CAIA Level 2 Quiz 24

The initial screening phase in private equity fund due diligence is designed to efficiently filter out investment proposals that do not align with the investor’s predefined portfolio objectives and criteria. This includes assessing factors such as industry sector, investment stage, geographical focus, and minimum quality standards. Proposals that fail to meet these initial requirements are immediately disqualified, saving valuable time and resources that would otherwise be spent on more in-depth analysis of unsuitable opportunities. The subsequent stages, such as meeting the team and detailed evaluation, are reserved for those proposals that successfully pass this preliminary screening.

The core issue with smoothed data, particularly from appraisals, is that it doesn’t reflect the true, immediate market movements. This delay in price adjustment creates opportunities for arbitrage in perfect markets. An arbitrageur could exploit this by buying an asset whose price is expected to rise due to a market movement but hasn’t yet adjusted, or short-selling an asset whose price is expected to fall. In the scenario presented, the ‘strongly smoothed’ return series shows a delayed response to a market surge. An arbitrageur could capitalize on this by buying the smoothed asset after the market surge but before its price fully reflects that surge, anticipating a future price increase (alpha) as the smoothing effect unwinds. This strategy is viable because the delayed price adjustment represents a predictable, exploitable inefficiency in the market.

The scenario describes a situation where implied volatility on two-year and 10-year German interest rates was priced identically, despite a high probability of further European Central Bank (ECB) easing which would typically lead to a steeper yield curve. Global macro funds exploited this by selling out-of-the-money puts on the shorter-term (two-year) swaps and buying out-of-the-money puts on the longer-term (10-year) swaps. This structure is a bet on yield curve steepening. If the yield curve steepens (long-term rates rise relative to short-term rates, or short-term rates fall more than long-term rates), the value of the long put on the 10-year swap increases, while the value of the short put on the two-year swap decreases (or becomes less valuable if it remains out-of-the-money). The optimal outcome described is stabilization with a sell-off led by the long end, which would cause the yield curve to steepen significantly, leading to substantial profits on the long 10-year puts and minimal losses (or gains if the short puts expire worthless) on the short two-year puts. The ECB’s subsequent 50 basis point cut further steepened the curve, validating the strategy and leading to significant gains.

Convertible arbitrage strategies aim to profit from mispricings between a convertible bond and its underlying equity. A key component of this strategy involves hedging the equity exposure. Delta hedging is a common method used to manage the risk associated with the equity component of the convertible bond. The delta of a convertible bond is influenced by the price of the underlying stock and the terms of the embedded option. By shorting a specific amount of the underlying stock, determined by the convertible bond’s delta, the arbitrageur seeks to create a market-neutral position. This hedging strategy aims to offset potential losses from adverse stock price movements, allowing the arbitrageur to capture the mispricing in the convertible bond itself, often related to the credit spread or the option’s implied volatility. The other options describe different hedging or risk management techniques that are not the primary method for managing the equity exposure of a convertible bond in this context.

The question tests the understanding of how investors gain exposure to commodity markets. While direct physical ownership is an option, it’s often impractical due to storage costs and the perishable nature of many commodities. Equity ownership in commodity-producing firms offers indirect exposure but mixes equity risk (equity beta) with commodity risk. Commodity indices, and derivatives based on them like swaps, are specifically designed to provide pure commodity beta exposure without the complexities of physical storage or the confounding factor of equity risk. Commodity index swaps are highlighted as a preferred vehicle for institutional investors due to their liquidity, competitive pricing, and the ability for investors to manage collateral for potentially higher returns.

When an investor decides to allocate capital to Commodity Trading Advisors (CTAs), a crucial decision involves the number of CTAs to include in the portfolio. Investing in a single CTA offers simplicity, particularly if the CTA is large and diversified with a strategy that closely tracks a benchmark. However, this approach concentrates risk on a single manager, potentially limiting the diversity of trading models employed. To mitigate these risks and achieve a more robust investment program, a diversified portfolio of CTAs is generally recommended. This diversification helps to reduce idiosyncratic risk and capture a broader range of market opportunities, aligning with the principles of portfolio construction aimed at enhancing risk-adjusted returns.

