Table III. Comparison of selected...

Table III.

Comparison of selected responses for Brazil vs Europe

Observations	Brazil 78 (%)	Europe 229 (%)	Δ (%)	p-value	Sig
In portfolio optimization, are objectives set for absolute risk?
Yes, variance, volatility or standard deviation	21.8	45.9	−24.1	0.000	***
Yes, VaR or CVaR	2.6	50.7	−48.1	0.000	***
Yes, semivariance or LPM	5.1	23.1	−18.0	0.000	***
In portfolio optimization, are objectives set for relative risk?
No	51.3	33.6	17.7	0.007	***
Yes, tracking error	34.6	49.8	−15.2	0.025	**
Yes, tracking error VaR or BVaR	19.2	18.8	0.5	0.930
Yes, semivariance or LPM	11.5	12.7	−1.1	0.794
Yes, other	0.0	2.6	−2.6	0.149
How is the covariance matrix estimated?
Sample covariance or RiskMetrics	53.8	59.8	−6.0	0.355
Explicit factors, such as CAPM	19.2	29.3	−10.0	0.084
Factor analysis or principal component analysis	3.8	12.7	−8.8	0.028	**
Shrinkage methods	1.3	3.9	−2.6	0.255
GARCH and others	17.9	7.9	10.1	0.012	**
How is VaR estimated?
Normal distribution	62.8	41.0	21.8	0.001	***
Higher moments	7.7	16.6	−8.9	0.053	*
Extreme value theory	5.1	8.3	−3.2	0.359
CVaR or expected shortfall	29.5	22.3	7.2	0.198
Other	15.4	12.7	2.7	0.542
What methods are used to address estimation risk?
Maximum concentration limit per asset	64.1	67.7	−3.6	0.562
Global minimum variance portfolio	5.1	17.0	−11.9	0.009	***
Bayesian methods	7.7	15.3	−7.6	0.089
Resampling	9.0	13.5	−4.6	0.291
Other	9.0	6.1	2.9	0.387
How is performance evaluated?
Sharpe ratio	78.2	77.3	0.9	0.868
Treynor ratio	7.7	10.9	−3.2	0.414
Sortino ratio	14.1	27.5	−13.4	0.017	**
Absolute return	65.4	41.5	23.9	0.000	***
Modigliani and Modigliani	5.1	3.1	2.1	0.395
Jensen’s alpha	15.4	34.1	−18.7	0.002	***
Information ratio	19.2	65.1	−45.8	0.000	***
Excess return relative to benchmark	75.6	31.4	44.2	0.000	***
How is alpha measured?
Multifactor models	9.0	21.4	−12.4	0.014	**
Single-factor models (such as CAPM)	17.9	26.6	−8.7	0.123
Performance attribution or style analysis	69.2	35.4	33.9	0.000	***
Peer group analysis	66.7	56.8	9.9	0.124
Other	5.1	2.2	2.9	0.183

Observations	Brazil 78 (%)	Europe 229 (%)	Δ (%)	p-value	Sig
In portfolio optimization, are objectives set for absolute risk?
Yes, variance, volatility or standard deviation	21.8	45.9	−24.1	0.000	***
Yes, VaR or CVaR	2.6	50.7	−48.1	0.000	***
Yes, semivariance or LPM	5.1	23.1	−18.0	0.000	***
In portfolio optimization, are objectives set for relative risk?
No	51.3	33.6	17.7	0.007	***
Yes, tracking error	34.6	49.8	−15.2	0.025	**
Yes, tracking error VaR or BVaR	19.2	18.8	0.5	0.930
Yes, semivariance or LPM	11.5	12.7	−1.1	0.794
Yes, other	0.0	2.6	−2.6	0.149
How is the covariance matrix estimated?
Sample covariance or RiskMetrics	53.8	59.8	−6.0	0.355
Explicit factors, such as CAPM	19.2	29.3	−10.0	0.084
Factor analysis or principal component analysis	3.8	12.7	−8.8	0.028	**
Shrinkage methods	1.3	3.9	−2.6	0.255
GARCH and others	17.9	7.9	10.1	0.012	**
How is VaR estimated?
Normal distribution	62.8	41.0	21.8	0.001	***
Higher moments	7.7	16.6	−8.9	0.053	*
Extreme value theory	5.1	8.3	−3.2	0.359
CVaR or expected shortfall	29.5	22.3	7.2	0.198
Other	15.4	12.7	2.7	0.542
What methods are used to address estimation risk?
Maximum concentration limit per asset	64.1	67.7	−3.6	0.562
Global minimum variance portfolio	5.1	17.0	−11.9	0.009	***
Bayesian methods	7.7	15.3	−7.6	0.089
Resampling	9.0	13.5	−4.6	0.291
Other	9.0	6.1	2.9	0.387
How is performance evaluated?
Sharpe ratio	78.2	77.3	0.9	0.868
Treynor ratio	7.7	10.9	−3.2	0.414
Sortino ratio	14.1	27.5	−13.4	0.017	**
Absolute return	65.4	41.5	23.9	0.000	***
Modigliani and Modigliani	5.1	3.1	2.1	0.395
Jensen’s alpha	15.4	34.1	−18.7	0.002	***
Information ratio	19.2	65.1	−45.8	0.000	***
Excess return relative to benchmark	75.6	31.4	44.2	0.000	***
How is alpha measured?
Multifactor models	9.0	21.4	−12.4	0.014	**
Single-factor models (such as CAPM)	17.9	26.6	−8.7	0.123
Performance attribution or style analysis	69.2	35.4	33.9	0.000	***
Peer group analysis	66.7	56.8	9.9	0.124
Other	5.1	2.2	2.9	0.183

Notes:

The p-values provided are from Pearson’s chi-square test with one degree of freedom. The null hypothesis is the equality of proportions, and the alternative hypothesis is inequality. We reject the null hypothesis at p < 0.01 (

***

); p < 0.05 (

) and p < 0.1 (

)

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