On the variance and skewness of the swap rate in a stochastic volatility interest rate model

2021 ◽  
Vol 55 (2) ◽  
pp. 109-123
Author(s):  
Lars Palapies
Keyword(s):  
Interest Rate ◽  
Stochastic Volatility ◽  
Rate Model ◽  
Interest Rate Model

Dual-curve Hull–White interest rate model with stochastic volatility

2017 ◽  
Vol 34 (3) ◽  
pp. 711-745
Author(s):  
Mei Choi Chiu ◽  
Wanyang Liang ◽  
Hoi Ying Wong
Keyword(s):  
Interest Rate ◽  
Stochastic Volatility ◽  
Rate Model ◽  
Interest Rate Model ◽  
Dual Curve

Pricing of Guaranteed Annuity Options in a Stochastic Volatility and Interest Rate Environment

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Keisuke Kizaki ◽  
Yoshifumi Muroi
Keyword(s):  
Interest Rate ◽  
Stochastic Volatility ◽  
Rate Model ◽  
Rate Environment ◽  
Interest Rate Model

AbstractThis article examines the pricing of guaranteed annuity options (GAOs) in a stochastic volatility and interest rate model. While the pricing of these options in a stochastic volatility and interest rate model has been examined in

On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters

2018 ◽  
Vol 24 (4) ◽  
pp. 309-321 ◽  
Author(s):  
Harold A. Lay ◽  
Zane Colgin ◽  
Viktor Reshniak ◽  
Abdul Q. M. Khaliq
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Interest Rate ◽  
Stochastic Volatility ◽  
Stochastic Systems ◽  
Multilevel Monte Carlo ◽  
Rate Model ◽  
Gpu Clusters ◽  
Interest Rate Model ◽  
Gpu Implementation

Abstract We explore different methods of solving systems of stochastic differential equations by first implementing the Euler–Maruyama and Milstein methods with a Monte Carlo simulation on a CPU. The performance of the methods is significantly improved through the recently developed antithetic multilevel Monte Carlo estimator, which yields a computation complexity of {\mathcal{O}(\epsilon^{-2})} root-mean-square error and does so without the approximation of Lévy areas. Further improvements in performance are gained by moving the algorithms to a GPU - first on a single device and then on a multi-GPU cluster. Our GPU implementation of the antithetic multilevel Monte Carlo displays a major speedup in computation when compared with many commonly used approaches in the literature. While our work is focused on the simulation of the stochastic volatility and interest rate model, it is easily extendable to other stochastic systems, and it is of particular interest to those with non-diagonal, non-commutative noise.

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