| Static principal component analysis weights | Extracts principal components to find fixed weights, which remain constant over time | @oet2015, @huotari2015, @evgenidis2017, @hakkio2009, @illing2006, @mundra2021, @nazlioglu2015, @rooj2025 |
| Dynamic principal component analysis weights | Uses rolling windows to estimate variable weights, as indicators change over time | @oet2015, @monin2019, @bonato2024 |
| Equal market weighting | Assigns equal weights to all indicators | @oet2015 |
| Variance-equal weighting | Standardizes indicators by their variance, then apply equal weighting | @huotari2015, @balakrishnan2009, @cardarelli2011, @illing2006, @mundra2021, @neves2022, @rooj2025 |
| Portfolio theoretic weighting | Weight indicators based on their correlation matrix, treating stress like portfolio risk | @hollo2012, @oet2015, @huotari2015, @duprey2017, @fava2024, @mundra2021 |
| Credit aggregate-weighting | Weights indicators based on importance of their underlying credit markets | @illing2006 |
| Dynamic credit weighting | Similar to credit aggregate-weighting, but adjusts weights over time | @oet2015 |
| Sample cumulative distribution function transformation | Transforms each indicator to its percentile rank in the historical distribution before aggregation | @illing2006 |
| Matrix association indexing | Measures the degree of co-movement across all indicators using association matrices | @chavleishvili2023, @chavleishvili2025, @kremer2021 |
| Exponentially weighted moving average | Applies exponentially declining weights to historical observations, giving more value to recent signals | @mundra2021 |
| Dynamic conditional correlation | Models time-varying correlations, capturing how relationships strengthen during stress | @mundra2021 |