Closed-end real estate funds, unlike open-end funds, do not allow for the creation or redemption of shares directly with the fund. This structural difference prevents arbitrageurs from continuously aligning the fund’s market price with its Net Asset Value (NAV). Consequently, closed-end funds, including those focused on real estate, are prone to trading at significant premiums or discounts to their NAVs, particularly when the underlying assets’ valuations are not readily observable or market-based. This divergence is a key characteristic distinguishing them from open-end funds where such arbitrage mechanisms are active.

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.

Operational due diligence for a hedge fund investor involves scrutinizing the fund’s back-office operations to ensure robustness and mitigate risks. A key aspect is evaluating the fund’s relationship with its administrator. A well-established and reputable administrator provides a layer of confidence regarding the accuracy of Net Asset Value (NAV) calculations, proper expense management, trade settlement, and dividend processing. Conversely, a less-known or smaller administrator necessitates a more rigorous review of their internal controls, accounting procedures, and error monitoring to ensure investor capital is protected. Prime brokers are also critical, offering services beyond custody and lending, including portfolio reporting, risk analysis, and capital introductions, all of which contribute to understanding the fund’s operational framework and market access.

The question asks to identify the manager with the most favorable risk-adjusted return based on the provided exhibit. The Sharpe ratio is the standard metric for risk-adjusted return, calculated as (Return – Risk-Free Rate) / Standard Deviation. A higher Sharpe ratio indicates a better risk-adjusted performance. Examining Exhibit 32.6, Manager C has the highest Sharpe ratio of 0.63, which is superior to Manager A’s 0.40 and Manager B’s 0.58. The MSCI World Equity Index has a Sharpe ratio of 0.16, and the Barclays Global Aggregate Bond Index has a Sharpe ratio of 0.55. Therefore, Manager C demonstrates the best risk-adjusted performance among the managed futures managers.

The scenario describes a situation where a country’s central bank is forced to defend its currency’s peg within a fixed exchange rate system. This defense involves selling foreign reserves to buy its own currency, which depletes the reserves. To replenish these reserves, the central bank might resort to borrowing from international markets. The question tests the understanding of the mechanisms and consequences of defending a currency peg under speculative pressure, a core concept in global macro and currency strategies within the CAIA curriculum.

This question assesses the understanding of the strategic purpose of stop-loss orders in global macro trading, as articulated by experienced managers like Bruce Kovner. The core principle is that a stop-loss should be triggered by a change in the market’s assessment of the trade’s viability, not by a predetermined risk tolerance. Option A correctly captures this by emphasizing the trade’s validity being compromised. Option B is incorrect because while risk capital allocation is a broader portfolio management concept, the stop-loss itself is about trade integrity. Option C is incorrect as it focuses on the maximum acceptable loss, which is a secondary consideration to the trade’s fundamental error signal. Option D is incorrect because it misinterprets the purpose of a stop-loss as a tool for managing overall portfolio exposure rather than for individual trade discipline.

The payoff-distribution approach to hedge fund replication aims to match the entire probability distribution of the hedge fund’s returns, not just the mean or specific moments. This is achieved by constructing a trading strategy that generates a payoff function, G(Rr), which maps the returns of a reserve asset (Rr) to the desired hedge fund return distribution. This process is analogous to derivative pricing, where a synthetic portfolio is created to mimic a target payoff. The factor-based approach, in contrast, primarily focuses on matching the hedge fund’s returns to a set of underlying factors, which may not capture the full distributional characteristics, especially higher moments like skewness and kurtosis, or the precise shape of the probability curve. Therefore, the payoff-distribution approach is considered a more ambitious definition of replication because it seeks to replicate the entire probability distribution, implying equality in distribution, which is a stronger condition than simply matching factor exposures.

The core of a momentum strategy in commodity futures, as described, relies on the persistence of price trends. 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. The inability to instantaneously adjust production or consumption means these inventory imbalances, and consequently price trends, can persist for a period. Therefore, a strategy that buys commodities with recent positive price performance (momentum) and sells those with negative performance aims to capture these persistent trends. The link to backwardation and contango is a related concept where backwardated markets (positive risk premium) are associated with low inventories, which in turn can drive momentum.

