Uncertainty Quantification of Derivative Instruments

被引:1
|
作者
Sun, Xianming [1 ,2 ]
Vanmaele, Michele [2 ]
机构
[1] Zhongnan Univ Econ & Law, Sch Finance, Wuhan 430073, Peoples R China
[2] Univ Ghent, Dept Appl Math Comp Sci & Stat, B-9000 Ghent, Belgium
关键词
Parameter uncertainty; Derivative pricing; Smolyak algorithm; Monte Carlo; Entropy; INTERPOLATION; BARRIER; OPTIONS; MODEL; COLLOCATION; VOLATILITY; VALUATION; BERMUDAN; PRICES;
D O I
10.4208/eajam.100316.270117a
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Model and parameter uncertainties are common whenever some parametric model is selected to value a derivative instrument. Combining the Monte Carlo method with the Smolyak interpolation algorithm, we propose an accurate efficient numerical procedure to quantify the uncertainty embedded in complex derivatives. Except for the value function being sufficiently smooth with respect to the model parameters, there are no requirements on the payoff or candidate models. Numerical tests carried out quantify the uncertainty of Bermudan put options and down-and-out put options under the Heston model, with each model parameter specified in an interval.
引用
收藏
页码:343 / 362
页数:20
相关论文
共 50 条
  • [1] Model uncertainty and its impact on the pricing of derivative instruments
    Cont, Rama
    [J]. MATHEMATICAL FINANCE, 2006, 16 (03) : 519 - 547
  • [2] HIGH DIMENSIONAL UNCERTAINTY QUANTIFICATION USING THE DERIVATIVE APPROACH
    Kubicek, M.
    Minisci, E.
    [J]. 11TH WORLD CONGRESS ON COMPUTATIONAL MECHANICS; 5TH EUROPEAN CONFERENCE ON COMPUTATIONAL MECHANICS; 6TH EUROPEAN CONFERENCE ON COMPUTATIONAL FLUID DYNAMICS, VOLS V - VI, 2014, : 6446 - 6457
  • [3] Uncertainty Quantification on Flutter Derivative Identification and Flutter Analysis of Bridges
    Feng, Zhou-Quan
    Lin, Yang
    Hua, Xu-Gang
    Chen, Zheng-Qing
    [J]. Zhongguo Gonglu Xuebao/China Journal of Highway and Transport, 2023, 36 (08): : 14 - 21
  • [4] ORTHOGONAL BASES FOR POLYNOMIAL REGRESSION WITH DERIVATIVE INFORMATION IN UNCERTAINTY QUANTIFICATION
    Li, Yiou
    Anitescu, Mihai
    Roderick, Oleg
    Hickernell, Fred
    [J]. INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2011, 1 (04) : 297 - 320
  • [5] Derivative-Enhanced Rational Polynomial Chaos for Uncertainty Quantification
    Sidhu, Karanvir S.
    Khazaka, Roni
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2024, 71 (04) : 1832 - 1841
  • [6] Polynomial Regression Approaches Using Derivative Information for Uncertainty Quantification
    Roderick, Oleg
    Anitescu, Mihai
    Fischer, Paul
    [J]. NUCLEAR SCIENCE AND ENGINEERING, 2010, 164 (02) : 122 - 139
  • [7] Supervision of derivative instruments
    Jordan, JL
    [J]. JOURNAL OF FINANCIAL SERVICES RESEARCH, 1995, 9 (3-4) : 433 - 444
  • [8] Broadband Uncertainty Quantification with the FDTD Method and the Multi-Complex Step Derivative Approximation
    Liu, Kae-An
    Sarris, Costas D.
    [J]. 2017 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), 2017, : 1642 - 1645
  • [9] Uncertainty quantification
    Leitch, Matthew
    [J]. JOURNAL OF RISK FINANCE, 2005, 6 (01)
  • [10] DEBRIS RE-ENTRY MODELING USING HIGH DIMENSIONAL DERIVATIVE BASED UNCERTAINTY QUANTIFICATION
    Mehta, Piyush M.
    Kubicek, Martin
    Minisci, Edmondo
    Vasile, Massimiliano
    [J]. ASTRODYNAMICS 2015, 2016, 156 : 3993 - 4011