Uncertainty Quantification on Financial Valuation Model

DOI

10.2991/978-94-6463-262-0_116How to use a DOI?

Keywords

market uncertainty; multi-stage stochastic process; probability distribution; financial planning; decision-making

Abstract

The increasing complexity of financial markets and the introduction of sophisticated products have amplified investment volatility and uncertainty. The present work introduces a general framework for addressing uncertainty in financial multi-stage planning problems. To solve this uncertainty quantification problem, the proposed methodology involves randomization of vectors, dimension reduction via KL-expansion, and distribution transformation using the Maximum Entropy principle. Stochastic solvers, such as Monte Carlo, Generalized Moment methods, or Stochastic Collocation methods, are then employed for forward uncertainty propagation, mapping stochastic inputs to output, and generating the probability distribution. This integrated approach aims to enhance financial analysis and planning by providing a comprehensive understanding of the decision-making context and stochastic factors that influence investment outcomes.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yunfei Fan
PY  - 2023
DA  - 2023/10/09
TI  - Uncertainty Quantification on Financial Valuation Model
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 1141
EP  - 1152
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_116
DO  - 10.2991/978-94-6463-262-0_116
ID  - Fan2023
ER  -