How can a CFA model estimated with SEM yield an SRMR>1?

I just estimated a simple CFA model with SEM, and obtained an SRMR (standardized root mean squared residual) of 2.212 . Unless I am mistaken, it should have a maximum value of 1.0. How is this possible?

The data is on a sample of 742 observations, and items are scored on a 10 pt scale

Command:
sem ( pih1-pih12 <- PIH), method(ADF)

The factor loadings look reasonable (no Heywood cases)

The model clearly does not fit well (not unexpected), and here are the fit indices:

-> estat gof, stats(all)

----------------------------------------------------------------------------
Fit statistic | Value Description
---------------------+------------------------------------------------------
Discrepancy |
chi2_ms(54) | 319.043 model vs. saturated
p > chi2 | 0.000
chi2_bs(66) | 748.898 baseline vs. saturated
p > chi2 | 0.000
---------------------+------------------------------------------------------
Population error |
RMSEA | 0.081 Root mean squared error of approximation
90% CI, lower bound | 0.073
upper bound | 0.090
pclose | 0.000 Probability RMSEA <= 0.05
---------------------+------------------------------------------------------
Baseline comparison |
CFI | 0.612 Comparative fit index
TLI | 0.526 Tucker-Lewis index
---------------------+------------------------------------------------------
Size of residuals |
SRMR | 2.212 Standardized root mean squared residual
CD | 0.926 Coefficient of determination
----------------------------------------------------------------------------

Of note,many of the standardized residuals could not be calculated, and thus are missing.