequent and subject to biases, which can artificially dampen measured volatility and correlation. This makes it difficult to use standard MPT optimization models without significant adjustments. While MPT suggests that adding non-correlated assets improves a portfolio’s risk-return profile, the inherent illiquidity and valuation complexities of private equity necessitate a more nuanced approach than simply using historical public market data as a proxy.

The question probes the understanding of how real options, specifically the option to shut down production, influence commodity forward curves. When a commodity price falls below the marginal cost of production, a producer can exercise the option to cease extraction. This action limits the downside volatility of future prices, as producers are not compelled to sell at a loss. Consequently, this real option tends to flatten the forward curve, particularly for longer-dated contracts, by capping extreme price declines. The other options describe different market phenomena or incorrect impacts on the forward curve. Option B describes a situation that would steepen a curve, not flatten it. Option C misinterprets the impact of inventory management on the forward curve. Option D incorrectly attributes the flattening effect solely to speculation without considering the role of real options.

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 was 17%. The benchmark analysis for European private equity funds (vintage year 2000, buyout focus) from inception to December 31, 2006, indicates the following quartiles: Max 34.8%, Upper Quartile 13.2%, Median 6.5%, Lower Quartile 0.0%, and Min -9.5%. Since PE Fund 1’s IRR of 17% falls between the Upper Quartile (13.2%) and the Maximum return (34.8%), it signifies performance that is significantly above average and approaching the top tier of its peer group. Therefore, it is considered excellent relative to its benchmark.

This question assesses the understanding of how to value real assets, specifically infrastructure, using a discounted cash flow (DCF) approach. The core principle is to project future cash flows generated by the asset and discount them back to the present using an appropriate discount rate that reflects the risk. Option A correctly identifies the need to project future cash flows and discount them. Option B is incorrect because while depreciation is a non-cash expense, it impacts taxable income and thus cash flow, but the primary method is DCF, not just depreciation. Option C is incorrect as market comparables are more relevant for assets with active markets, and infrastructure often lacks this. Option D is incorrect because while operational efficiency is important for cash flow generation, it’s a component of the cash flow projection, not the primary valuation methodology itself.

The core of unsmoothing appraisal-based returns lies in estimating the unobservable ‘true’ price from observable ‘reported’ (smoothed) prices. Equation 16.4 in the provided text, P_{true,t} = P_{reported,t-1} + \frac{1}{\alpha} (P_{reported,t} – P_{reported,t-1}), illustrates this relationship. Here, \alpha is the decay parameter, representing the speed at which true prices influence reported prices. A higher \alpha means reported prices react more quickly to true price changes. The equation shows that the true price is the previous reported price adjusted by the most recent reported price change, scaled by \frac{1}{\alpha}. If \alpha = 0.40, then \frac{1}{\alpha} = 2.5. This means that a $10 increase in the reported price implies a $25 difference between the current true price and the previous reported price. This indicates that the smoothed price change of $10 is a muted reflection of a larger underlying true price change. Therefore, to estimate the true price change, one must amplify the observed reported price change by the inverse of the \alpha parameter.

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 purely income-based would focus only on rental income without considering capital appreciation or depreciation.

Kaldor’s definition of speculative stocks focuses on the difference between actual holdings and what would be held if prices were expected to remain unchanged. This implies that any position taken with the primary motivation of profiting from price changes, beyond what is needed for normal business operations or hedging, is speculative. Working’s definition, conversely, defines hedging as any futures activity by those handling the physical commodity, with speculation being any activity not classified as hedging. Therefore, an investor holding commodities for inflation hedging, currency hedging, or hedging against changes in other asset prices, as described in the scenario, would not be considered speculating according to Kaldor’s framework, as these are reasons beyond simple price appreciation and are tied to broader financial risk management. Working’s definition might classify some of these as hedging if they involve physical commodity handling, but the question specifically asks about the distinction between speculative and non-speculative based on the motivation of price change expectation versus other business/financial reasons.

