CAIA Level 2 Quiz 70

Hicks’s theory, as discussed in the context of commodity markets, posits that producers, due to the technical rigidities in their production processes and the need to cover planned sales, have a stronger incentive to hedge their future output compared to consumers who have more flexibility in acquiring inputs. This leads to a relative weakness on the demand side of futures markets. Consequently, for speculators to be willing to absorb this excess supply of futures contracts (i.e., more sellers than buyers), they require compensation for taking on this additional risk. This compensation comes in the form of a discount on the futures price relative to the expected spot price. Therefore, the futures price is typically expected to be lower than the anticipated spot price, a phenomenon known as normal backwardation. The other options describe scenarios that are either contrary to this theory or represent different market dynamics.

The core objective of an endowment is to provide a perpetual stream of income while preserving the real value of the principal. This long-term perspective, often spanning centuries, necessitates an investment strategy that prioritizes capital preservation and sustainable growth over short-term gains. While generating income is crucial, it must be balanced with the need to outpace inflation and maintain purchasing power over an indefinite period. Therefore, the primary focus is on long-term capital appreciation and income generation that supports the institution’s mission without depleting the corpus.

The question tests the understanding of how diversification impacts risk and return profiles across different private equity asset classes, specifically focusing on the trade-off between downside protection and upside potential. The provided text highlights that while diversification generally reduces risk (as measured by standard deviation and semideviation) and improves risk-adjusted metrics like the Sortino ratio for all submarkets, it also tends to normalize the risk profile and limit the upside potential. However, U.S. Venture Capital (VC) portfolios are presented as an exception, where diversification, due to historically high average returns, can actually lead to improving risk profiles and potentially enhanced upside. Therefore, the statement that diversification limits upside potential for all submarkets is incorrect due to the specific case of U.S. VC.

The core of unsmoothing appraisal-based returns lies in estimating the true, underlying value from a series of smoothed reported values. Equation 16.4, derived from the smoothing model, provides a direct method for this. It states that the true price at time t (P_true_t) can be estimated by taking the previously reported price (P_reported_{t-1}) and adding an adjustment. This adjustment is a multiple (1/\theta) of the most recent change in the reported price (P_reported_t – P_reported_{t-1}). The parameter \theta, which ranges from 0 to 1, dictates the speed of the decay function; a smaller \theta means more smoothing and a larger multiplier (1/\theta) is needed to ‘unsmooth’ the price. Therefore, to estimate the true price, one must use the most recent reported price and the prior reported price, along with the estimated smoothing parameter.

Capital at Risk (CaR) in managed futures represents the maximum potential loss if all positions in a portfolio simultaneously hit their predetermined stop-loss levels within a single trading period. The provided exhibit calculates CaR by taking a 1% adverse price move for each individual futures contract and summing these potential losses. The total notional value of the positions is $1,657,050. A 1% adverse move across all these positions would result in a total loss of $16,571 ($1,657,050 * 0.01). When expressed as a percentage of the account value ($1,000,000), this represents a Capital at Risk of 1.657% (-$16,571 / $1,000,000). This metric is useful for understanding the downside risk based on the manager’s stop-loss strategy, but it has limitations, such as not accounting for potential offsets from long and short positions or the possibility of price gaps exceeding stop-loss levels, especially in less liquid markets.

Convertible arbitrage strategies, particularly those involving delta hedging, aim to profit from the difference between realized and implied volatility. When realized volatility exceeds implied volatility, the gamma trading (buying low and selling high as the stock price moves) generates profits that outpace the cost of time decay (theta) of the embedded option. This scenario leads to a net positive return for the strategy, exceeding the risk-free rate. Conversely, if implied volatility is higher than realized volatility, the time decay erodes gains, potentially leading to underperformance relative to a risk-free investment.

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 estimated true price is the previous reported price adjusted by the most recent reported price change, scaled by the inverse of the decay parameter (1/\alpha). This scaling factor amplifies the reported price change to reflect a potentially larger underlying true price change. Therefore, to estimate the true price, one needs to know the previous reported price, the current reported price, and the decay parameter \alpha.

