Asset pricing under model uncertainty with finite time and states
In this study, we consider the asset pricing under model uncertainty with finite time and under a family of probability, and explore its relationship with risk neutral probability meastates structure. For the single-period securities model, we give a novel definition of arbitrage sure. Focusing on the financial market with short sales prohibitions, we separately investigate the necessary and sufficient conditions for no-arbitrage asset pricing based on nonlinear expectation which composed with a family of probability. When each linear expectation driven by the probability in the family of probability becomes martingale measure, the necessary and sufficient conditions are same, and coincide with the existing results. Furthermore, we expand the main results of single-period securities model to the case of multi-period securities model. By-product, we obtain the superhedging prices of contingent claim under model uncertainty.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On robust fundamental theorems of asset pricing in discrete time
This paper is devoted to a study of robust fundamental theorems of asset pricing in discrete time and finite horizon settings. Uncertainty is modelled by a (possibly uncountable) family of price processes on the same pro…
Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning
Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We propose a simple change: sort assets usin…
Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information
Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper propos…
Bayesian InferenceThe Risk-Neutral Equivalent Pricing of Model-Uncertainty
Existing approaches to asset-pricing under model-uncertainty adapt classical utility-maximization frameworks and seek theoretical comprehensiveness. We move toward practice by considering binary model-risks and by emphas…
modelNH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty
Language data are increasingly acquired and governed as assets, yet platforms often price candidate resources before knowing their true privacy or access costs. We study online pricing for governed language data assets u…