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

TL;DR

This paper proposes quantum computing algorithms for dynamic asset pricing models, leveraging quantum superposition and entanglement to achieve exponential efficiency gains over classical methods, and introduces quantum decision-theoretic foundations for ambiguity and uncertainty.

Contribution

It introduces a novel quantum algorithmic framework for solving complex asset pricing models with exponential speed-up and incorporates quantum decision theory for handling ambiguity.

Findings

Quantum algorithms achieve exponential efficiency over classical methods.

Quantum states represent equilibrium asset prices.

Quantum decision-theoretic foundations address ambiguity and uncertainty.

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.