A Student-t Copula Economic Scenario Generator for Emerging-Market Liability-Driven Investment: Density Forecasting, Tail Dependence, and Indonesian Evidence

Abstract

Pension funds and insurers in emerging markets size long-horizon capital with economic scenario generators (ESGs)—econometrically, simulators of a multivariate predictive density. The production benchmark draws its innovations by Gaussian Cholesky factorization, forcing thin marginal tails and zero joint tail dependence, so it cannot generate the simultaneous yield and currency shocks that dominate emerging-market stress; as supervisors move market-risk capital to expected shortfall, a density blind to joint extremes understates the number they now act on. We retain the three-layer liability-driven-investment cascade but replace its innovation engine with a Student-t copula over Student-t marginals, a more general specification of which the Gaussian benchmark is a special case, and validate it on Indonesian data from February 2010 to May 2026. A boundary-corrected bootstrap test rejects the Gaussian dependence structure; the heavy-tailed engine is the better-calibrated joint density forecast both in sample and in real-time rolling evaluation, with a scoring advantage that is clear in sample and small in real time; and the Gaussian default understates 99% expected-shortfall capital by about 11%, a direction that is robust across bootstrap resamples and across truncation and tail-heaviness settings, though imprecise in magnitude. Framed as a density forecast and judged by calibration and proper scores, the copula engine is better calibrated and more prudent, pricing the joint extremes the Gaussian default omits.