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10-1 capital market expectations: framework and macro considerations (2…
10-1 capital market expectations:
framework and macro considerations
1 introduction
fundamental law fo investing: the uncertainty of the future
capital market expectations CME:
expectations concerning the risk and return prospects of asset classes
an essential input to formulating a strategic asset allocations
2 framework and challenges
a framework for developing
capital market expectations
disciplined
approach
1 specify the set of expectations needed including time horizon apply
2 research the historical record
3 specify the mehods and / or models used and infor requirements
4 determine the best sources for infor needs
5 interpret the current investment enviroment
6 provide the set of expectatiosn needed, documenting the conclusions
7 monitor actual outcomes and compare with expectiontations, providing feedback to improve the expectations-settign process
good forecasts
unbiased objective and well researched
efficient - min the size of forecast errors
internally consistent both cross-sectionally and intertemporally
challenges in forecasting
limitations of econmic data
the time lag
one or more official revisions to initial data values
definitions and calculation methods change too
periodically re-base
data measurement errors and biases
transcription errors
survivorship bias
appraisal (smoothed) data
→measured volatilities biased downward
and correlations with other assets tend to be understated
the limitatiosn of historical estimates
changes in regime → nonstationarity - different parts of a data series reflect different underlying statistical properties
→should use only that part fo the time series that appears relevant to the present
should use the longest data hisoory that reasonable assuance of stationarity
higher-frequency data - improve the precision of the sample variances, covariances and correlations
not improve the precision of the sample mean
many variables are considered - large num of observations may be statistical mecessity
as frequency increases, the likelihood increases that data may be asynchronous across variables
→distorts correlations and induces lead-lag relationships
normally distributed
→skewness and fat tails
ex post risk can be a biased mesasure of ex ante risk
peso problem: looking backward, likely to underestimate ex ante risk and overestimate ex ante anticipated returns
biases in analysts' methods
data-mining bias: such patterns cannot be expected to have predictive value
time-period bias: relates to results that are period specific
to avoid: able to provide an econimic rationale / to examine the forecasting relationship out of sample
the failure to account for conditioning infor
unconditional forecasts: dilute infor by averaging over environments, lead to misperception of prospective risk and return
misinterpretation of correlations
negligible measured correlation may reflect a strong but nonlinear relationship
psychological biases
anchoring bias
status quo bias
confirmation bias
overconfidence bias
prudence bias
abailability bias
model uncertainty: whether structurally and / or conceptually correct
parameter uncertainty:parameters invariably estimated with error
input uncertainty: whether the inputs are correct
asset allocation - the primary determinant fo long-run portfolio performance
to ensure internal consistency arcoss asset classes (cross-sectional consistency)
and over various time horizons (intertemporal consistency)
→poor risk-return characteristics over any horizon
→distort the connection between decisons and horizon