Research
On-device research index

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.

168,657 papers · 148 categories

Trend · papers per month

3517021,0521,403 · Jun 202019922001200920172026
48 results for two-factor model

Study analyzes correlation structure in two-factor Hull-White model for XVA calculations.

problem Capturing the correlation structure in two-factor Hull-White model for accurate XVA calculations.
method Combination of approximation formula and Monte-Carlo simulation to investigate correlation structure.
result Hull-White model effectively captures de-correlation of the yield curve under specific parameter conditions.

The study examines how global economic policy uncertainty affects crude oil futures volatility.

problem Predicting crude oil futures volatility using global economic policy uncertainty.
method Established single-factor and two-factor models under the GARCH-MIDAS framework, tested with rolling-window and fixed-span specifications.
result GEPU changes have stronger predictive power than the GEPU index for crude oil futures volatility.

For an affine two factor model, we study the asymptotic properties of the maximum likelihood and least squares estimators of some appearing parameters in the so-called subcritical (ergodic) case based on continuous time observations. We prove strong consistency and asymptotic normality of the estimators in question.

2013-02-14abs ↗pdf ↗

Divides state space into regions with identical term structure shapes.

problem Classifying term structure shapes in the two-factor Vasicek model.
method Using envelopes and winding numbers to divide and classify the state space.
result Nearly complete classification of parameter space regarding term structure shapes.

A simplified model for fixed income portfolio optimisation.

problem Modeling interest rates and credit risk in fixed income portfolios.
method Proposes a two-factor model for the time evolution of the efficient frontier.
result The efficient frontier is mainly controlled by linear constraints, with standard deviation less important.

Investment decision triggered by a convex curve in a two-factor uncertainty model.

problem Optimal irreversible investment in a company with two products whose prices follow geometric Brownian motions.
method Two-dimensional optimal stopping problem, nonlinear integral equation, convex curve characterization.
result Optimal investment decision is characterized by a convex curve, unique solution to a nonlinear integral equation.

Paper presents a fast algorithm for pricing Bermudan swaptions under the two-factor Hull-White model.

problem Evaluating Bermudan swaption prices under the two-factor Hull-White model with high computational efficiency.
method Discretization of expected value calculation, Gaussian kernel sums, fast Gauss transform, grid rotation for stability.
result Significant reduction in computation time and improved stability for correlation close to -1.

Empirical evidence suggests that fixed income markets exhibit unspanned stochastic volatility (USV), that is, that one cannot fully hedge volatility risk solely using a portfolio of bonds. While [1] showed that no two-factor Cox-Ingersoll-Ross (CIR) model can exhibit USV, it has been unknown to date whether CIR models …

2017-05-08abs ↗pdf ↗

The paper solves complex swing option pricing equations with numerical methods.

problem Valuation of swing options with jumps under a mean-reverting model.
method Proposes second-order numerical methods to solve PIDEs convection-dominated and with nonlocal integral terms.
result Numerical methods confirm second-order convergence behavior.

Energy companies need efficient procedures to perform market calibration of stochastic models for commodities. If the Black framework is chosen for option pricing, the bottleneck of the market calibration is the computation of the variance of the asset. Energy commodities are commonly represented by multi-factor linear…

2018-09-11abs ↗pdf ↗

Recursive Marginal Quantization (RMQ) allows fast approximation of solutions to stochastic differential equations in one-dimension. When applied to two factor models, RMQ is inefficient due to the fact that the optimization problem is usually performed using stochastic methods, e.g., Lloyd's algorithm or Competitive Le…

2017-04-21abs ↗pdf ↗

A similarity structure on a connected manifold M is a Riemannian metric on its universal cover such that the fundamental group of M acts by similarities. If the manifold M is compact, we show that the universal cover admits a de Rham decomposition with at most two factors, one of which is Euclidean. Very recently, afte…

2015-07-20abs ↗pdf ↗

Novel method for estimating currency option parameters with improved accuracy.

problem Improving currency option pricing accuracy and calibration process.
method Develops approximate formulas for two parameters in stochastic volatility models with exponentially-affine characteristic functions.
result Superior accuracy in parameter estimation for currency options.

Pricing formulae for defaultable corporate bonds with discrete coupons under consideration of the government taxes in the united model of structural and reduced form models are provided. The aim of this paper is to generalize the comprehensive structural model for defaultable fixed income bonds (considered in [1]) into…

2013-09-06abs ↗pdf ↗

Investigates portfolio selection with transaction costs and stochastic volatility, using deep learning for computation.

problem Optimal portfolio selection with transaction costs and stochastic volatility.
method Two-factor stochastic volatility model, option-implied utility function, deep learning policy iteration.
result Deep learning method effectively computes optimal investment decisions under transaction costs and stochastic volatility.

