P. Abrahamsen, A review of Gaussian random fields and correlation functions, Norsk Regnesentral, 1997.

P. Abrahamsen and F. E. Benth, Kriging with inequality constraints, Mathematical Geology, vol.33, issue.6, pp.719-744, 2001.
DOI : 10.1023/A:1011078716252

F. Ametrano and M. Bianchetti, Bootstrapping the illiquidity: Multiple yield curves construction for market coherent forward rates estimation, chapter 1, Risk Books, 2009.

L. Andersen, Discount curve construction with tension splines, Review of Derivatives Research, vol.9, issue.3, pp.227-267, 2007.
DOI : 10.1007/s11147-008-9021-2

H. Asgharian, W. Hess, and L. Liu, A spatial analysis of international stock market linkages, Journal of Banking & Finance, vol.37, issue.12, pp.4738-4754, 2013.
DOI : 10.1016/j.jbankfin.2013.08.015

F. Bachoc, Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification, Computational Statistics & Data Analysis, vol.66, issue.0, pp.6655-69, 2013.
DOI : 10.1016/j.csda.2013.03.016

L. Barzanti and C. Corradi, A note on direct term structure estimation using monotonic splines. Rivista di matematica per le scienze economiche e sociali, pp.101-108, 1999.

X. Bay, L. Grammont, and H. Maatouk, A new method for interpolating in a convex subset of a Hilbert space, Computational Optimization and Applications, vol.142, issue.1, p.1136466, 2015.
DOI : 10.1007/s10589-017-9906-9

URL : https://hal.archives-ouvertes.fr/hal-01136466

X. Bay, L. Grammont, and H. Maatouk, Generalization of the Kimeldorf-Wahba correspondence for constrained interpolation, Electronic Journal of Statistics, vol.10, issue.1, 2016.
DOI : 10.1214/16-EJS1149

URL : https://hal.archives-ouvertes.fr/hal-01270237

R. E. Baysal, B. L. Nelson, and J. Staum, Response surface methodology for simulating hedging and trading strategies, 2008 Winter Simulation Conference, pp.629-637, 2008.
DOI : 10.1109/WSC.2008.4736123

F. E. Benth, Kriging smooth futures curves, Energy Risk, pp.64-69, 2015.

N. Branger and C. Schlag, Model Risk: A Conceptual Framework for Risk Measurement and Hedging, EFMA 2004 Basel Meetings Paper, 2004.
DOI : 10.2139/ssrn.493482

C. Forum and C. Forum, QIS 5 Technical Specification; Risk-free interest rates, CFO Forum and CRO Forum, 2010.

M. Chibane, J. Selvaraj, and G. Sheldon, Building Curves on a Good Basis, SSRN Electronic Journal, 2009.
DOI : 10.2139/ssrn.1394267

N. Chiu, S. Fang, J. E. Lavery, J. Lin, W. et al., Approximating term structure of interest rates using cubic L1 splines, European Journal of Operational Research, vol.184, issue.3, pp.990-1004, 2008.
DOI : 10.1016/j.ejor.2006.12.008

R. Cont, MODEL UNCERTAINTY AND ITS IMPACT ON THE PRICING OF DERIVATIVE INSTRUMENTS, Mathematical Finance, vol.1856, issue.1, pp.519-547, 2006.
DOI : 10.1287/moor.1040.0138

URL : https://hal.archives-ouvertes.fr/halshs-00002695

N. Cressie, The origins of kriging, Mathematical Geology, vol.2, issue.3, pp.239-252, 1990.
DOI : 10.1007/BF00889887

N. A. Cressie, Statistics for Spatial Data, Revised Edition., Biometrics, vol.50, issue.1, 1993.
DOI : 10.2307/2533238

M. H. Davis and D. G. Hobson, THE RANGE OF TRADED OPTION PRICES, Mathematical Finance, vol.36, issue.1, pp.1-14, 2007.
DOI : 10.1073/pnas.37.12.826

J. De-andrés-sánchez and A. T. Gómez, Estimating a fuzzy term structure of interest rates using fuzzy regression techniques, European Journal of Operational Research, vol.154, issue.3, pp.804-818, 2004.
DOI : 10.1016/S0377-2217(02)00854-8

A. Debón, F. Martínez-ruiz, and F. Montes, A geostatistical approach for dynamic life tables: The effect of mortality on remaining lifetime and annuities, Insurance: Mathematics and Economics, vol.47, issue.3, pp.47327-336, 2010.
DOI : 10.1016/j.insmatheco.2010.07.007