The question tests the understanding of the challenges in evaluating managed futures performance due to data reporting practices. Selection bias occurs when funds with strong performance voluntarily report their data, while those with weak performance may withhold it, leading to an overestimation of industry-wide returns. Look-back bias is related but specifically refers to the practice of resuming reporting after a period of poor performance has ended, effectively masking prior underperformance. Survivorship bias is a similar concept where only funds that have survived are included in analyses, excluding those that failed. The lack of a central, mandatory database exacerbates these issues, making it difficult to obtain a truly representative sample of managed futures traders’ performance.

The Bailey criteria are a set of standards used to evaluate the suitability of a benchmark. For private equity, benchmarks often fall short on several of these criteria. ‘Unambiguous/knowable’ is problematic because private equity benchmarks typically provide aggregate data rather than clearly identifiable individual assets. ‘Investable’ is also an issue, as private equity benchmarks are not directly investable in the same way public market indices are. ‘Measurable’ is challenged by the infrequent and appraisal-based nature of private equity valuations, leading to difficulties in frequent performance calculation. ‘Specified in advance’ is less critical for private equity, which is often viewed as an absolute-return asset class where manager incentives aren’t tied to index performance. However, the question asks which criterion is LEAST problematic, implying a relative strength. While all criteria have challenges in private equity, the ‘specified in advance’ criterion is often considered less of a practical impediment compared to the data availability and investability issues. Managers are evaluated on absolute performance, and while a benchmark might be used for context, its pre-specification is not as fundamental to the evaluation process as the clarity and measurability of the benchmark itself.

Hedonic price indices estimate the value of properties that have not recently traded by modeling property prices as a function of their specific characteristics. This involves fitting an econometric model using data from recent transactions to determine the relationship between property features (like size and quality) and their prices. The estimated parameters from this model are then applied to all properties within the index, including those that did not transact, to infer their current values. This approach directly addresses property heterogeneity, unlike repeat-sales indices which focus solely on price changes of properties that have transacted multiple times.

Convertible arbitrage strategies aim to profit from mispricings between a convertible bond and its underlying stock. A key aspect of this strategy is understanding the relationship between the convertible bond’s price, its parity (the market value of the underlying shares), and the conversion premium. When the conversion premium is high, it suggests the convertible bond is relatively expensive compared to the value of the shares it can be converted into. In such a scenario, a convertible arbitrage strategy would involve selling the expensive convertible bond (shorting it) and simultaneously buying the equivalent value of the underlying stock. This is a classic “sell high, buy low” approach applied to the convertible bond market. The goal is to capture the difference between the bond’s premium price and the underlying stock’s value, expecting the premium to revert to its mean or the bond’s price to converge with its parity.

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 the active, opportunistic deviations of TAA.

When an investor replaces a poorly performing CTA manager with a new one, the investor effectively loses the benefit of the previous manager’s losses not being subject to performance fees. This is because the new manager’s gains will be subject to performance fees until a new high-water mark is established. To simply break even, the new manager must generate returns that not only compensate for the previous manager’s underperformance but also cover the performance fee on those gains. If the previous manager had a drawdown of 25%, meaning the Net Asset Value (NAV) is 75% of the initial investment, the new manager needs to achieve a 33.33% return to recover the initial capital (0.75 * 1.3333 = 1.0). If the performance fee is 20%, this 33.33% return must be gross of fees. Therefore, the new manager needs to earn a return of 41.67% (33.33% / (1 – 0.20)) to offset the performance fee and achieve a net return equivalent to breaking even on the initial capital.

Operational due diligence for hedge funds involves scrutinizing the internal controls and processes that safeguard investor assets and ensure accurate reporting. A critical aspect of this is ‘following the cash,’ which entails understanding who has the authority to move funds and what controls are in place to prevent unauthorized transactions. Requiring dual signatures for large cash movements is a fundamental internal control designed to mitigate the risk of fraud or error. While other options address important operational aspects, they do not directly pertain to the specific control mechanism for cash movement.

