u/Appropriate-Ad5679

I have built an hybrid GARCH + DDPM to estimate VaR the model uses GARCH volatility to standardise residuals and a MLP- based nnet with FiLM layers for market feature conditioning to generate distribution

After training the model is passing christoffersen and kupeic test but the daq test is failing the generated distributions are too large until i use a quantile map to transform these distributions to reasonable values can somebody recommend what changes i should make here