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Takanori Adachi

Publications and source records attributed to Takanori Adachi.

12 recordsLinked to original sources

Aharanov-Bohm Type Arbitrage and Homological Obstructions in Financial Markets

We introduce a simplicial and categorical formulation of Aharonov--Bohm (AB) type arbitrage in filtered market systems. Given a filtration modeled as a contravariant functor $F : \mathcal T^{op} \to \mathbf{Prob},$ we consider the associated conditional expectation transport functor $\mathcal E \circ F : \mathcal T^{op} \to \mathbf{Ban},$ and the canonical distortion $dF(i) := (\mathcal E \circ F)(i)(1),$ which measures the failure of constant functions to be preserved under non-measure-preserving transitions. Motivated by the multiplicative transport structure of $dF$, we introduce a simplicial distortion operator defined recursively on the nerve $N_\bullet(\mathcal T)$ of the time category. This construction describes recursively accumulated transported distortions along composable chains of morphisms and leads naturally to a notion of holonomy along loops. We interpret non-trivial holonomy as a global observable invisible at the level of individual transitions, analogous to the Aharonov--Bohm effect in physics. This yields a notion of AB arbitrage, in which arbitrage opportunities arise from global loop effects rather than local price discrepancies. We further introduce simplicial admissibility conditions ensuring that recursively accumulated distortions remain integrable, and show how non-trivial holonomy can be translated into base-time observable self-financing trading strategies through executable loop dynamics. This establishes a connection between categorical holonomy structures and economically realizable arbitrage. The framework developed here suggests a global and homological perspective on arbitrage theory, in which market inconsistencies are encoded by recursively accumulated simplicial distortions and their holonomy along loops in the underlying time category.

q-fin.MF↗

Martingale Cohomology, Holonomy, and Homological Arbitrage

We introduce a transport cohomological framework for categorical filtrations. Given a contravariant filtration $F:\mathcal T^{op}\to\mathbf{Prob}$ on a small category \(\mathcal T\), conditional expectation induces transport operators between local probabilistic states. Using the simplicial structure of the nerve \(N_\bullet(\mathcal T)\), we construct simplex-local cochain complexes associated with parametrized simplices and study their transport cohomology. The resulting framework naturally produces loop effects and holonomy structures. In particular, transport around closed simplicial histories may generate nontrivial probabilistic distortions, even when the initial and terminal objects coincide. The associated holonomy operators encode global transport effects between probabilistic states and detect obstructions generated by loop transport. This leads to the notion of homological arbitrage, understood as a global transport phenomenon emerging from probabilistic distortion along loops. From this viewpoint, the essential source of loop effects is the probabilistic distortion generated by transport around closed simplicial histories. The present framework is structurally analogous to parallel transport and holonomy in differential geometry, providing a geometric viewpoint on categorical filtrations and probabilistic transport structures.

q-fin.MF↗

A geometric model of synthetic filtrations via context-dependent time

Classical filtrations in probability theory formalize the accumulation of information along a linear time axis: the past is unique and the present evolves into an uncertain future. In reality, however, this linearity may itself be an illusion - an artifact of human perception that collapses multiple possible histories into a single apparent path. In this paper, we propose a geometric and homological model of synthetic filtrations, where the present arises as a synthesis of many potential pasts. To achieve this, we introduce a new category $Σ$, extending the simplex category $Δ$ so that each moment of time carries contextual structure. Synthetic filtrations are realized as contravariant functors $Σ^{op} \to Prob$, where $Prob$ is the category of probability spaces with null-preserving maps. We then develop a homological analysis of $Σ$-filtrations, constructing chain complexes whose boundaries are given by conditional expectations. Their homology groups measure informational "holes" - probabilistic obstructions arising from the incompatibility of contextual expectations. As a concrete realization, we define Dirichlet filtrations, in which measures on simplices arise from Dirichlet distributions, reflecting both parameter and contextual uncertainty. Bayesian updating is then interpreted as a categorical transformation of a Dirichlet functor, revealing learning as a reconstruction of coherence across contexts. This framework suggests that what appears as a linear temporal order is merely a projection of a higher contextual geometry. It unifies categorical probability, homological algebra, and Bayesian reasoning, offering a new language for uncertainty in mathematics, finance, and cognition.

math.PR↗

Hierarchical Structure of Uncertainty

We introduce a new concept called uncertainty spaces which is an extended concept of probability spaces. Then, we express n-layer uncertainty which we call hierarchical uncertainty by a hierarchically constructed sequence of uncertainty spaces, called a U-sequence. We use U-sequences for providing examples that illustrate Ellsberg's paradox. We will use the category theory to get a bird's eye view of the hierarchical structure of uncertainty. We discuss maps between uncertainty spaces and maps between U-sequences, seeing that they form categories of uncertainty spaces and the category of U-sequences, respectively. We construct an endofunctor of the category of measurable spaces in order to embed a given U-sequence into it. Then, by the iterative application of the endofunctor, we construct the universal uncertainty space which may be able to serve as a basis for multi-layer uncertainty theory.