The Schatten quasi-norm can be used to bridge the gap between the nuclear norm and rank function, and is the tighter approximation to matrix rank. However, most existing Schatten quasi-norm minimization (SQNM) algorithms, as well as for nuclear norm minimization, are too slow or even impractical for large-scale problem…

2016-06-02abs ↗pdf ↗

We study the existence of a unique stationary distribution and ergodicity for a 2-dimensional affine process. The first coordinate is supposed to be a so-called alpha-root process with α\in(1,2]. The existence of a unique stationary distribution for the affine process is proved in case of α\in(1,2]; further, in case of…

2013-02-11abs ↗pdf ↗

The problem of portfolio allocation in the context of stocks evolving in random environments, that is with volatility and returns depending on random factors, has attracted a lot of attention. The problem of maximizing a power utility at a terminal time with only one random factor can be linearized thanks to a classica…

2019-08-20abs ↗pdf ↗

A new method solves complex financial equations efficiently.

problem Solving worst-case and best-case prices for two-factor uncertain volatility models.
method Decompose and integrate, then optimize; piecewise constant control; closed-form Green's functions; 2D convolution integrals; monotone numerical integration; Fast Fourier Transforms.
result The method efficiently computes the value function and optimal control, converging to the viscosity solution of the HJB equation.

The paper proposes a framework for information-theoretic predictive uncertainty measures.

problem The need for reliable estimation of predictive uncertainty in machine learning.
method Revisiting core concepts, categorizing predictive uncertainty measures based on model and approximation of true distribution.
result Identification of conditions under which certain predictive uncertainty measures excel.

We extend the now classic structural credit modeling approach of Black and Cox to a class of "two-factor" models that unify equity securities such as options written on the stock price, and credit products like bonds and credit default swaps. In our approach, the two sides of the stylized balance sheet of a firm, namel…

2011-10-26abs ↗pdf ↗

New measure quantifies contrastive self-supervised learning's generalization ability.

problem Limited theoretical understanding of contrastive self-supervised learning's generalization.
method Defined (σ,δ)(σ,δ)-measure to mathematically quantify data augmentation and provide an upper bound for downstream classification error.
result Generalization ability is related to alignment of positive samples, divergence of class centers, and concentration of augmented data.

Personal income distributions in Japan are analyzed empirically and a simple stochastic model of the income process is proposed. Based on empirical facts, we propose a minimal two-factor model. Our model of personal income consists of an asset accumulation process and a wage process. We show that these simple processes…

2005-05-25abs ↗pdf ↗

To investigate the universal structure of interactions in financial dynamics, we analyze the cross-correlation matrix C of price returns of the Chinese stock market, in comparison with those of the American and Indian stock markets. As an important emerging market, the Chinese market exhibits much stronger correlations…

2012-02-02abs ↗pdf ↗

Deep weight factorization improves neural network training through smooth optimization of sparse penalties.

problem Challenges in applying sparse regularization in neural networks due to non-differentiability of penalties.
method Introduces deep weight factorization, decomposing weights into multiple factors for smooth optimization of L1L_1-penalized networks.
result Deep weight factorization outperforms shallow factorization and pruning methods consistently across various architectures and datasets.

Polynomial processes have the property that expectations of polynomial functions (of degree nn, say) of the future state of the process conditional on the current state are given by polynomials (of degree n\leq n) of the current state. Here we explore the application of polynomial processes in the context of structur…

2017-10-27abs ↗pdf ↗

New insights into how large learning rates affect transformer training dynamics.

problem Understanding how large learning rates impact the training of transformer models.
method Analyzing a simplified linear transformer model with a two-factor product map.
result Large learning rates can lead to various training outcomes including cycles, chaos, or divergence.

We show that essential punctured spheres in the complement of links with distance three bridge spheres have bounded complexity. We define the operation of tangle product, a generalization of both connected sum and Conway product. Finally, we use the bounded complexity of essential punctured spheres to show that the bri…

2011-08-29abs ↗pdf ↗

Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.

problem Explaining the difference in default dependence between monthly and annual aggregation.
method Dynamic low-rank state-space model with AR(1) latent credit-state factors.
result Effective correlation matrices and rank copulas are generated from monthly default count data.

Efficiently calibrates Bergomi models to VIX derivatives using vector quantization.

problem Calibrating Bergomi models to VIX derivatives for accurate pricing.
method Applied vector quantization in mixed Bergomi models for fast and efficient option pricing.
result Calibration of Bergomi models to VIX derivatives is feasible and accurate over daily data.

The scaling properties of the time series of asset prices and trading volumes of stock markets are analysed. It is shown that similarly to the asset prices, the trading volume data obey multi-scaling length-distribution of low-variability periods. In the case of asset prices, such scaling behaviour can be used for risk…

2005-01-13abs ↗pdf ↗

We compute the transition probability between two learning tasks, and show that it decomposes into two factors. The first depends on the geometry of the loss landscape of a model trained on each task, independent of any particular model used. This is related to an information theoretic distance function, but is insuffi…

2018-10-04abs ↗pdf ↗