The question tests the understanding of the liquidity premium in real estate investments. Private real estate is inherently illiquid due to factors like the uniqueness of properties, high transaction costs (which can approach 10% of the property value), and the lengthy sales process involving marketing, due diligence, and negotiation. This illiquidity necessitates a higher expected return to compensate investors for the risks and inconveniences associated with holding such assets. This additional return is known as the liquidity premium. Therefore, investors who are less concerned with immediate access to their capital should consider allocating to illiquid assets like private real estate to potentially benefit from this premium.

The J-curve in private equity reflects the typical pattern of fund performance over time. Initially, the fund experiences negative returns due to management fees, operational costs, and the time lag in valuing and realizing investments. As investments mature and are successfully exited, the fund’s performance improves, leading to positive returns. The question asks about the primary driver of this initial negative performance phase. Option A correctly identifies the immediate deduction of fees and expenses, coupled with the delayed recognition of potential gains, as the core reasons for the early dip. Option B is incorrect because while capital calls are part of the process, they don’t inherently cause negative returns; it’s how the capital is deployed and managed that impacts performance. Option C is incorrect; the valuation of successful investments typically occurs later, not immediately, contributing to the J-curve’s upward trend, not its initial decline. Option D is incorrect because while market volatility can affect valuations, the fundamental structure of private equity fund accounting and investment cycles is the primary, consistent driver of the J-curve’s initial negative phase.

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 deviation from this baseline, driven by price expectations, constitutes speculation. 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. The core distinction lies in the primary driver of the position: Kaldor emphasizes the expectation of price change as the defining characteristic of speculation, while Working categorizes based on involvement with the physical commodity. Therefore, an investor holding commodities solely for inflation hedging, as described, would not be considered speculating under Kaldor’s framework because the primary motivation is not solely price appreciation but a broader risk management objective.

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 a positive risk premium, an increase in volatility leads to a rise in both the risk premium and the convenience yield. Conversely, in periods of a 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 and persist, especially if these spikes are consistent with the existence of a convenience yield.

Latency arbitrage, as described, exploits temporary price discrepancies arising from differences in the speed at which various trading venues update their quotes. A key mechanism involves identifying a security whose price has moved on a faster-updating exchange but has not yet been reflected on a slower-updating exchange. The arbitrageur simultaneously sells the security on the faster exchange and buys it on the slower exchange, aiming to profit when the slower exchange’s price eventually aligns with the faster one. This strategy relies on technological infrastructure and the timing of quote updates, not on fundamental analysis or insider information. While it contributes to market efficiency by correcting stale prices, the core profit driver is the timing advantage derived from technological infrastructure.

The question tests the understanding of how the financialization of commodity markets can impact their characteristics, specifically focusing on the term structure of futures. The provided text mentions a study by Haigh et al. (2007) that investigated the impact of increased investor presence, such as commodity swap dealers and hedge funds, on the term structure of West Texas Intermediate (WTI) crude oil futures. This study found an increase in price efficiency and co-integration between near-month and longer-maturity futures. This implies that as more financial participants enter the market, the relationship between different futures contracts becomes tighter and more responsive to information, leading to a more efficient price discovery mechanism across the futures curve.

The provided exhibit illustrates a forward curve for natural gas, which is upward sloping (in contango). This upward slope, particularly pronounced during the fall-winter period, is attributed to the costs and limitations associated with storing natural gas. When storage capacity is nearing its limits or when demand is expected to surge (like during winter), the cost of carrying inventory increases, leading to higher forward prices. This reflects the incentive for market participants to hold inventory to meet future demand, and the cost associated with that storage. The question tests the understanding of how storage costs and demand expectations influence the shape of a commodity’s forward curve, a core concept in commodity market analysis.

This question tests the understanding of the typical portfolio characteristics of fundamental equity long/short hedge funds. The text explicitly states that these funds are typically highly concentrated, holding a relatively small number of stocks, with core positions ranging from three to ten and non-core positions from twenty to forty. This contrasts with strategies like equity market neutral or statistical arbitrage, which may hold hundreds or thousands of positions. Therefore, a portfolio with over 100 distinct equity positions would be considered highly diversified for a fundamental long/short strategy, not concentrated.

