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Duality of causal distributionally robust optimization: the discrete-time case (2401.16556v1)

Published 29 Jan 2024 in math.PR and math.OC

Abstract: This paper studies distributionally robust optimization (DRO) in a dynamic context. We consider a general penalized DRO problem with a causal transport-type penalization. Such a penalization naturally captures the information flow generated by the dynamic model. We derive a tractable dynamic duality formula under mild conditions. Furthermore, we apply this duality formula to address distributionally robust version of average value-at-risk, stochastic control, and optimal stopping.

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References (44)
  1. Causal optimal transport and its links to enlargement of filtrations and continuous-time stochastic optimization. Stochastic Processes and their Applications, 130(5):2918–2953, May 2020.
  2. D. Aldous. Weak convergence and the general theory of processes. Incomplete draft of monograph, July 1981.
  3. Causal transport in discrete time and applications. SIAM J. Optim., 27(4):2528–2562, Jan. 2017.
  4. Existence, duality, and cyclical monotonicity for weak transport costs. Calculus of Variations and Partial Differential Equations, 58(6):203, 2019.
  5. Adapted Wasserstein distances and stability in mathematical finance. Finance Stoch, 24(3):601–632, July 2020a.
  6. All adapted topologies are equal. Probab. Theory Relat. Fields, 178(3-4):1125–1172, Dec. 2020b.
  7. Fundamental properties of process distances. Stochastic Processes and their Applications, 130(9):5575–5591, Sept. 2020c.
  8. Adapted Wasserstein distance between the laws of SDEs, Sept. 2022. arXiv:2209.03243.
  9. Wasserstein distributional robustness of neural networks. In Advances in Neural Information Processing Systems. Curran Associates, Inc., Dec. 2023.
  10. D. Bartl and J. Wiesel. Sensitivity of multi-period optimization problems in adapted Wasserstein distance, Aug. 2023. SIAM J. Financial Math. (forthcoming).
  11. Computational aspects of robust optimized certainty equivalents and option pricing. Mathematical Finance, 30(1):287–309, 2020.
  12. The Wasserstein space of stochastic processes, Apr. 2021a. arXiv:2104.14245.
  13. Sensitivity analysis of Wasserstein distributionally robust optimization problems. Proc. R. Soc. A., 477(2256):20210176, Dec. 2021b.
  14. Sensitivity of robust optimization problems under drift and volatility uncertainty, 2023. arXiv:2311.11248.
  15. D. Bertsekas and S. E. Shreve. Stochastic Optimal Control: The Discrete-Time Case. Athena Scientific, Dec. 1996.
  16. J. Bion–Nadal and D. Talay. On a Wasserstein-type distance between solutions to stochastic differential equations. Ann. Appl. Probab., 29(3), June 2019.
  17. J. Blanchet and K. Murthy. Quantifying distributional model risk via optimal transport. Mathematics of OR, 44(2):565–600, May 2019.
  18. Robust wasserstein profile inference and applications to machine learning. Journal of Applied Probability, 56(3):830–857, 2019.
  19. Distributionally robust mean-variance portfolio selection with wasserstein distances. Management Science, 68(9):6382–6410, 2022.
  20. B. Bouchard and M. Nutz. Arbitrage and duality in nondominated discrete-time models. The Annals of Applied Probability, 25(2):823–859, Apr. 2015.
  21. S. Eckstein and G. Pammer. Computational methods for adapted optimal transport, Mar. 2022. arXiv:2203.05005.
  22. K. Fan. Minimax theorems. Proceedings of the National Academy of Sciences, 39(1):42–47, Jan. 1953.
  23. H. Föllmer and A. Schied. Stochastic Finance: An Introduction in Discrete Time. In Stochastic Finance. De Gruyter, Dec. 2008.
  24. R. Gao and A. Kleywegt. Distributionally robust stochastic optimization with wasserstein distance. Mathematics of OR, Aug. 2022.
  25. C. A. García Trillos and N. García Trillos. On the regularized risk of distributionally robust learning over deep neural networks. Res Math Sci, 9(3):54, Aug. 2022.
  26. B. Han. Distributionally robust risk evaluation with a causality constraint and structural information, Apr. 2023. arXiv:2203.10571.
  27. M. F. Hellwig. Sequential decisions under uncertainty and the maximum theorem. Journal of Mathematical Economics, 25(4):443–464, Jan. 1996.
  28. F. H. Knight. Risk, Uncertainty and Profit. Boston, New York, Houghton Mifflin Company, 1921.
  29. Risk measures based on weak optimal transport, Dec. 2023. arXiv:2312.05973.
  30. R. Lassalle. Causal transport plans and their Monge–Kantorovich problems. Stochastic Analysis and Applications, 36(3):452–484, May 2018.
  31. J. Lott and C. Villani. Ricci curvature for metric-measure spaces via optimal transport. Annals of Mathematics, 169(3):903–991, 2009.
  32. P. Mohajerin Esfahani and D. Kuhn. Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Math. Program., 171(1):115–166, Sept. 2018.
  33. M. Nendel and A. Sgarabottolo. A parametric approach to the estimation of convex risk functionals based on Wasserstein distance, Oct. 2022. arXiv:2210.14340.
  34. G. Pammer. A note on the adapted weak topology in discrete time, May 2022. arXiv:2205.00989.
  35. S. Peng. Nonlinear Expectations and Stochastic Calculus under Uncertainty: With Robust CLT and G-Brownian Motion, volume 95 of Probability Theory and Stochastic Modelling. Springer, 2019.
  36. G. C. Pflug. Version-independence and nested distributions in multistage stochastic optimization. SIAM J. Optim., 20(3):1406–1420, Jan. 2010.
  37. G. C. Pflug and A. Pichler. A distance for multistage stochastic optimization models. SIAM J. Optim., 22(1):1–23, Jan. 2012.
  38. H. Rahimian and S. Mehrotra. Distributionally robust optimization: A review, Aug. 2019. arXiv:1908.05659.
  39. R. T. Rockafellar. Convex Analysis. Princeton University Press, Princeton, 1970.
  40. L. Rüschendorf. The Wasserstein distance and approximation theorems. Z. Wahrscheinlichkeitstheorie verw Gebiete, 70(1):117–129, Mar. 1985.
  41. A. Shapiro. Minimax and risk averse multistage stochastic programming. European Journal of Operational Research, 219(3):719–726, June 2012.
  42. M. Xu. Risk measure pricing and hedging in incomplete markets. Annals of Finance, 2(1):51–71, Jan. 2006.
  43. T. Yamada and S. Watanabe. On the uniqueness of solutions of stochastic differential equations. Journal of Mathematics of Kyoto University, 11(1):155–167, Jan. 1971.
  44. A simple and general duality proof for wasserstein distributionally robust optimization, Oct. 2022. arXiv:2205.00362.
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