The question probes the rationale behind the Yale Endowment’s exclusion of corporate bonds, a core tenet of the endowment model as described. The provided text explicitly states that Yale avoids corporate bonds due to the principal-agent conflict, where corporate management might prioritize stockholders over bondholders. Furthermore, it highlights that the incremental return from corporate bonds over government bonds is minimal, and their liquidity can diminish during extreme market events, counteracting the desired tail-hedge effect of fixed income. Therefore, the primary reasons are the inherent conflict of interest and the potential for illiquidity during crises, which outweigh the marginal yield benefit.

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 rapid and severe price dislocation. The text explicitly states that many quants were “betting on the positive drift for each factor to continue as well as the low correlation structure to hold,” and when these assumptions failed simultaneously, the market impact was amplified. The failure was not due to flawed models per se, but rather the systemic risk arising from the homogeneity of strategies and the synchronized exit. High-frequency trading (HFT) strategies, which operated on a different time horizon and often started and ended in cash daily, generally performed well during this period, indicating that the issue was specific to the fundamental-factor space and the way these strategies were implemented and unwound by a large number of participants.

The question tests the understanding of how volatility shocks impact commodity futures prices, specifically in relation to the convenience yield and risk premium. Research indicates a positive relationship between volatility and convenience yield. During periods of positive risk premium, an increase in volatility leads to a rise in both the risk premium and the convenience yield. Conversely, in periods of negative risk premium, a volatility shock would cause these to decline. Therefore, strategies like momentum models, which are positively correlated with volatility, may capture excess returns when volatility spikes occur, particularly if these spikes are associated with a positive risk premium.

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 credit spread will lead to a higher discount rate when the probability of conversion is low, and conversely, a lower discount rate when conversion is highly probable.

The question tests the understanding of how intellectual property (IP) is valued and the inherent risks associated with early-stage IP investments. Newly created IP, such as exploratory research or pending patents, is characterized by significant uncertainty regarding its future value and utility. This uncertainty is analogous to venture capital investments, where a high failure rate is expected, but a small number of successes can generate substantial returns. Mature IP, conversely, has established utility and a more predictable income stream, leading to more certain valuations and market pricing that reflects known risks. Therefore, the scenario described, involving newly created IP with uncertain outcomes, aligns with the characteristics of early-stage or ‘ex ante’ IP investments.

Operational due diligence for fixed-income arbitrage funds requires a thorough examination of their cash management practices. Given the high leverage and the nature of instruments used (futures, options, swaps), significant cash is often held to meet margin calls. The question probes the investor’s understanding of how this cash should be managed. The correct approach emphasizes liquidity and capital preservation, investing in high-quality, short-term instruments, rather than seeking alpha generation. Option B is incorrect because while cash is essential for liquidity, its primary purpose in this context is not alpha generation. Option C is incorrect as it suggests a focus on yield, which contradicts the principle of prioritizing liquidity and safety for operational cash. Option D is incorrect because it implies that cash balances are primarily for investment opportunities, overlooking their critical role in managing leverage and margin requirements.

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 potentially true, do not represent the primary portfolio-level benefits that drive real estate’s inclusion in a diversified strategy.

The bottom-up cash flow projection method for valuing a private equity fund involves a granular analysis of individual portfolio companies. This includes assessing the quality of the fund manager, the legal structure, and the current holdings. Crucially, it requires projecting exit multiples and timing for each company to derive company-level cash flow streams. These streams are then aggregated and adjusted for the partnership structure to arrive at net cash flows for the limited partner. These net cash flows are subsequently discounted to determine the present value of the fund. While this approach aims for economic reality, its reliance on detailed, company-specific exit projections can be resource-intensive and challenging, especially when such information is scarce.