Effects of jump-diffusion models for the house price dynamics in the pricing of fixed-rate mortgages, insurance and coinsurance

[Abstract] In the pricing of fixed rate mortgages with prepayment and default options, we introduce jump-diffusion models for the house price evolution. These models take into account sudden changes in the price (jumps) during bubbles and crisis situations in real estate markets. After posing the models based on partial-integro differential equations (PIDE) problems for the contract, insurance and the fraction of the total loss not covered by the insurance (coinsurance), we propose appropriate numerical methods to solve them.This work has been partially funded by MINECO of Spain (Project MTM2013-47800-C2-1-P)

Pricing pension plans under jump–diffusion models for the salary

[Abstract] In this paper we consider the valuation of a defined benefit pension plan in the presence of jumps in the underlying salary and including the possibility of early retirement. We will consider that the salary follows a jump–diffusion model, thus giving rise to a partial integro-differential equation (PIDE). After posing the model, we propose the appropriate numerical methods to solve the PIDE problem. These methods mainly consists of Lagrange–Galerkin discretizations combined with augmented Lagrangian active set techniques and with the explicit treatment of the integral term. Finally, we compare the numerical results with those ones obtained with Monte Carlo techniques.This paper has been partially funded by MCINN (Project MTM2010-21135-C02-01 and MTM2013-47800-C2-1-P) and by Xunta de Galicia (Ayuda GRC2014/044, partially funded with FEDER funds).Xunta de Galicia; GRC2014/04

Exponential Time Integration and Second-Order Difference Scheme for a Generalized Black-Scholes Equation

We apply an exponential time integration scheme combined with a central difference scheme on a piecewise uniform mesh with respect to the spatial variable to evaluate a generalized Black-Scholes equation. We show that the scheme is second-order convergent for both time and spatial variables. It is proved that the scheme is unconditionally stable. Numerical results support the theoretical results

Collocation method based on modified ‎cubic‎ B-spline ‎for option pricing ‎models

Collocation‎‎ ‎method ‎based ‎on ‎modified‎ cubic B-spline functions ‎has ‎been ‎developed‎ ‎for ‎the ‎valuation ‎‎‎of European‎, ‎American and Barrier options of single ‎asset. ‎The ‎new ‎approach ‎contains ‎‎discretizing ‎of‎ t‎‎emporal ‎derivative‎ ‎using ‎finite ‎difference ‎approximations ‎and ‎approximating‎ the option price with the ‎modified‎ B-spline functions‎. ‎Stability of this method has been discussed and shown that it is unconditionally stable‎. ‎The ‎efficiency ‎of ‎the‎ ‎proposed ‎method ‎is ‎tested ‎by ‎different ‎examples‎‎‎.

A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options

In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm, which learns the optimal stopping rule from Monte-Carlo samples of the underlying risk factors. Cashflow-paths are then created by applying the learned stopping strategy on a new set of realizations of the risk factors. Furthermore, in a second phase the risk factors are regressed against the cashflow-paths to obtain approximations of pathwise option values. The regression step is carried out by ordinary least squares as well as neural networks, and it is shown that the latter produces more accurate approximations. The expected exposure is formulated, both in terms of the cashflow-paths and in terms of the pathwise option values and it is shown that a simple Monte-Carlo average yields accurate approximations in both cases. The potential future exposure is estimated by the empirical $\alpha$-percentile. Finally, it is shown that the expected exposures, as well as the potential future exposures can be computed under either, the risk neutral measure, or the real world measure, without having to re-train the neural networks