Check rounding and overflow handling mode
By default, the Data Type Conversion block rounds to Floor and Wraps overflows.
In contrast, by default, fi rounds to Nearest and Saturates overflows.
You'll need to move away from default behavior of one or both to get full input-output agreement for all possible input values.
Parameter Underflowing to Zero Could have Huge Impact
The data type fixdt(1,21,-1) that you've set for a signal seems to also get used for some parameter associated with the signal. Since the spacing between representable values is Slope = 2^1 = 2, the representable values are
-2097152, -2097150, ... -2, 0, 2, 4, ... 2097148, 2097150
so -1 is not representable and gets rounded to representable value 0.
Depending on how that parameter is used the impact of the quantization could be minor or huge.
For example, if the parameter is used in multiplication, then multiplying by the ideal value -1 will be very different than multiplying by the quantized value 0.
Figure out how the parameter is used in your model to determine the expected impact of quantization.
See if you can separate the data type used for the parameter from that used for the signal. For example, suppose the -1 was the parameter value of a gain block. If the data type of the gain parameter was configured to be Same As Input, then it could be changed to Inherit via Internal Rule or fixdt(1,2,0) to accurately represent -1.
