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SUMMARY:Parametric Monte Carlo with Chebyshev Interpolation
DTSTART:20170626T120000
DTEND:20170626T130000
DTSTAMP:20260407T230432Z
UID:5bd728bc752c084149b9cbfa8ea06c142bd63e1c089fc4ab97a299b3
CATEGORIES:Conferences - Seminars
DESCRIPTION:Kathrin GLAU (Technische Universität München)\nWe propose to
  reduce the computational complexity of the evaluation of parametric expec
 tations by (tensorized Chebyshev interpolation). Fast and acchurate evalua
 tion of parametric expectations is a frequently occuring task in finance. 
 Recurrent tasks such as pricing\, calibration and risk assessment need to 
 be executed accurately and in real time. We concentrate on Parametric Opti
 on Pricing (POP) and show that polynomial interpolation in the parameter s
 pace promises to reduce run-times while maintaining accuracy. The attracti
 ve properties of Chebyshev interpolation and its tensorized extension enab
 le us to identify criteria for (sub)exponential convergence and explicit e
 rror bounds. We show that these results apply to a variety of European (ba
 sket) options and affine asset models. Numerical experiments confirm our f
 indings. Exploring the potential of the method further\, we empirically in
 vestigate the efficiency of the Chebyshev method combined with Monte-Carlo
  for multivariate and path-dependent options. For a wide and important ran
 ge of problems\, the Chebyshev method turns out to be more efficient than 
 parametric multilevel Monte-Carlo.
LOCATION:UNIL\, Extranef\, room 126 https://planete.unil.ch/plan/?local=EX
 T-126
STATUS:CONFIRMED
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