Convertible arbitrage involves hedging a long position in a convertible bond with a short position in the underlying stock. The goal is to profit from the difference between the bond’s price and the value of its embedded options and the hedged stock position. The delta of the convertible bond, which represents its sensitivity to changes in the underlying stock price, is crucial for determining the appropriate hedge ratio. The delta of a convertible bond is not simply the delta of the straight bond plus the delta of the embedded call option. Instead, it’s a more complex calculation that accounts for the interaction between the bond and the option, and how changes in stock price affect the likelihood of conversion. The provided Black-Scholes Greeks are for a standard European call option, not a convertible bond. The delta of a convertible bond is influenced by factors like interest rates, dividend yields, time to maturity, volatility, and the conversion premium. A common approach to hedging convertible bonds is to dynamically adjust the short position in the underlying stock as the bond’s delta changes. This dynamic hedging aims to maintain a delta-neutral position, thereby reducing exposure to stock price movements. The question tests the understanding that the delta of a convertible bond is not a simple sum of its components and requires a more sophisticated calculation that considers the interplay of various factors, making the direct application of standard Black-Scholes Greeks for a plain vanilla option insufficient for precise hedging.

The core principle of robust risk management in commodity trading, as highlighted in the provided text, emphasizes the critical need for segregation of duties to ensure objectivity and control. Specifically, the reconciliation of trades with primary brokers and OTC confirmations, along with the recording and confirmation of trades, should be handled by different individuals. This separation prevents a single point of failure and acts as a crucial gatekeeping mechanism. Microsoft Excel, while useful for data analysis, lacks the necessary audit trails, auditable reporting capabilities, and the inherent controls required for a dedicated risk management system, making it inadequate for comprehensive risk oversight and accurate reporting of risk and net asset values to stakeholders.

The core challenge in applying Modern Portfolio Theory (MPT) to private equity lies in the inherent difficulties of accurately estimating risk and correlation for this asset class. Private equity valuations are often infrequent and subject to biases, which can artificially suppress measured volatility and correlation with public markets. Standard performance measures like IRR, which are capital-weighted, differ from the time-weighted measures used for public markets, further complicating direct correlation analysis. While MPT suggests that adding non-correlated assets can improve a portfolio’s risk-return profile, the practical estimation of these parameters for private equity is problematic, making it difficult to precisely determine optimal allocations using traditional MPT optimization models.

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

The scenario describes a common issue in Defined Contribution (DC) plans where participants fail to rebalance their portfolios as they approach retirement. The example illustrates how an initial allocation of 70% equity and 30% fixed income, if equity returns significantly outperform fixed income over two decades, can drift to an 85% equity/15% fixed income allocation. This is problematic because a portfolio heavily weighted towards equities becomes too risky for someone nearing retirement, who typically requires a more conservative asset allocation to preserve capital and ensure income stability. Target-date funds are designed to address this by automatically adjusting the asset allocation over time, becoming more conservative as the target retirement date approaches, thereby mitigating the risk of a drifting allocation.

The binomial model is preferred for pricing convertible bonds because it can accommodate various contractual features, such as call and put provisions, which are not easily handled by the Black-Scholes model. The Black-Scholes model is designed for European options and does not inherently account for early exercise or other embedded features that are common in convertible bonds. The binomial approach, by modeling discrete price movements, allows for the valuation of these embedded options and the bond’s features at each node of the tree, providing a more accurate valuation. While the component approach is conceptually useful, its reliance on Black-Scholes limits its applicability to more complex convertible bond structures.

The question probes the understanding of how valuation smoothing in private equity, particularly buyouts, can distort reported beta values. Exhibit 13.5 indicates a very low beta (0.06) for buyouts compared to equities (1.00). The provided text explicitly states that this low beta for buyouts is ‘most likely can be explained by the valuation smoothing observed in private equity.’ This smoothing process artificially reduces the observed volatility, leading to a lower calculated beta. Therefore, the most accurate interpretation is that the reported low beta for buyouts is a consequence of this smoothing effect, rather than an indication of genuinely lower systematic risk or a superior hedging strategy.

