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Related papers: Kriging of financial term-structures

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The shape of the futures term structure is essential to commodity hedgers and speculators as futures prices serve as a forecast of future spot prices. Commodity markets quotes futures prices on a selection of maturities and delivery…

Pricing of Securities · Quantitative Finance 2022-03-30 Andrea Maran , Andrea Pallavicini

In this paper, we analyze the diversity of term structure functions (e.g., yield curves, swap curves, credit curves) constructed in a process which complies with some admissible properties: arbitrage-freeness, ability to fit market quotes…

Computational Finance · Quantitative Finance 2014-04-02 Areski Cousin , Ibrahima Niang

The market practice of extrapolating different term structures from different instruments lacks a rigorous justification in terms of cash flows structure and market observables. In this paper, we integrate our previous consistent theory for…

Pricing of Securities · Quantitative Finance 2013-04-05 Andrea Pallavicini , Damiano Brigo

We provide a new kriging procedure of processes on graphs. Based on the construction of Gaussian random processes indexed by graphs, we extend to this framework the usual linear prediction method for spatial random fields, known as kriging.…

Statistics Theory · Mathematics 2014-06-26 Thibault Espinasse , Jean-Michel Loubes

Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict…

Statistics Theory · Mathematics 2019-03-20 Wenjia Wang , Rui Tuo , C. F. Jeff Wu

Seismic fragility curves have been introduced as key components of Seismic Probabilistic Risk Assessment studies. They express the probability of failure of mechanical structures conditional to a seismic intensity measure and must take into…

Applications · Statistics 2022-10-13 Clement Gauchy , Cyril Feau , Josselin Garnier

Kriging and Gaussian Process Regression are statistical methods that allow predicting the outcome of a random process or a random field by using a sample of correlated observations. In other words, the random process or random field is…

Methodology · Statistics 2025-10-14 Marius Marinescu

The use of weather index insurances is subject to spatial basis risk, which arises from the fact that the location of the user's risk exposure is not the same as the location of any of the weather stations where an index can be measured. To…

Applications · Statistics 2024-08-01 Yiping Guo , Johnny Siu-Hang Li

This paper introduces a novel stochastic model for credit spreads. The stochastic approach leverages the diffusion of default intensities via a CIR++ model and is formulated within a risk-neutral probability space. Our research primarily…

Risk Management · Quantitative Finance 2026-01-09 Mohamed Ben Alaya , Ahmed Kebaier , Djibril Sarr

Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate…

Methodology · Statistics 2011-05-10 V. Dubourg , F. Deheeger , B. Sudret

We give a comprehensive review of credit term structure modeling methodologies. The conventional approach to modeling credit term structure is summarized and shown to be equivalent to a particular type of the reduced form credit risk model,…

Pricing of Securities · Quantitative Finance 2009-12-29 Arthur M. Berd

Many geosciences data are imprecise due to various limitations and uncertainties in the measuring process. One way to preserve this imprecision in a geostatistical mapping framework is to characterize the measurements as intervals rather…

Methodology · Statistics 2019-11-22 Brennan Bean , Yan Sun , Marc Maguire

Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models -- such as Ordinary Kriging (OK) -- assume spatial stationarity, which makes it difficult to capture the…

Physics and Society · Physics 2025-07-11 Peng Luo , Yilong Wu , Yongze Song

Fixed income markets share many features with the equity markets. However there are significant differences as well and many attempts have been done in the past to develop specific tools which describe (and possibly forecasts) the behavior…

Condensed Matter · Physics 2007-05-23 Livio Marangio , Alessandro Ramponi , Massimo Bernaschi

Functional Ordinary Kriging is the most widely used method to predict a curve at a given spatial point. However, uncertainty remains an open issue. In this article a distribution-free prediction method based on two different modulation…

Methodology · Statistics 2024-10-01 Anna De Magistris , Andrea Diana , Elvira Romano

Kriging is a widely recognized method for making spatial predictions. On the sphere, popular methods such as ordinary kriging assume that the spatial process is intrinsically homogeneous. However, intrinsic homogeneity is too strict in many…

Methodology · Statistics 2021-07-08 Nicholas W. Bussberg , Jacob Shields , Chunfeng Huang

In simulation, Median Polish Kriging is a technique used to predict unobserved data points in two-dimensional space. The linear behavior of the traditional Median Polish Kriging in the estimation of the mean function in a high grid makes…

Other Computer Science · Computer Science 2013-08-01 Firas Al Rekabi , Asim El Sheikh

This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often…

Machine Learning · Statistics 2017-07-26 Didier Rullière , Nicolas Durrande , François Bachoc , Clément Chevalier

Much research in systemic risk is focused on default contagion. While this demands an understanding of valuation, fewer articles specifically deal with the existence, the uniqueness, and the computation of equilibrium prices in structural…

Computational Finance · Quantitative Finance 2015-01-30 Johannes Hain , Tom Fischer

A crucial step in fitting a regression model to data is determining the model's structure, i.e., the subset of explanatory variables to be included. However, the uncertainty in this step is often overlooked due to a lack of satisfactory…

Methodology · Statistics 2025-11-13 Ryan Martin , Naomi Singer , Jonathan Williams
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