Section 3.1.4
The Singer–Terhaar approach for determining the expected return on an asset class
involves determining the risk premium arising from systematic risk as a weighted
average of the risk premiums arising from a fully integrated market and fully segmented
market, where the weights for the fully integrated market are the degree of integration
of the markets.
The risk premium for the fully integrated market is given by
where is the Sharpe ratio for the
world market portfolio.
· The risk premium for the fully segmented market is given by
· In addition, if there are market imperfections, such as illiquidity premiums, they
must be added in.
· Finally, the expected return on the asset class is determined by adding these risk
premiums to the risk-free rate, in classical capital asset pricing model fashion.
Step
1: Systematic risk premium in fully integrated market
(23% × 0.85 ×
Risk
0.31) =
premium:
6.06%
2: Systematic risk premium in fully segmented market
(23% × 0.31)
= 7.13%
3: Weight systematic risk premiums by degree of integration:
(0.65 × 6.06 + 0.35 × 7.13) = 6.43%
6.43% +
0.60% =
4: Add the illiquidity premium
7.03%
5: Add the risk-free rate 2.5% + 7.03%
= 9.53%
6.) Among the three countries examined by the investment team, which is in the most
attractive phase of the business cycle for equity returns?
A. Hungary
B. Ireland
C. Spain
Answer = A
“Capital Market Expectations,” John P. Calverley, Alan M. Meder, CFA, Brian D. Singer,
CFA, and Renato Staub
Sections 4.1.2, 4.6.2, 4.6.6
The most favorable phases when considering equity returns are initial recovery and
early upswing whereas the late upswing, slowdown, and recession phases carry
the greater risk for equities.
Hungary has the combination of factors consistent with the initial recovery/early
upswing phases of the business cycle – increasing production, low inflation, improving
confidence, stimulatory fiscal/monetary policies, and abundant capacity. These
indicators point to strongly rising stock prices and therefore most attractive for equity
returns.
Rogers
Ted Rogers is the director of a research team that analyzes traditional and non-traditional
sources of energy for investment purposes. For traditional energy sources, a number of high-
frequency historical data series are available. For non-traditional energy sources, the data are
generally quarterly and tend to hide a great deal of the volatility that Rogers knows to exist
because appraised values are used instead of market values. To supplement the quarterly data,
Rogers's team uses an index of the top 30 firms in new and experimental technologies, called
the "NEXT Index." Although not all of the firms in the NEXT are energy firms, the index is
available as a weekly series. However, the NEXT does change its composite mix of firms
frequently as firms in the index fail or are sold to larger firms that are not in the index.
To determine the correlation matrix within the different energy sectors, Rogers's team relies on
a weighted average of correlations derived from multifactor models and historical correlations.
Although the combined experience within the team favors emphasizing the correlations derived
from the multifactor models, historical correlations are given a greater weight within the
weighted average calculations to reduce the future expected performance estimates of different
investment models being considered. This practice of purposefully understating the expected
future performance of these investment models is viewed as a safety measure by the team and
as a way to manage client expectations.
In a recent meeting, the team discussed how using the last two years of historical data for oil-
related industries generated relationships between factors that had not existed in the past. One
member of the team, Steve Phillips, stated: "The relationships reflect the fact that hurricane
activity in the last two years has affected oil concerns worldwide. There is no reason to believe
that such relationships will continue in the future."
Most of the team agreed with Phillips but conceded that a number of clients specifically
requested an analysis of the previous two years of data with an expectation that new trends
were emerging within the industry. The team decided to add more variables to the analysis in
order to show that the relationships the team believed to be significant actually outweighed the
importance of these recently found relationships. After adding several additional variables, the
team found that the model did not improve in predictive ability, but the recently found
relationships were indeed no longer significant.
1.) The quarterly data available for non-traditional energy sources are best described as
data with a:
A. time-period bias.
B. smoothing bias.
C. survivorship bias.
Answer = B
"Capital Market Expectations," John P. Calverley, Alan M. Meder, Brian D. Singer, and
Renato Staub
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