scholarly journals A Multi-Index Quasi--Monte Carlo Algorithm for Lognormal Diffusion Problems

2017 ◽  
Vol 39 (5) ◽  
pp. S851-S872 ◽  
Author(s):  
Pieterjan Robbe ◽  
Dirk Nuyens ◽  
Stefan Vandewalle
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Algorithm ◽  
Multi Index ◽  
Quasi Monte Carlo ◽  
Diffusion Problems

scholarly journals Multilevel Quasi-Monte Carlo methods for lognormal diffusion problems

2017 ◽  
Vol 86 (308) ◽  
pp. 2827-2860 ◽  
Author(s):  
Frances Y. Kuo ◽  
Robert Scheichl ◽  
Christoph Schwab ◽  
Ian H. Sloan ◽  
Elisabeth Ullmann
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Quasi Monte Carlo ◽  
Diffusion Problems

scholarly journals A QUASI-MONTE CARLO ALGORITHM FOR THE NORMAL INVERSE GAUSSIAN DISTRIBUTION AND VALUATION OF FINANCIAL DERIVATIVES

2006 ◽  
Vol 09 (06) ◽  
pp. 843-867 ◽  
Author(s):  
FRED ESPEN BENTH ◽  
MARTIN GROTH ◽  
PAUL C. KETTLER
Keyword(s):  
Monte Carlo ◽  
Value At Risk ◽  
Scale Parameter ◽  
Monte Carlo Algorithm ◽  
Asian Options ◽  
Inverse Gaussian ◽  
Option Prices ◽  
Quasi Monte Carlo ◽  
Normal Inverse Gaussian Distribution ◽  
Normal Inverse Gaussian

We propose a quasi-Monte Carlo (qMC) algorithm to simulate variates from the normal inverse Gaussian (NIG) distribution. The algorithm is based on a Monte Carlo technique found in Rydberg [13], and is based on sampling three independent uniform variables. We apply the algorithm to three problems appearing in finance. First, we consider the valuation of plain vanilla call options and Asian options. The next application considers the problem of deriving implied parameters for the underlying asset dynamics based on observed option prices. We employ our proposed algorithm together with the Newton Method, and show how we can find the scale parameter of the NIG-distribution of the logreturns in case of a call or an Asian option. We also provide an extensive error analysis for this method. Finally we study the calculation of Value-at-Risk for a portfolio of nonlinear products where the returns are modeled by NIG random variables.

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