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Related papers: Consistent Recalibration of Yield Curve Models

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Nelson and Siegel curves are widely used to fit the observed term structure of interest rates in a particular date. By the other hand, several interest rate models have been developed such their initial forward rate curve can be adjusted to…

Mathematical Finance · Quantitative Finance 2017-07-11 Patricia Kisbye , Karem Meier

AutoRegressive Conditional Heteroscedasticity (ARCH) models are standard for modeling time series exhibiting volatility, with a rich literature in univariate and multivariate settings. In recent years, these models have been extended to…

Methodology · Statistics 2026-03-19 Alexander Aue , Sebastian Kühnert , Gregory Rice , Jeremy VanderDoes

We provide a lean, non-technical exposition on the pricing of path-dependent and European-style derivatives in the Cox-Ross-Rubinstein (CRR) pricing model. The main tool used in the paper for cleaning up the reasoning is applying static…

Mathematical Finance · Quantitative Finance 2018-03-02 Jarno Talponen , Minna Turunen

We construct a higher-order gravity model including all corrections up to mass dimension six. Starting from the Jordan frame, we derive the field equations and specialize to the FLRW background, where the dynamics take the form of a…

General Relativity and Quantum Cosmology · Physics 2026-01-13 C. M. G. R. Morais , G. Rodrigues-da-Silva , L. G. Medeiros

Methods for forecasting time series adhering to linear constraints have seen notable development in recent years, especially with the advent of forecast reconciliation. This paper extends forecast reconciliation to the open question of…

Methodology · Statistics 2025-10-27 Daniele Girolimetto , Anastasios Panagiotelis , Tommaso Di Fonzo , Han Li

Traditional gradient-based sampling methods, like standard Hamiltonian Monte Carlo, require that the desired target distribution is continuous and differentiable. This limits the types of models one can define, although the presented models…

Computation · Statistics 2025-04-28 Jimmy Huy Tran , Tore Selland Kleppe

We consider a short rate model, driven by a stochastic process on the cone of positive semidefinite matrices. We derive sufficient conditions ensuring that the model replicates normal, inverse or humped yield curves.

Pricing of Securities · Quantitative Finance 2014-05-08 Alessandro Gnoatto

Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent research efforts, on the other hand, have focused on optimizing…

Machine Learning · Computer Science 2025-02-11 Sima Noorani , Orlando Romero , Nicolo Dal Fabbro , Hamed Hassani , George J. Pappas

Conformal risk control (CRC) provides distribution-free guarantees for controlling the expected loss at a user-specified level. Existing theory typically assumes that the loss decreases monotonically with a tuning parameter that governs the…

Machine Learning · Statistics 2026-04-21 Tareq Aldirawi , Yun Li , Wenge Guo

We consider Hamiltonians associated to optimal control problems for affine systems on the torus. They are not coercive and are possibly unbounded from below in the direction of the drift of the system. The main assumption is the strong…

Optimization and Control · Mathematics 2024-01-18 Martino Bardi

In performative Reinforcement Learning (RL), an agent faces a policy-dependent environment: the reward and transition functions depend on the agent's policy. Prior work on performative RL has studied the convergence of repeated retraining…

Machine Learning · Computer Science 2025-05-12 Vasilis Pollatos , Debmalya Mandal , Goran Radanovic

Tiny Recursive Models (TRMs) have recently demonstrated remarkable performance on ARC-AGI, showing that very small models can compete against large foundation models through a two-step refinement mechanism that updates an internal reasoning…

Machine Learning · Computer Science 2026-03-10 Paulius Rauba , Claudio Fanconi , Mihaela van der Schaar

The paper studies conical, convex, and affine models in the framework of behavioral systems theory. We investigate basic properties of such behaviors and address the problem of constructing models from measured data. We prove that closed,…

Optimization and Control · Mathematics 2023-10-25 Alberto Padoan , Florian Dörfler , John Lygeros

Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning. HMC is known to run very efficiently in practice and its popular second-order "leapfrog"…

Data Structures and Algorithms · Computer Science 2018-08-13 Oren Mangoubi , Nisheeth K. Vishnoi

Nonlinear hyperbolic partial differential equations govern continuum traffic flow models. Higher-order traffic flow models consisting of continuum equations and velocity dynamics were introduced to address the limitations of the Lighthill,…

Analysis of PDEs · Mathematics 2024-07-10 Nandan Maiti , Bhargava Rama Chilukuri

Cardiovascular outcome trials commonly face competing risks when non-CV death prevents observation of major adverse cardiovascular events (MACE). While Cox proportional hazards models treat competing events as independent censoring,…

Methodology · Statistics 2026-02-19 Tuo Wang , Yu Du

This work introduces a formulation of model predictive control (MPC) which adaptively reasons about the complexity of the model based on the task while maintaining feasibility and stability guarantees. Existing MPC implementations often…

Robotics · Computer Science 2024-11-07 Joseph Norby , Ardalan Tajbakhsh , Yanhao Yang , Aaron M. Johnson

Slow roll reconstruction is derived from the Hamilton-Jacobi formulation of inflationary dynamics. It automatically includes information from sub-leading terms in slow roll, and facilitatesthe inclusion of priors based on the duration on…

Astrophysics · Physics 2009-06-23 Peter Adshead , Richard Easther

We consider the problem of sparsity-constrained $M$-estimation when both explanatory and response variables have heavy tails (bounded 4-th moments), or a fraction of arbitrary corruptions. We focus on the $k$-sparse, high-dimensional regime…

Machine Learning · Computer Science 2019-05-31 Liu Liu , Tianyang Li , Constantine Caramanis

The correlated stochastic volatility models constitute a natural extension of the Black and Scholes-Merton framework: here the volatility is not a constant, but a stochastic process correlated with the price log-return one. At present,…

Statistical Finance · Quantitative Finance 2008-12-02 E. Cisana , L. Fermi , G. Montagna , O. Nicrosini
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