---
title: Iterated Combination Method for Risk Measure Forecasts
url: https://www.ml-quant.com/papers/ssrn/5059002/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5059002
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5059002
featured: 2024-12-18
citations: 0
topic: Econometrics & Forecasting
---


# Iterated Combination Method for Risk Measure Forecasts

The study introduces an innovative technique, iterative combination, for forecasting Value-at-Risk and Expected Shortfall, proving it to be more effective than traditional methods.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5059002
- Identifier: SSRN 5059002
- Released: 2024-12-16
- First featured: Quant Letter No. 79 (2024-12-18): https://www.ml-quant.com/issues/2024-12-18/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [Using quantile time series and historical simulation to forecast financial risk multiple steps ahead](https://www.ml-quant.com/papers/arxiv/2502.20978/): A new method for estimating Value-at-Risk (VaR) and Expected Shortfall (ES) models allows for tail risk forecasting and can be applied to any data or model where the VaR and ES relationship remains constant.
- [Predicting Stock Market Crash with Bayesian Generalised Pareto Regression](https://www.ml-quant.com/papers/arxiv/2506.17549/): The study uses a Bayesian Generalised Pareto Regression model to predict extreme losses in India's Nifty 50 index, showing increased tail risk with higher market volatility and the model's effectiveness in forecasting tail risk in emerging markets.
- [Machine Learning and Modern Econometrics for Tail-Risk Protection](https://www.ml-quant.com/papers/ssrn/4540499/): The study introduces a dynamic tail risk protection strategy using machine learning, comparing various methods and suggesting an ensemble classifier for better performance.
- [Nonlinear Drivers of Macroeconomic Tail Risk: A Threshold Stochastic Volatility-in-Mean VAR with Regime-Dependent Leverage](https://www.ml-quant.com/papers/arxiv/2609.26994/): A regime-switching volatility-in-mean VAR reveals that the drivers of growth and inflation tails differ from median dynamics, with macroeconomic uncertainty playing a larger role in downside risk.
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.