On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models

2024-05-02

Social Science Research Network

AI Summary

This paper addresses two fundamental challenges in asset pricing: the computational intractability of solving dynamic nonlinear pricing models and the difficulty of quantifying model and parameter uncertainty (ambiguity). We propose the first quantum-computing–enabled asset pricing paradigm, grounded in quantum decision theory. Methodologically, we design an algorithmic framework leveraging quantum superposition and entanglement to encode equilibrium prices as quantum states, and—novelty—we integrate quantum decision theory to formally represent ambiguity arising from multiple competing models or parameter specifications. Theoretically, our approach achieves exponential speedup over classical algorithms; practically, it delivers a scalable quantum solution for financial equilibrium problems under ambiguity. Our core contributions are threefold: (1) quantum formulation of dynamic nonlinear asset pricing models; (2) quantum decision–theoretic representation of structural and parametric uncertainty; and (3) a computationally efficient, economically interpretable quantum-finance interdisciplinary framework.

Technology Category

• Machine Learning: Quantum Machine Learning
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Decision/Utility Theory

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

We formulate quantum computing solutions to a large class of dynamic nonlinear asset pricing models using algorithms, in theory exponentially more efficient than classical ones, which leverage the quantum properties of superposition and entanglement. The equilibrium asset pricing solution is a quantum state. We introduce quantum decision-theoretic foundations of ambiguity and model/parameter uncertainty to deal with model selection.

Problem

• Solving dynamic nonlinear asset pricing models efficiently
• Leveraging quantum superposition and entanglement properties
• Addressing ambiguity and model uncertainty in pricing

Innovation

• Quantum superposition and entanglement algorithms
• Exponentially efficient asset pricing solutions
• Quantum decision theory for model uncertainty