The core issue with appraisal-based returns, like those from the NCREIF NPI, is that they 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 $R_{t,true} = (R_{t,reported} – \rho R_{t-1,reported}) / (1 – \rho)$, aims to reveal the underlying, more volatile true returns. A high positive autocorrelation coefficient (rho) indicates that past returns are highly predictive of current returns, which is characteristic of smoothed data. When this smoothing effect is removed, the true volatility (standard deviation) of the series is expected to increase significantly, as demonstrated by the NCREIF NPI’s standard deviation jumping from 4.01% to 13.38% after unsmoothing. Therefore, relying on smoothed return data for asset allocation, particularly in mean-variance optimization, would lead to an overestimation of the attractiveness of assets with smoothed returns because their perceived risk is understated.

In a multistrategy fund, the independence of the risk management function is paramount to ensure objective oversight. A risk manager whose compensation is directly tied to portfolio performance, or who reports to a portfolio manager, may face conflicts of interest. This could lead to a reluctance to flag or enforce risk limits, especially if doing so might negatively impact short-term performance. Therefore, an independent risk manager, reporting to senior management and compensated independently of specific strategy performance, is crucial for maintaining the integrity of risk controls and protecting the overall fund from excessive risk-taking.

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. Since the production of feed grains requires significantly more land per calorie than direct human consumption of vegetables, this increased demand for feed grains directly translates into greater pressure for agricultural land expansion. Therefore, the most significant driver of increased demand for agricultural land, stemming from rising global incomes, is the shift towards meat-rich diets.

This question tests the understanding of how non-zero-sum dynamics in futures markets can contribute to returns for trend-following strategies. The core argument presented is that participants with offsetting positions in spot markets may be willing to incur losses in futures to achieve a net gain. This willingness to accept futures losses, driven by broader portfolio considerations, creates an imbalance that trend-following managers can exploit. The other options represent less direct or incorrect explanations for the sources of return in this context. Option B misinterprets the role of behavioral finance by suggesting it *causes* trends rather than explaining *why* they might exist and be exploitable. Option C incorrectly attributes the primary source of return to the efficiency of the market, which is contrary to the premise of exploiting trends. Option D oversimplifies the concept by focusing solely on technical rules without acknowledging the underlying market structure and participant behavior.

