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An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

A Non-Stochastic Special Model of Risk Based on Radon Transform.

Marcin Makowski¹, Edward W Piotrowski¹

¹Faculty of Physics, Department of Mathematical Methods in Physics, University of Białystok, ul. Ciołkowskiego 1L, 15-245 Białystok, Poland.

Entropy (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

This study introduces a novel financial risk model where risk is linked to the ease of selling an instrument. It uses the Radon transform, connecting financial risk to physics concepts like uncertainty.

Keywords:

Radon transform complex system error financial instrument information market risk uncertainty

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Area of Science:

Financial mathematics
Complex systems analysis
Physics of uncertainty

Background:

Risk is a fundamental concept across sciences like physics, biology, and engineering.
Complex systems, particularly financial markets, heavily rely on understanding risk.
Existing models often depend on statistical assumptions, necessitating alternative approaches.

Purpose of the Study:

To introduce a novel risk model with a transactional-financial interpretation.
To redefine financial risk based on the potential for loss and opportunities for disposal (selling).
To explore the subjective perception of risk by introducing financial time and a financial frame of reference.

Main Methods:

Development of a risk model based on the transactional interpretation of models.
Introduction of the concepts of financial time and a financial frame of reference.
Proposal of the Radon transform for quantifying financial risk.

Main Results:

A new framework for understanding financial risk is presented.
The model establishes a link between the number of disposal opportunities and the level of risk.
The Radon transform is demonstrated as a viable tool for risk measurement.

Conclusions:

The proposed risk model offers a non-statistical approach rooted in transactional principles.
The methodology connects financial risk to fundamental physics concepts such as uncertainty, entropy, and information.
Computed tomography algorithms can be applied to uncertainty analysis in experimental physics.