Pricing of range accrual swap in the quantum finance Libor Market Model

Baaquie, Belal E.; Du, Xin; Tang, Pan; Cao, Yang

doi:10.1016/j.physa.2014.01.042

Cited by 32 publications

(1 citation statement)

References 10 publications

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“…Quantum computing not only could be applied to an asset management or trading problem, but it could perform trading optimization, risk profiling and prediction. In this context, the work of Baaquie [53] represents an innovative solution. The objective of this study [53] consists in taking advantage of quantum finance theory to price the rate range accrual swaps which refer to the exchange of one set of cash flows for another.…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Machine Learning for Quantitative Finance Applications: A Survey

et al. 2019

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The analysis of financial data represents a challenge that researchers had to deal with. The rethinking of the basis of financial markets has led to an urgent demand for developing innovative models to understand financial assets. In the past few decades, researchers have proposed several systems based on traditional approaches, such as autoregressive integrated moving average (ARIMA) and the exponential smoothing model, in order to devise an accurate data representation. Despite their efficacy, the existing works face some drawbacks due to poor performance when managing a large amount of data with intrinsic complexity, high dimensionality and casual dynamicity. Furthermore, these approaches are not suitable for understanding hidden relationships (dependencies) between data. This paper proposes a review of some of the most significant works providing an exhaustive overview of recent machine learning (ML) techniques in the field of quantitative finance showing that these methods outperform traditional approaches. Finally, the paper also presents comparative studies about the effectiveness of several ML-based systems.

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Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Machine Learning for Quantitative Finance Applications: A Survey

et al. 2019

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Generalized affine transform on pricing quanto range accrual note

Huang

Zhang

2020

The North American Journal of Economics and Finance

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Quantum Field Theory for Economics and Finance

Baaquie

2018

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The underlying template running through all the chapters of this book is the application of the concepts of quantum field theory to the description of indeterminate and random phenomena, be they classical or quantum in origin. Quantum field theory was initially developed to explain the phenomena of high energy physics and soon spread to condensed matter and solid state physics. The common thread of these applications was that of a quantum system with a large number of degrees of freedom (independent variables). The pioneering work of Wilson (1983) and Witten (1989) showed that quantum field theory is not tied to quantum physics but, instead, has a wide range of applications in many other fields. These groundbreaking developments brought to the forefront what can be called quantum mathematics, mentioned earlier in the Preface. Quantum mathematics refers to the system of mathematical concepts that arise in quantum systems-with some of the leading concepts being that of quantum fields, vacuum expectation values, Hamiltonians, state spaces, operators, correlation functions, Feynman path integrals and Lagrangians. 1 The interpretation of quantum mathematics, in general, does not have any relation to physics and, instead, needs to reflect the specificity of the domain of inquiry to which quantum mathematics is being applied. Many of the standard books on quantum field theory are written primarily for a readership that is drawn from theoretical physics. There are voluminous and encyclopedic books on quantum field theory-such as the three-volume opus by Weinberg (2010) which runs for more than 1,500 pages-that are meant for professional theorists and researchers, being inaccessible to nonspecialists and beginners. 1 There is a clash of terminology regarding the term "Lagrangian." In economics, the term is used for the auxiliary function-for which there is no special term in physics-required when using a Lagrange multiplier for constrained optimization. In physics, the term "Lagrangian" encodes the fundamental model describing a quantum phenomenon, and has an ontological status equal to that of the Hamiltonian. In this book, physics terminology is used.

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Pricing of range accrual swap in the quantum finance Libor Market Model

Cited by 32 publications

References 10 publications

Machine Learning for Quantitative Finance Applications: A Survey

Machine Learning for Quantitative Finance Applications: A Survey

Generalized affine transform on pricing quanto range accrual note

Quantum Field Theory for Economics and Finance

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