Jordi Llorens-Terrazas, Mika Meitz
arXiv 15 Jun 2026 · Econometrics
arXiv:2606.16773 · PDF · DOI · OpenAlex · Extracted main text
We propose a flexible framework for modeling the predictive distributions of nonlinear, possibly multivariate time series. Our approach expresses a general predictive distribution in an appropriate generative representation that is based on a folklore result from measure theoretic probability. This representation provides a direct simulation-based approximation to the predictive distribution, enabling straightforward computation of forecasts for the conditional mean and variance, fan charts, value at risk, expected shortfall, joint tail risks, and other quantities of interest. We estimate this generative representation using a version of conditional generative adversarial networks and provide a formal statistical analysis of estimation under weak temporal dependence. Specifically, estimation is expressed as a particular minimax problem and we establish consistency of its approximate solutions in Hausdorff distance. The empirical relevance of the approach is illustrated using applications to equity returns, realized variance, and realized covariances. The proposed method is also computationally manageable, with estimation in our applications taking approximately one minute on a standard laptop.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Song, Shanshan and Wang, Tong and Shen, Guohao and Lin, Yuanyuan and… (2026) Wasserstein generative regression | 1.000 | 10 | 3 | 100% |
| 2 | Zhou, Xingyu and Jiao, Yuling and Liu, Jin and Huang, Jian (2023) A deep generative approach to conditional sampling | 0.979 | 16 | 5 | 94% |
| 3 | Meitz, Mika (2024) Statistical inference for generative adversarial networks and other minimax problems self | 0.956 | 8 | 4 | 88% |
| 4 | Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu… (2014) Generative adversarial nets | 0.843 | 3 | 3 | 100% |
| 5 | Arcones, Miguel Angel and Yu, Bin (1994) Central limit theorems for empirical and U-processes of stationary mixing sequences | 0.737 | 4 | 3 | 50% |
| 6 | Hornik, Kurt and Stinchcombe, Maxwell and White, Halbert (1989) Multilayer feedforward networks are universal approximators | 0.737 | 3 | 2 | 100% |
| 7 | Kallenberg, Olav (2021) Foundations of Modern Probability | 0.644 | 5 | 2 | 40% |
| 8 | Guidolin, Massimo and Timmermann, Allan (2008) International asset allocation under regime switching, skew, and kurtosis preferences | 0.644 | 2 | 2 | 100% |
| 9 | Shmuel Kandel and Robert F. Stambaugh (1996) On the Predictability of Stock Returns: An Asset-Allocation Perspective | 0.644 | 2 | 2 | 100% |
| 10 | Yarotsky, Dmitry (2017) Error bounds for approximations with deep ReLU networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 75 scored citations.