Comments on
“When Does Idiosyncratic Risk Really Matter”
Yuanchen Chang
National Chengchi University
1
Summary of this paper


Propose a simple noise reduction method
that includes two different noisy measures
(value weighted and equal weighted indices)
in the same predictive regression to
reexamine the relation between idiosyncratic
risk and the expected future market return.
Results show that the noise effect tends to
cancel out and produce predictive power.
2
Contribution of this paper

Resolving the empirical debate on the
pricing role of idiosyncratic risk and unify
the time-series evidence with the crosssectional evidence.
3
Some observations

1. The claim that a stock’s
proportional investor base is equal to
its market value weight may not be
true.
• Control for institutional holdings.
4
Some observations

2. Alternative proxies for
idiosyncratic risk:
•
•

Mutual fund or hedge fund volatility.
VIX
3. Alternative weighting scheme for
market weight.
•
Negatively related to market weight(Arnold and
Hsu, 2005).
5
Some observations

4. How to handle variables with
multicollinearity concerns?
• Form a principal component by grouping
•
together the two variables into a
composite index.
Use a joint F test on the dual predictors to
see if they are not significantly different
from zero.
6
Some more observations

5. Why stop at dual predictors?
• Trio might be better.
7
Final comment

I enjoy reading this paper and
I think it has potentials to make a
contribution to the current literature.
8