E. Derman, Model Risk, Quantitative Strategies Research Notes, 1996.

E. Eberlein and J. Jacod, On the range of options prices, Finance and Stochastics, vol.1, issue.2, pp.131-140, 1997.
DOI : 10.1007/s007800050019

E. Karoui, N. Jeanblanc-picquè, M. Shreve, and S. E. , Robustness of the Black and Scholes Formula, Mathematical Finance, vol.8, issue.2, pp.93-126, 1998.
DOI : 10.1111/1467-9965.00047

M. R. Fengler and L. Hin, A simple and general approach to fitting the discount curve under no-arbitrage constraints, Finance Research Letters, vol.15, pp.78-84, 2015.
DOI : 10.1016/j.frl.2015.08.006

G. B. Fernandes and R. Artes, Spatial dependence in credit risk and its improvement in credit scoring, European Journal of Operational Research, vol.249, issue.2, 2015.
DOI : 10.1016/j.ejor.2015.07.013

C. P. Fries, Curves and term structure models: Definition, calibration and application of rate curves and term structure models, 2013.

M. Fujii, Y. Shimada, and A. Takahashi, Collateral posting and choice of collateral currency, 2010.

G. Gan, S. Lin, and X. , Valuation of large variable annuity portfolios under nested simulation: A functional data approach, Insurance: Mathematics and Economics, vol.62, issue.0, pp.138-150, 2015.
DOI : 10.1016/j.insmatheco.2015.02.007

M. Gilli, S. Große, and E. Schumann, Calibrating the Nelson-Siegel-Svensson model. COMISEF working papers series, 2010.

S. Golchi, D. R. Bingham, H. Chipman, and D. A. Campbell, Monotone Emulation of Computer Experiments, SIAM/ASA Journal on Uncertainty Quantification, vol.3, issue.1, pp.370-392, 2015.
DOI : 10.1137/140976741

T. C. Green and S. Figlewski, Market Risk and Model Risk for a Financial Institution Writing Options, The Journal of Finance, vol.55, issue.4, pp.1465-1499, 1999.
DOI : 10.1111/0022-1082.00152

G. Guojun, Application of data clustering and machine learning in variable annuity valuation, Insurance: Mathematics and Economics, vol.53, issue.3, pp.795-801, 2013.

P. S. Hagan and G. West, Interpolation Methods for Curve Construction, Applied Mathematical Finance, vol.15, issue.1, pp.89-129, 2006.
DOI : 10.1080/13504860500396032

P. Henaff, A Normalized Measure of Model Risk, SSRN Electronic Journal, 2010.
DOI : 10.2139/ssrn.1613024

J. Hull and A. White, Libor vs. OIS: The Derivatives Discounting Dilemma, SSRN Electronic Journal, vol.11, issue.3, pp.14-27, 2013.
DOI : 10.2139/ssrn.2211800

D. R. Jones, M. Schonlau, W. , and W. , Efficient Global Optimization of Expensive Black-Box Functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.
DOI : 10.1023/A:1008306431147

M. Kanevski, M. Maignan, A. Pozdnoukhov, and V. Timonin, Interest rates mapping. Physica A: Statistical Mechanics and its Applications, pp.3897-3903, 2008.

C. Kenyon and R. Stamm, Discounting, Libor, CVA and Funding: Interest Rate and Credit Pricing, 2012.
DOI : 10.1057/9781137268525

J. Kerkhof and B. Melenberg, Backtesting for risk-based regulatory capital, Journal of Banking & Finance, vol.28, issue.8, pp.1845-1865, 2004.
DOI : 10.1016/j.jbankfin.2003.06.007

G. S. Kimeldorf and G. Wahba, A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines, The Annals of Mathematical Statistics, vol.41, issue.2, pp.495-502, 1970.
DOI : 10.1214/aoms/1177697089

J. P. Kleijnen and W. C. Van-beers, Monotonicity-preserving bootstrapped Kriging metamodels for expensive simulations, Journal of the Operational Research Society, vol.54, issue.3, pp.708-717, 2012.
DOI : 10.1057/palgrave.jors.2601492

D. Krige, A statistical approach to some mine valuation and allied problems on the Witwatersrand: By DG Krige, 1951.

M. P. Laurini and M. Moura, Constrained smoothing -splines for the term structure of interest rates, Insurance: Mathematics and Economics, vol.46, issue.2, pp.339-350, 2010.
DOI : 10.1016/j.insmatheco.2009.11.008

