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Comments on “When Does Idiosyncratic Risk Really Matter”

Yuanchen ChangNational Chengchi University

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Summary of this paperSummary 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.

Contribution of this paperContribution of this paper Resolving the empirical debate on the

pricing role of idiosyncratic risk and unify the time-series evidence with the cross-sectional evidence.

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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.

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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).

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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.

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Some more observations

5. Why stop at dual predictors?• Trio might be better.

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Final comment

I enjoy reading this paper and I think it has potentials to make a

contribution to the current literature.