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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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19395877 · May 202619922001200920182026
48 results for cashflow decomposition

The study uses historical revenue data to forecast music catalog cashflows and multipliers.

problem Valuation of music catalogs based on historical revenue data.
method Risk-neutral approach using discounted cashflows formula.
result Ask prices are close to multipliers justified by median song cashflows, while best bids are near multipliers justified by bottom decile cashflows.

This paper studies the problem of optimal investment in incomplete markets, robust with respect to stopping times. We work on a Brownian motion framework and the stopping times are adapted to the Brownian filtration. Robustness can only be achieved for logartihmic utility, otherwise a cashflow should be added to the in…

2007-02-24abs ↗pdf ↗

The paper addresses pricing interest rate derivatives in markets with volatility uncertainty.

problem Pricing interest rate derivatives under uncertainty about volatility.
method Modeling volatility uncertainty with G-Brownian motion and defining forward sublinear expectation.
result Developed robust pricing formulas for interest rate derivatives.

The study assesses music as an investment asset class using discounted cashflow models.

problem Quantifying the risk and return characteristics of music royalty assets.
method Fitting three discounted cashflow models to Royalty Exchange platform transactions and backtesting performance.
result Life of Rights music assets had risk and return characteristics comparable to stocks in the S\&P500 over 5 years.

Deep learning approximates Bermudan option exposures and future values.

problem Computing accurate expected and future exposures for high-dimensional Bermudan options.
method Neural network-based approach combining Deep Optimal Stopping and regression.
result Neural network approximations of pathwise option values are more accurate.

In this paper, we study the dual representation for generalized multiple stopping problems, hence the pricing problem of general multiple exercise options. We derive a dual representation which allows for cashflows which are subject to volume constraints modeled by integer valued adapted processes and refraction period…

2011-12-12abs ↗pdf ↗

This paper provides intuition on the relationship of accrual and mark-to-market valuation for cash and forward interest rate trades. Discounted cashflow valuation is compared to spread-based valuation for forward trades, which explains the trader's view on valuation. This is followed by Taylor series approximation for …

2016-02-18abs ↗pdf ↗

Framework for pricing waterfall structures using simulation and uncertainty modeling.

problem Pricing complex structured finance instruments under uncertainty.
method Simulation-based uncertainty modeling, calibrated probability distributions, PyTorch implementation, Adjoint Algorithmic Differentiation (AAD).
result Efficient gradient computation for risk sensitivity analysis and optimization.

We introduce a new approach to incorporate uncertainty into the decision to invest in a commodity reserve. The investment is an irreversible one-off capital expenditure, after which the investor receives a stream of cashflow from extracting the commodity and selling it on the spot market. The investor is exposed to pri…

2018-03-15abs ↗pdf ↗

Optimal portfolios for fat-tailed risks using a new tail risk measure.

problem Optimizing portfolios for pension funds and insurance liabilities with extreme risk sensitivity.
method Developed a new tail risk measure (Extreme Deviation, XD) and optimized portfolios based on this measure.
result Optimal portfolios maximize return per unit of XD, balancing hedging and risk contributions.

In Bender and Dokuchaev (2013), we studied a control problem related to swing option pricing in a general non-Markovian setting. The main result there shows that the value process of this control problem can be uniquely characterized in terms of a first order backward SPDE and a pathwise differential inclusion. In the …

2014-02-26abs ↗pdf ↗

This research improves debt collection strategies using advanced machine learning.

problem Accurate estimation of propensity to pay and cashflow for optimal debt collection.
method Developed a machine learning framework with pre-processing and model selection.
result The proposed model outperforms current industry strategies.

The paper develops a model for sovereign debt dynamics with explicit maturity structure.

problem Analyzing the sustainability and risk of long-term sovereign debt issuance.
method Discrete-time model with explicit maturity structure, deterministic and stochastic extensions.
result The model identifies conditions for ergodic convergence and derives analytical formulas for key metrics.

