Empirical analysis of quantum finance interest rates models

Baaquie, Belal E.; Cao, Yang

doi:10.1016/j.physa.2009.02.044

Cited by 43 publications

(6 citation statements)

References 7 publications

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“…These theoretical explorations were supported by empirical investigations, such as those conducted by Baaquie and Yang (2009) [5], which validated the efficacy of quantum models in capturing the dynamics of financial markets, particularly for interest rate predictions. Additionally, Agrawal and Sharda (2010) [6] extended the quantum approach to human decision-making processes, offering a fresh perspective on behavioral finance by modeling decision-making under uncertainty with quantum probabilistic frameworks.…”

Section: Literature Reviewmentioning

confidence: 75%

Quantum Temporal Winds: Turbulence in Financial Markets

Zheng,

Dong

2024

Mathematics

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This paper leverages turbulence theory from physics to examine the similarities and differences between financial market volatility and turbulent phenomena on a statistical physics level. By drawing analogies between the dynamics of financial markets and fluid turbulence, an innovative analytical framework has been developed to enhance our understanding of the complexity inherent in financial markets. The research methodology involves a comparative analysis of several national stock market indices and simulated turbulent velocity time series, with a particular focus on key statistical properties such as probability distributions, correlation structures, and power spectral densities. Furthermore, a financial market capital flow model has been established, and corresponding solutions have been proposed. Through computational simulations and data analysis, it was discovered that financial market volatility shares some statistical characteristics with turbulence, yet there are significant differences in the shape of probability distributions and the timescales of correlations. This indicates that although financial markets exhibit patterns similar to turbulence, as a multivariate-driven complex system, their behavioral patterns do not completely correspond to natural turbulence phenomena, highlighting the limitations of directly applying turbulence theory to financial market analysis. Additionally, the study explores the use of Bézier curves to simulate market volatility and, based on these analyses, formulates trading strategies that demonstrate practical applications in risk management. This research provides fresh perspectives for the fields of financial market theory and econophysics, offering new insights into the complexity of financial markets and the prevention and management of financial risks.

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Section: Literature Reviewmentioning

confidence: 75%

Quantum Temporal Winds: Turbulence in Financial Markets

Zheng,

Dong

2024

Mathematics

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“…Baaquie (2009) shifts the focus to the finance industry with the Libor market model (LMM), the benchmark for interest rate modeling for both Libor and Euribor rates. Baaquie proposes a quantum finance formulation of the LMM, leading to a critical generalization that Libors, corresponding to different future times, are not perfectly correlated.Extending this quantum perspective to empirical analysis, Baaquie et al (2009) calibrate and test models of interest rates using futures data for Libor and Euribor. Their study compares two models-the linear theory of bond forward interest rates and the non-linear theory embedded in the Libor Market Model.…”

Section: Quantum Financementioning

confidence: 99%

Quantum Temporal Winds: Turbulence in Financial Markets

Zheng,

Dong

2024

Preprint

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This paper draws upon the theory of turbulence in physics to explore the similarities and differences between volatility in financial markets and turbulent phenomena at a statistical physics level. By analogizing the dynamics of financial markets with fluid turbulence, we have developed an innovative analytical framework aimed at deepening the understanding of the complexity inherent in financial markets. Our research methodology includes a comparative analysis of simulated time series data for the Standard & Poor's 500 Index and fully developed turbulent velocity time series. We focus on the similarities in key statistical physical properties, including probability distributions, correlation structures, and power spectral densities. Furthermore, we have established a model of capital flow in financial markets and proposed corresponding solutions. Through computational simulations and data analysis, we find that while financial market volatility shares some statistical characteristics with turbulent phenomena, significant differences exist in the shapes of probability distributions and the temporal scales of correlations. This suggests that although financial markets exhibit patterns akin to turbulence, the behavior of this complex, multi-variable driven system does not completely correspond to natural turbulence, highlighting the limitations of directly applying turbulence theory to financial market analysis. Our study offers a new perspective for the theories of financial markets and the field of econophysics, providing fresh insights into the complexity of financial markets and contributing to the prevention and management of financial risk.

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“…The generalized and flexible approach of quantum-like modeling makes it a primary candidate for any situations that involves the formalization of uncertainty. This is one of the main reasons that researchers in economics and finance have used the quantum-like modeling approaches for various research aspects of their field such as price dynamics models (Choustova, 2007), stock prices (Pedram, 2012), interest rates (Baaquie and Yang, 2009) and so on. Quantum-like modeling is even becoming a subject of interest for analyzing the behavior of animals and species such as lizards and the way they react and make decisions in their society (Aerts et al, 2013b).…”

Section: What Can Quantum-like Modeling Bring To Computational Domains?mentioning

confidence: 99%