q-fin.MF↗

Discrete signature and its application to finance

Signatures, one of the key concepts of rough path theory, have recently gained prominence as a means to find appropriate feature sets in machine learning systems. In this paper, in order to compute signatures directly from discrete data without going through the transformation to continuous data, we introduced a discretized version of signatures, called "flat discrete signatures". We showed that the flat discrete signatures can represent the quadratic variation that has a high relevance in financial applications. We also introduced the concept of "discrete signatures" that is a generalization of "flat discrete signatures". This concept is defined to reflect the fact that data closer to the current time is more important than older data, and is expected to be applied to time series analysis. As an application of discrete signatures, we took up a stock market related problem and succeeded in performing a good estimation with fewer data points than before.

q-fin.MF↗

Generalized Filtrations and Its Application to Binomial Asset Pricing Models

We introduce generalized filtration with which we can represent situations such as some agents forget information at some specific time. The filtration is defined as a functor to a category Prob whose objects are all probability spaces and whose arrows correspond to measurable functions satisfying an absolutely continuous requirement [Adachi and Ryu, 2019]. As an application of a generalized filtration, we develop a binomial asset pricing model, and investigate the valuations of financial claims along this type of non-standard filtrations.

q-fin.MF↗

A Binomial Asset Pricing Model in a Categorical Setting

Adachi and Ryu introduced a category Prob of probability spaces whose objects are all probability spaces and whose arrows correspond to measurable functions satisfying an absolutely continuous requirement in [Adachi and Ryu, 2019]. In this paper, we develop a binomial asset pricing model based on Prob. We introduce generalized filtrations with which we can represent situations such as some agents forget information at some specific time. We investigate the valuations of financial claims along this type of non-standard filtrations.

q-fin.MF↗

Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study

This letter investigates the dynamic relationship between market efficiency, liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin time series is anti-persistent. After 2013, as liquidity increased, the Hurst exponent rose to approximately 0.5, improving market efficiency. For several periods, however, the Hurst exponent was found to be significantly less than 0.5, making the time series anti-persistent during those periods. We also investigate the multifractal degree of the Bitcoin time series using the generalized Hurst exponent and find that the multifractal degree is related to market efficiency in a non-linear manner.

q-fin.ST↗

Multi-Dimensional Pass-Through and Welfare Measures under Imperfect Competition

This paper provides a comprehensive analysis of welfare measures when oligopolistic firms face multiple policy interventions and external changes under general forms of market demands, production costs, and imperfect competition. We present our results in terms of two welfare measures, namely, marginal cost of public funds and incidence, in relation to multi-dimensional pass-through. Our arguments are best understood with two-dimensional taxation where homogeneous firms face unit and ad valorem taxes. The first part of the paper studies this leading case. We show, e.g., that there exists a simple and empirically relevant set of sufficient statistics for the marginal cost of public funds, namely unit tax and ad valorem pass-through and industry demand elasticity. We then specialize our general setting to the case of price or quantity competition and show how the marginal cost of public funds and the pass-through are expressed using elasticities and curvatures of regular and inverse demands. Based on the results of the leading case, the second part of the paper presents a generalization with the tax revenue function specified as a general function parameterized by a vector of multi-dimensional tax parameters. We then argue that our results are carried over to the case of heterogeneous firms and other extensions.

econ.GN↗

A Category of Probability Spaces

We introduce a category Prob of probability spaces whose objects are all probability spaces and arrows are corresponding to measurable functions satisfying an absolutely continuous requirement. We can consider a Prob-arrow as an evolving direction of information with a way of its interpretation. We introduce a contravariant functor E from Prob to Set, the category of sets. The functor E provides conditional expectations along arrows in Prob, which are generalizations of the classical conditional expectations. For a Prob arrow f, we introduce two concepts f-measurability and f-independence and investigate their interaction with conditional expectations along f. We also show that the completion of probability spaces is naturally formulated as an endofunctor of Prob.

math.PR↗

Monetary value measures in a category of probability spaces

We generalize the notion of monetary value measures developed with category theory in [Adachi, 2014] by extending their base category from the category \c{hi} to the category of probability spaces Prob introduced in [Adachi and Ryu, 2016].

q-fin.MF↗

A Framework for Analyzing Stochastic Jumps in Finance based on Belief and Knowledge

We introduce a formal language IE that is a variant of the language PAL developed in [van Benthem 2011] by adding a belief operator and a common belief operator,specializing to stochastic analysis. A constant symbol in the language denotes a stochastic process so that we can represent several financial events as formulae in the language, which is expected to be clues of analyzing the moments that some stochastic jumps such as financial crises occur based on knowledge and belief of individuals or those shared within groups of individuals. In order to represent beliefs, we use sigma-complete Boolean algebras as generalized sigma-algebras. We use the representation for constructing a model in which the interpretations of the formulae written in the language IE reside. The model also uses some new categories for integrating several components appeared in the theory into one.

q-fin.MF↗