The question tests the understanding of how a crack spread hedge functions to lock in a refiner’s margin. In Scenario A, the cash market price for crude oil increased from $89.58 to $90.06 per barrel, resulting in a loss on the long crude oil position in the cash market. Simultaneously, the cash market prices for gasoline and heating oil decreased. The hedge was established by shorting gasoline and heating oil futures and going long crude oil futures. When cash prices rise, the long crude oil position in futures gains value, and when product prices fall, the short product positions in futures gain value. In Scenario A, the cash market experienced a rise in crude oil costs and a fall in product prices. The refiner bought crude oil at $90.06 (a loss of $0.48/bbl compared to the futures price of $88.68) and sold gasoline at $2.3492/gallon ($98.66/bbl) and heating oil at $2.4818/gallon ($104.24/bbl). The futures market saw a gain on the crude oil futures (from $88.68 to $90.06, a gain of $1.38/bbl) and losses on the product futures (gasoline from $110.08 to $99.16, a loss of $10.92/bbl; heating oil from $111.54 to $104.54, a loss of $7.00/bbl). The net effect of the futures positions is a gain that offsets the loss in the cash market, thereby locking in the refiner’s margin. Specifically, the futures gain on crude is $1.38/bbl. The losses on the product futures are $10.92/bbl for gasoline and $7.00/bbl for heating oil. For a 3:2:1 spread, the net futures gain is calculated as: (Gain on Crude Futures) – (Loss on Gasoline Futures) – (Loss on Heating Oil Futures). However, the question asks about the *net impact* of the futures positions on the refiner’s overall profitability, considering the cash market outcome. The refiner’s cash market margin decreased due to higher crude costs and lower product prices. The futures positions are designed to offset this. The long crude futures gained value as crude prices rose. The short gasoline and heating oil futures lost value as product prices fell. The question asks for the net outcome of the *futures positions* in this scenario. The futures gain on crude oil is $90.06 – $88.68 = $1.38 per barrel. The futures loss on gasoline is $110.08 – $99.16 = $10.92 per barrel. The futures loss on heating oil is $111.54 – $104.54 = $7.00 per barrel. The net impact of the futures positions, considering the 3:2:1 ratio (60 contracts crude, 40 gasoline, 20 heating oil), is: (60 * $1.38) – (40 * $10.92) – (20 * $7.00) = $82.80 – $436.80 – $140.00 = -$494.00. This calculation is per 1000 barrels per contract. To get the per barrel impact, we need to consider the total crude barrels (60,000) and the spread ratio. The futures crack spread calculation provided in the text is: [(40 ×$110.08) +(20 ×$111.54) −(60 ×$88.68)]/60 =$21.88/bbl. This represents the initial locked-in margin. In Scenario A, the cash market outcome for the refiner is: Crude cost: $90.06/bbl. Revenue from gasoline: (40,000 bbl * $98.66/bbl) = $3,946,400. Revenue from heating oil: (20,000 bbl * $104.24/bbl) = $2,084,800. Total revenue: $6,031,200. Total crude cost: 60,000 bbl * $90.06/bbl = $5,403,600. Cash margin: ($6,031,200 – $5,403,600) / 60,000 bbl = $627,600 / 60,000 bbl = $10.46/bbl. The futures market outcome for the refiner: Gain on crude futures: (60 contracts * 1000 bbl/contract) * ($90.06 – $88.68) = 60,000 bbl * $1.38/bbl = $82,800. Loss on gasoline futures: (40 contracts * 1000 bbl/contract) * ($110.08 – $99.16) = 40,000 bbl * $10.92/bbl = $436,800. Loss on heating oil futures: (20 contracts * 1000 bbl/contract) * ($111.54 – $104.54) = 20,000 bbl * $7.00/bbl = $140,000. Net futures gain/loss: $82,800 – $436,800 – $140,000 = -$494,000. The question asks for the net impact of the futures positions. The futures positions are designed to offset the cash market movements. The cash market margin decreased to $10.46/bbl. The initial locked-in margin was $21.56/bbl (cash) and $21.88/bbl (futures). The net profit from the combined cash and futures positions should approximate the initial locked-in margin. The net gain/loss from the futures positions is -$494,000. This loss in the futures market is meant to compensate for the adverse movement in the cash market. The cash market margin fell by $21.56 – $10.46 = $11.10/bbl. The futures positions should provide a gain of approximately this amount. Let’s re-examine the futures calculation. The futures crack spread is calculated as: [(40 * $99.16) + (20 * $104.54) – (60 * $90.06)] / 60 = ($3966.40 + $2090.80 – $5403.60) / 60 = $657.60 / 60 = $10.96/bbl. This is the value of the futures position at expiration. The initial futures crack spread was $21.88/bbl. The change in the futures value is $10.96 – $21.88 = -$10.92/bbl. This represents the loss on the futures positions. This loss of $10.92/bbl in the futures market offsets the decrease in the cash market margin. The cash market margin decreased from $21.56 to $10.46, a drop of $11.10/bbl. The futures loss of $10.92/bbl largely offsets this. The question asks for the net impact of the futures positions. The futures positions resulted in a net loss of $10.92 per barrel. This loss is the outcome of the futures contracts’ performance relative to their initial prices. The refiner went long crude futures at $88.68 and closed them at $90.06, resulting in a gain of $1.38/bbl. The refiner shorted gasoline futures at $110.08 and closed them at $99.16, resulting in a gain of $10.92/bbl. The refiner shorted heating oil futures at $111.54 and closed them at $104.54, resulting in a gain of $7.00/bbl. The net gain on the futures positions is: (1 * $1.38) – (2 * $10.92) – (1 * $7.00) = $1.38 – $21.84 – $7.00 = -$27.46. This calculation is based on the 3:2:1 ratio. The net gain/loss per barrel of crude oil is: (1 * $1.38) – (2/3 * $10.92) – (1/3 * $7.00) = $1.38 – $7.28 – $2.33 = -$8.23. Let’s use the contract values. The futures crack spread at expiration is calculated as: [(40 contracts * $99.16/bbl) + (20 contracts * $104.54/bbl) – (60 contracts * $90.06/bbl)] / 60 contracts = ($3966.40 + $2090.80 – $5403.60) / 60 = $657.60 / 60 = $10.96/bbl. The initial futures crack spread was $21.88/bbl. The change in the futures value is $10.96 – $21.88 = -$10.92/bbl. This represents the loss incurred on the futures positions. This loss offsets the decline in the cash market margin. The question asks for the net impact of the futures positions. The futures positions resulted in a net loss of $10.92 per barrel.

