---
title: Tail-Risk Forecasting with General Cubic Distributions
url: https://www.ml-quant.com/papers/ssrn/7504480/
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 7504480
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7504480
featured: 2026-09-25
citations: unknown
topic: Derivatives & Volatility
---


# Tail-Risk Forecasting with General Cubic Distributions

A cubic quantile framework forecasts Value-at-Risk and Expected Shortfall more reliably than GARCH benchmarks across eight equity indices without requiring a parametric density.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7504480
- Identifier: SSRN 7504480
- Released: 2026-09-24
- First featured: Quant Letter No. 132 (2026-09-25): https://www.ml-quant.com/issues/2026-09-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility
- Authors: Laura Garcia-Jorcano, Angel Leon, Trino Manuel Ñíguez

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