The paper proposes and discusses semiorthogonal decompositions for moduli spaces of vector bundles.

problem Decompositions of moduli spaces of vector bundles with fixed determinant of odd degree.
method Semiorthogonal decompositions, Grothendieck ring of varieties, mirror symmetry, graph potentials, Fukaya category.
result Evidence for a conjectural semiorthogonal decomposition of moduli spaces of rank 2 bundles with odd determinant.

We combine aspects of the notions of finite decomposition complexity and asymptotic property C into a notion that we call finite APC-decomposition complexity. Any space with finite decomposition complexity has finite APC-decomposition complexity and any space with asymptotic property C has finite APC-decomposition comp…

2017-09-04abs ↗pdf ↗

Study shows OAT decomposition generates unexplained profit and loss, while SU decompositions depend on risk factor order.

problem Understanding profit and loss attribution in financial markets.
method Used financial market data from 2003 to 2022 to compare OAT, SU, and ASU decompositions.
result SU decompositions are sensitive to risk factor order and cannot identify all relevant risk factors.

New tensor network decompositions improve CNN performance.

problem Limited exploration of tensor network decompositions for CNNs.
method Characterized a new class of CNN modules and experimentally compared various decompositions.
result Some nonlinear decompositions outperform existing ones in terms of accuracy and efficiency.

A double pants decomposition of a 2-dimensional surface is a collection of two pants decomposition of this surface introduced in arXiv:1005.0073v2. There are two natural operations acting on double pants decompositions: flips and handle twists. It is shown in arXiv:1005.0073v2 that the groupoid generated by flips and h…

2010-08-22abs ↗pdf ↗

Let J1\mathcal{J}^1 be the real form of a complex simple Jordan algebra such that the automorphism group is F4(20)\mathrm{F}_{4(-20)}. By using some orbit types of F4(20)\mathrm{F}_{4(-20)} on J1\mathcal{J}^1, for F4(20)\mathrm{F}_{4(-20)}, explicitly, we give the Iwasawa decomposition, the Oshima--Sekiguchi's KεK_ε-Iwasawa decomp…

2011-09-05abs ↗pdf ↗

We study the topological types of pants decompositions of a surface by associating to any pants decomposition P,P, in a natural way its pants decomposition graph, Γ(P).Γ(P). This perspective provides a convenient way to analyze the maximum distance in the pants complex of any pants decomposition to a pants decomposition c…

2011-06-07abs ↗pdf ↗

New method uses random decompositions for high-dimensional Bayesian optimization.

problem Learning accurate decompositions for high-dimensional black-box functions.
method Data-independent random tree-based decomposition sampling.
result Random decomposition upper-confidence bound algorithm (RDUCB) yields significant empirical gains.

Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.

problem Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.
method Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.
result Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.

Decompositions on manifolds appear in various geometric structures. Necessary and sufficient conditions for quotient spaces of decompositions to be manifolds are widely characterized. We characterize necessary and sufficient conditions to be kk-manifolds (k=1,2)(k = 1, 2), which generalize characterizations in the codimens…

2017-03-15abs ↗pdf ↗

We give an example of two JSJ decompositions of a group that are not related by conjugation, conjugation of edge-inclusions, and slide moves. This answers the question of Rips and Sela stated in "Cyclic splittings of finitely presented groups and the canonical JSJ decomposition," Ann. of Math. 146 (1997), 53-109. On th…

2001-10-17abs ↗pdf ↗

We consider a union of two pants decompositions of the same orientable 2-dimensional surface of any genus g. Each pants decomposition corresponds to some handlebody bounded by this surface, so two pants decompositions correspond to a Heegaard splitting of a 3-manifold. We introduce a groupoid FT acting on double pants …

2010-05-01abs ↗pdf ↗

We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters. Thanks to the well-developed theory of information geometry, the reconstructed tensor is unique and always minimizes the KL divergence from an inpu…

2018-02-13abs ↗pdf ↗

The aim of this paper is to provide some new tools to aid the study of decomposition complexity, a notion introduced by Guentner, Tessera and Yu. In this paper, three equivalent definitions for decomposition complexity are established. We prove that metric spaces with finite hyperbolic dimension have finite (weak) deco…

2015-09-22abs ↗pdf ↗