Exhibit 28.6 demonstrates how different correlation assumptions between NYMEX heating oil and NYMEX unleaded gasoline can significantly impact a portfolio’s net asset value (NAV). A correlation of -1 implies that the prices of these two commodities move in perfectly opposite directions. In the provided scenario, a $3.00 price change in heating oil (positive for the portfolio) is paired with an equivalent price change in unleaded gasoline (negative for the portfolio). The calculation for the change in NAV under a -1 correlation is as follows: (11,813 bbl. * $3.00/bbl.) + (-11,882 bbl. * -$3.00/bbl.) = $35,439 + $35,646 = $71,085. The exhibit shows a change in NAV of -$71,085, which aligns with the calculation if the price changes were applied in the opposite direction to the portfolio’s holdings as presented in the table. The question asks for the impact on NAV given a -1 correlation and a $3.00 price change for heating oil. The calculation for the impact on NAV is: (Position in Heating Oil * Price Change in Heating Oil) + (Position in Unleaded Gasoline * Price Change in Unleaded Gasoline). From Exhibit 28.5, the position in heating oil is 11,813 bbl. and in unleaded gasoline is -11,882 bbl. If heating oil increases by $3.00 and unleaded gasoline decreases by $3.00 (due to -1 correlation), the change in NAV would be (11,813 * $3.00) + (-11,882 * -$3.00) = $35,439 + $35,646 = $71,085. However, the exhibit shows a negative change in NAV. This implies that the positions in the exhibit are structured such that a positive price movement in heating oil and a negative price movement in unleaded gasoline would lead to a loss. Let’s re-examine Exhibit 28.6. It shows a $3.00 price change for heating oil and a corresponding price change for unleaded gasoline. The ‘Correlation -1’ column shows a change in NAV of -$71,085. This means that a $3.00 increase in heating oil and a $3.00 decrease in unleaded gasoline resulted in a $71,085 loss. This would occur if the portfolio was long heating oil and short unleaded gasoline, and the price movements were as described. The question asks for the impact on NAV when the correlation is -1 and heating oil experiences a $3.00 price increase. The exhibit directly provides this outcome as -$71,085.

The core difference highlighted in the provided text between venture capital (VC) and buyout strategies lies in their approach to risk and return. VC is characterized by a high-risk, high-reward model where a few significant successes must compensate for numerous failures, often in cutting-edge sectors with uncertain outcomes. Buyouts, conversely, focus on established industries, financial engineering, and corporate restructuring, aiming for more stable, albeit potentially lower, returns with a higher probability of success per investment. The question probes the fundamental driver of returns for each strategy, and the correct answer accurately reflects this distinction: VC relies on company and market building with the potential for substantial growth, while buyouts leverage financial engineering and operational efficiencies in mature businesses.

The question tests the understanding of the primary drivers of higher Internal Rates of Return (IRRs) for timber investments outside the United States, as presented in the provided text. The text explicitly states that shorter rotation periods are a key factor contributing to higher IRRs for species like Eucalyptus. While currency risk, market structure, and regulatory issues are mentioned as considerations for non-U.S. investments, they are presented as additional risks or factors influencing returns, not as the primary reasons for *higher* IRRs compared to U.S. species. The text highlights that Eucalyptus species tend to have the highest IRRs ‘in part because of the shorter periods to rotation.’ Therefore, shorter rotation periods are the most direct and significant explanation for the observed higher IRRs.

This question tests the understanding of operational due diligence for merger arbitrage funds, specifically focusing on the trade-off between risk and return when considering potential deals versus announced deals. Investing in announced deals is generally considered safer due to greater certainty, but offers lower expected returns. Conversely, taking positions based on potential merger activity, while riskier, offers the possibility of higher returns if successful. A thorough due diligence process would involve understanding the manager’s approach to this risk-return spectrum, including their concentration in specific sectors or cross-border deals if they engage in potential deal arbitrage.

