Pith. sign in

REVIEW 3 cited by

Tail-GAN: Learning to Simulate Tail Risk Scenarios

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2203.01664 v4 pith:QA2KUSRY submitted 2022-03-03 q-fin.RM

classification q-fin.RM
keywords riskscenariostailclassdatadata-drivendynamicjoint
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-driven approach for simulating realistic, high-dimensional multi-asset scenarios, focusing on accurately representing tail risk for a class of static and dynamic trading strategies. We exploit the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES) to design a Generative Adversarial Network (GAN) that learns to simulate price scenarios preserving these tail risk features. We demonstrate the performance of our algorithm on synthetic and market data sets through detailed numerical experiments. In contrast to previously proposed data-driven scenario generators, our proposed method correctly captures tail risk for a broad class of trading strategies and demonstrates strong generalization capabilities. In addition, combining our method with principal component analysis of the input data enhances its scalability to large-dimensional multi-asset time series, setting our framework apart from the univariate settings commonly considered in the literature.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Extreme Value Perspective on Learning Stress Laws

    q-fin.RM 2026-07 conditional novelty 6.5 of 10

    SS-GEN splices an explicit radial tail law with a DGM-learned angular law so standard generative models produce asymptotically exact multivariate extremes and rare-event probabilities beyond the data.

  2. Dynamic data generation and dynamic portfolio selection: an application of a score-based diffusion model

    q-fin.PM 2025-07 reject novelty 6.0 of 10

    An adaptive score-based diffusion model generates sequential market scenarios with adapted-Wasserstein error bounds, and a policy-gradient agent trained on these scenarios outperforms several portfolio benchmarks.

  3. Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events

    cs.LG 2025-06 accept novelty 4.0 of 10

    A review of synthetic data generation for extreme events that compiles methods, datasets, and an evaluation framework focused on extremeness rather than privacy.

Pith tools