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 mimicking the asset allocation of successful endowments is a common strategy, the text explicitly states that this alone does not guarantee similar performance. The key differentiator highlighted is the value added through superior manager selection and market timing, which are active management skills that cannot be replicated solely by replicating an asset allocation. Therefore, simply adopting the asset mix without the underlying expertise in choosing managers and timing the market would likely lead to suboptimal results.

The question probes the understanding of the ‘increased allocation to active funds hypothesis’ as a reason for the observed decline in hedge fund alpha and rise in beta. This hypothesis posits that as more capital flows into hedge funds, their systematic risk (beta) increases due to the trading decisions of investors who also hold traditional assets. During times of market stress, these investors might liquidate both types of assets, leading to a higher correlation between traditional and alternative investments. The other options represent different hypotheses: the ‘fund bubble hypothesis’ attributes the decline to an influx of less-skilled managers, while the ‘capacity constraint hypothesis’ suggests that alpha is a zero-sum game and diminishing due to increased assets under management. The concept of ‘managerial skill decay’ is not presented as one of the primary hypotheses in the provided text.

Rho measures the sensitivity of a convertible bond’s value to changes in interest rates. A positive Rho indicates that the convertible’s value will increase as interest rates rise, and a negative Rho indicates that its value will decrease as interest rates rise. This sensitivity is particularly pronounced when the convertible is trading closer to its straight bond value (i.e., when the conversion option is out-of-the-money or at-the-money). As interest rates rise, the present value of the fixed coupon payments decreases, impacting the bond’s value. For convertibles, Rho tends to increase as the bond moves further from parity, meaning it’s valued more like a traditional bond. Therefore, an arbitrageur would need to monitor and potentially adjust their position based on interest rate movements, especially when the convertible’s value is heavily influenced by its fixed-income characteristics.

Convertible arbitrage strategies, particularly those involving delta hedging, are sensitive to the frequency of rebalancing. Continuous delta hedging, while theoretically ideal, is impractical due to transaction costs and the discrete nature of price movements and trading. In practice, arbitrageurs rehedge at discrete intervals, either based on time (e.g., daily) or price movements (e.g., every $1 move). The choice of rehedging frequency directly impacts the risk of the position being poorly hedged. A smaller rehedging interval generally reduces the risk of significant deviations from a delta-neutral position, but increases transaction costs. Conversely, a larger interval reduces transaction costs but increases the risk of adverse price movements causing substantial unhedged exposure. Therefore, the optimal rehedging strategy involves a trade-off between managing hedging risk and minimizing transaction costs, with the ideal frequency varying based on market conditions and the specific characteristics of the convertible security.

The question tests the understanding of how futures contracts differ from direct investments in underlying assets, specifically regarding currency risk. While direct investment in foreign equities exposes an investor to both the equity performance and the currency fluctuations of that foreign market, a futures contract on a foreign asset, such as a European equity index, does not carry this direct currency exposure. The value of the futures contract itself is denominated in the contract’s currency, and the investor’s obligation is to settle based on that value. The only currency risk arises from the margin posted and any unrealized profits or losses that haven’t been converted. Therefore, a long position in a European equity index futures contract has minimal exposure to changes in the price of the euro compared to holding the actual European equities.

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.

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 provided illustrates this by regressing the benchmark’s excess returns against the excess returns of various factors, with the coefficients representing the fund’s exposure to each factor. The remaining unexplained return is the residual error. Therefore, the core of this approach is identifying and utilizing these underlying risk drivers.

The question tests the understanding of how equally risk-weighted allocations are constructed. This method involves weighting strategies inversely proportional to their historical standard deviations. The provided exhibit shows that the HFRX Merger Arbitrage Index had a significantly higher allocation (21.65%) in the equally risk-weighted portfolio compared to the equally weighted portfolio (12.50%). This is directly attributable to its lower historical standard deviation (as indicated by the lower variance of 21.65% in column 2 compared to other strategies). Conversely, the HFRX Convertible Arbitrage Index received a lower weight (6.69%) due to its higher historical standard deviation, particularly impacted by significant losses in late 2008. The other options are incorrect because they either misrepresent the relationship between volatility and weighting or cite allocations from different weighting methodologies (e.g., equally weighted or mean-variance unconstrained).