The question tests the understanding of the historical evolution of commodity research in asset allocation. Early research, particularly in the 1970s, began to challenge the prevailing view of commodities as solely high-risk investments. Greer’s 1978 study is a landmark in this regard, demonstrating that a collateralized basket of commodity futures could offer lower risk and higher returns compared to equities. Bodie and Rosansky further supported these findings in 1980 by highlighting the diversification benefits of commodities when added to stock portfolios and their effectiveness as an inflation hedge. Fama and French’s 1988 work identified a business cycle component in industrial metal prices, adding another layer to understanding commodity behavior. The introduction of indices like the GSCI in 1991 and DJUBSI later facilitated broader commodity investment and research. Therefore, the period when academic studies began to highlight the positive role of commodities in institutional portfolios, moving beyond the perception of high risk, is the 1970s, with seminal work emerging in the latter half of that decade.

The core issue with appraisal-based real estate indices like NCREIF is the smoothing effect on returns, which artificially lowers observed volatility and autocorrelation. Unsmoothing aims to correct this by using the estimated autocorrelation coefficient (rho) to reconstruct a more accurate series of true returns. The formula Rt,true = (Rt,reported – rho * Rt-1,reported) / (1 – rho) is used for this purpose. A high positive autocorrelation (like the 83.1% for NCREIF) indicates that current reported returns are heavily influenced by past reported returns, masking the true underlying volatility. When this smoothing effect is removed, the standard deviation of the returns increases significantly, as demonstrated by the NCREIF NPI’s standard deviation rising from 4.01% to 13.38% after unsmoothing. This increased volatility is a more realistic reflection of the asset’s risk. Therefore, failing to unsmooth appraisal-based returns leads to an underestimation of risk, which can result in an over-allocation to such assets in a mean-variance optimization framework.

The core-satellite portfolio approach in private equity involves allocating capital to a ‘core’ portfolio of established, lower-risk funds and a ‘satellite’ portfolio of newer, higher-risk, or experimental funds. The satellite portfolio is designed to capture opportunities arising from market shifts or new strategies, acting as a form of real option. The decision to allocate more to the satellite portfolio (exploration) versus the core portfolio (exploitation) is influenced by several factors. A longer investment time horizon allows for greater exploration, as the potential for future upside from novel strategies is more valuable over extended periods. The availability of reserve capital also enables more exploration, providing a buffer for potential losses in newer ventures. Finally, anticipated market volatility or disruption suggests a greater need for diversification through exploration, as it spreads risk across a wider range of potential future market conditions. Conversely, a stable market environment favors a more concentrated core portfolio.

Winsorizing is a statistical technique used to mitigate the impact of extreme values (outliers) in a dataset. In the context of quantitative equity strategies, where data like price-to-earnings ratios are normalized using z-scoring, outliers can disproportionately influence the final ranking or score. By Winsorizing, extreme z-scores (e.g., above 3 or below -3) are capped at these threshold values. This process standardizes the data by limiting the influence of the most extreme observations, preventing them from skewing the overall results when combining multiple indicators. This is crucial for building robust quantitative models that are less sensitive to anomalous data points.

The NCREIF National Property Index (NPI) is a prime example of an appraisal-based real estate index. Appraisal-based indices rely on periodic professional valuations of properties rather than actual transaction prices. This approach is necessitated by the illiquid nature of real estate, where properties do not trade frequently enough for transaction-based calculations to be reliable, especially for short-term returns. The NPI specifically uses appraisals to estimate property values on a quarterly basis. While transaction-based indices exist and are valuable, they are not the primary methodology for the NPI. Indices based on hedonic pricing models or repeat-sales methodologies are types of transaction-based approaches, and while they have their own merits, they are not the defining characteristic of the NPI.

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, where the benchmark’s excess return is explained by the weighted sum of factor excess returns plus an error term. The weights (betas) are estimated using historical data. Therefore, the core of this approach is identifying and utilizing these underlying risk factors to mimic the benchmark’s behavior.

The scenario describes a common issue in Defined Contribution (DC) plans where participants fail to rebalance their portfolios as they approach retirement. The example illustrates how an initial allocation of 70% equity and 30% fixed income, if equity returns significantly outperform fixed income over two decades, can drift to an 85% equity/15% fixed income allocation. This is problematic because a portfolio heavily weighted towards equities becomes too risky for someone nearing retirement, who typically needs a more conservative allocation to preserve capital. Target-date funds are designed to address this by automatically adjusting the asset allocation over time, becoming more conservative as the target retirement date approaches. This automatic rebalancing and glide path management is the primary benefit they offer to participants who may not actively manage their investments.