M. P. Laurini and A. Ohashi, A noisy principal component analysis for forward rate curves, European Journal of Operational Research, vol.246, issue.1, pp.140-153, 2015.
DOI : 10.1016/j.ejor.2015.04.038

M. Liu and J. Staum, Stochastic kriging for efficient nested simulation of expected shortfall, The Journal of Risk, vol.12, issue.3, p.3, 2010.
DOI : 10.21314/JOR.2010.211

M. Ludkovski, Kriging metamodels for Bermudan option pricing, 2015.

H. Maatouk and X. Bay, A New Rejection Sampling Method for Truncated Multivariate Gaussian Random Variables Restricted to Convex Sets. To appear in Monte Carlo and Quasi-Monte Carlo Methods 2014, 2014.
URL : https://hal.archives-ouvertes.fr/emse-01339361

H. Maatouk and X. Bay, Gaussian Process Emulators for Computer Experiments with Inequality Constraints, Mathematical Geosciences, vol.93, issue.5, 2014.
DOI : 10.1007/s11004-017-9673-2

URL : https://hal.archives-ouvertes.fr/hal-01096751

H. Maatouk, O. Roustant, R. , and Y. , Cross-Validation Estimations of Hyper-Parameters of Gaussian Processes with Inequality Constraints, Procedia Environmental Sciences, vol.27, pp.38-44, 2015.
DOI : 10.1016/j.proenv.2015.07.105

URL : https://hal.archives-ouvertes.fr/emse-01185753

K. V. Mardia, J. T. Kent, C. R. Goodall, and J. A. And-little, Kriging and splines with derivative information, Biometrika, vol.83, issue.1, pp.207-221, 1996.
DOI : 10.1093/biomet/83.1.207

G. Matheron, Principles of geostatistics, Economic Geology, vol.58, issue.8, pp.1246-1266, 1963.
DOI : 10.2113/gsecongeo.58.8.1246

M. Morini, Understanding and Managing Model Risk: A practical guide for quants, traders and validators, 2011.
DOI : 10.1002/9781118467312

C. R. Nelson and A. F. Siegel, Parsimonious Modeling of Yield Curves, The Journal of Business, vol.60, issue.4, pp.473-489, 1987.
DOI : 10.1086/296409

A. Pallavicini and M. Tarenghi, Interest-Rate Modeling with Multiple Yield Curves, SSRN Electronic Journal, 2010.
DOI : 10.2139/ssrn.1629688

N. D. Paulson and C. E. Hart, A spatial approach to addressing weather derivative basis risk: A drought insurance example, 2006 Annual Meeting of American Agricultural Economics Association, 2006.

A. Ramponi, ADAPTIVE AND MONOTONE SPLINE ESTIMATION OF THE CROSS-SECTIONAL TERM STRUCTURE, International Journal of Theoretical and Applied Finance, vol.06, issue.02, pp.195-212, 2003.
DOI : 10.1142/S0219024903001840

C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), 2005.

C. P. Robert, Simulation of truncated normal variables, Statistics and Computing, vol.82, issue.2, 1995.
DOI : 10.1007/BF00143942

URL : https://hal.archives-ouvertes.fr/hal-00431310

O. Roustant, D. Ginsbourger, and Y. Deville, Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization, Journal of Statistical Software, vol.51, issue.1, pp.1-55, 2012.
DOI : 10.18637/jss.v051.i01

URL : https://hal.archives-ouvertes.fr/hal-00495766

T. J. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

A. Smith and T. Wilson, Fitting yield curves with long term constraints, 2001.

J. B. Sousa, M. L. Esquível, and R. M. Gaspar, Machine learning Vasicek model calibration with Gaussian processes, Communications in Statistics-Simulation and Computation, issue.6, pp.41776-786, 2012.

J. M. Steeley, Testing Term Structure Estimation Methods: Evidence from the UK STRIPS Market, Journal of Money, Credit and Banking, vol.8, issue.4, pp.1489-1512, 2008.
DOI : 10.1111/j.1538-4616.2008.00168.x

E. Stutvoet, Fitting Financial Models to Market Data Using Kriging, 2007.

L. E. Svensson, Estimating and interpreting forward interest rates: Sweden 1992-1994, National Bureau of Economic Research, 1994.