Generative Scenario Modeling for Stress Testing: Develop generative ML models (e.g., GANs, VAEs) to Simulate Realistic Stress Scenarios Reflecting Private Market Outcomes (Cash Flows, Valuations, Distributions), Informing Stress Testing and Contingency Planning
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v7i1.4918
Abstract
Alternative investments can deliver diversification and risk premia, but for LPs the most dangerous feature in stress periods is not mark-to-market valuation drawdowns, but "same-direction cash-flow squeeze": distributions fall sharply or even freeze, while capital calls become more concentrated and more frequent, making liquidity management passive. The latent-factor cash flow model proposed by Cao and van Beek introduces systematic latent factors and heavy-tailed idiosyncratic terms into the TA framework, and links call rates, distribution rates, and growth rates to macro variables, making stress testing estimable, transmissible, and reproducible. Building on this, this paper proposes Generative Scenario Modeling (GSM): using the latent-factor model as an interpretable backbone, and under the cash flow–NAV identity and business-boundary constraints, introducing conditional GANs/conditional VAEs to learn tail dependence, nonlinear regime shifts, and scenario-cluster structure, generating a stress-scenario library of quarterly net cash flows, NAV paths, valuation adjustments, and distribution changes, for shortfall distribution estimation, commitment pacing and rebalancing evaluation, and tiered trigger-based contingency planning. The paper concludes by engaging the discussion of "whether LPs should use AI to assist decision-making," emphasizing definition consistency, confidentiality and compliance, model governance, and human review, to avoid treating AI as an automatic adjudicator. Keywords: private markets; cash flow risk; stress testing; generative models; scenario library; LP liquidity management.
Keywords
private markets; LP liquidity risk; latent-factor cash flow model; conditional generative models; scenario library
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