Désiré Yannick Tangman

High-order computational methods for option valuation under multifactor models

Many of the different numerical techniques in the partial differential equations framework for solving option pricing problems have employed only standard second-order discretization schemes. A higher-order discretization has the advantage of producing low size matrix systems for computing sufficiently accurate option prices and this paper proposes new computational schemes yielding high-order convergence rates for the solution of multi-factor option problems. These new schemes employ Galerkin finite element discretizations with quadratic basis functions for the approximation of the spatial derivatives in the pricing equations for stochastic volatility and two-asset option problems and time integration of the resulting semi-discrete systems requires the computation of a single matrix exponential. The computations indicate that this combination of high-order finite elements and exponential time integration leads to efficient algorithms for multi-factor problems. Highly accurate European prices are obtained with relatively coarse meshes and high-order convergence rates are also observed for options with the American early exercise feature. Various numerical examples are provided for illustrating the accuracy of the option prices for Heston’s and Bates stochastic volatility models and for two-asset problems under Merton’s jump-diffusion model.

A new radial basis functions method for pricing American options under Merton's jump-diffusion model

A new radial basis functions (RBFs) algorithm for pricing financial options under Mertons jump-diffusion model is described. The method is based on a differential quadrature approach, that allows the implementation of the boundary conditions in an efficient way. The semi-discrete equations obtained after approximation of the spatial derivatives, using RBFs based on differential quadrature are solved, using an exponential time integration scheme and we provide several numerical tests which show the superiority of this method over the popular Crank–Nicolson method. Various numerical results for the pricing of European, American and barrier options are given to illustrate the efficiency and accuracy of this new algorithm. We also show that the option Greeks such as the Delta and Gamma sensitivity measures are efficiently computed to high accuracy.

A new fourth-order numerical scheme for option pricing under the CEV model

Abstract The empirically observed negative relationship between a stock price and its return volatility can be captured by the constant elasticity of variance option pricing model. For European options, closed form expressions involve the non-central chi-square distribution whose computation can be slow when the elasticity factor is close to one, volatility is low or time to maturity is small. We present a fast numerical scheme based on a high-order compact discretisation which accurately computes the option price. Various numerical examples indicate that for comparable computational times, the option price computed with the scheme has higher accuracy than the Crank–Nicolson numerical solution. The scheme accurately computes the hedging parameters and is stable for strongly negative values of the elasticity factor.

Efficient and high accuracy pricing of barrier options under the CEV diffusion

Binomial and trinomial lattices are popular techniques for pricing financial options. These methods work well for European and American options, but for barrier options, the need to place a tree node very close to a barrier brings difficulties in their implementation and a large number of time steps are usually required when the barrier is close to the current asset price. A finite difference implementation is simpler and we propose a fourth-order numerical scheme for continuously and discretely monitored barriers. We demonstrate the superior performance of our technique over existing procedures for the Black-Scholes model and we then price barriers under constant elasticity of variance (CEV) diffusion. Continuously monitored barriers under CEV admit an analytical solution but evaluation via this formula is not straightforward. Furthermore, discretely monitored barriers have to be priced numerically. Our main contribution is therefore a highly accurate and efficient numerical scheme for barrier options under CEV and we provide several numerical examples to illustrate the merit of the new technique.

Analysis of an implicitly restarted simpler GMRES variant of augmented GMRES

We analyze a Simpler GMRES variant of augmented GMRES with implicit restarting for solving nonsymmetric linear systems with small eigenvalues. The use of a shifted Arnoldi process in the Simpler GMRES variant for computing Arnoldi basis vectors has the advantage of not requiring an upper Hessenberg factorization and this often leads to cheaper implementations. However the use of a non-orthogonal basis has been identified as a potential weakness of the Simpler GMRES algorithm. Augmented variants of GMRES also employ non-orthogonal basis vectors since approximate eigenvectors are added to the Arnoldi basis vectors at the end of a cycle and in case the approximate eigenvectors are ill-conditioned, this may have an adverse effect on the accuracy of the computed solution. This problem is the focus of our paper where we analyze the shifted Arnoldi implementation of augmented GMRES with implicit restarting and compare its performance and accuracy with that based on the Arnoldi process. We show that augmented Simpler GMRES with implicit restarting involves a transformation matrix which leads to an efficient implementation and we theoretically show that our implementation generates the same subspace as the corresponding GMRES variant. We describe various numerical tests that indicate that in cases where both variants are successful, our method based on Simpler GMRES keeps comparable accuracy as the augmented GMRES variant. Also, the Simpler GMRES variants perform better in terms of computational time required.

COS method for option pricing under a regime-switching model with time-changed Lévy processes

We extend the regime-switching model to the rich class of time-changed Lévy processes and use the Fourier cosine expansion (COS) method to price several options under the resulting models. The extension of the COS method to price under the regime-switching model is not straightforward because it requires the evaluation of the characteristic function which is based on a matrix exponentiation which is not an easy task. For a two-state economy, we give an analytical expression for computing this matrix exponential, and for more than two states, we use the Carathéodory–Fejér approximation to find the option prices efficiently. In the new framework developed here, it is possible to allow switches not only in the model parameters as is commonly done in literature, but we can also completely switch among various popular financial models under different regimes without any additional computational cost. Calibration of the different regime-switching models with real market data shows that the best models are the regime-switching time-changed Lévy models. As expected by the error analysis, the COS method converges exponentially and thus outperforms all other numerical methods that have been proposed so far.

A high-order finite difference method for option valuation

In this paper, we propose the use of an efficient high-order finite difference algorithm to price options under several pricing models including the BlackScholes model, the Mertons jumpdiffusion model, the Hestons stochastic volatility model and the nonlinear transaction costs or illiquidity models. We apply a local mesh refinement strategy at the points of singularity usually found in the payoff of most financial derivatives to improve the accuracy and restore the rate of convergence of a non-uniform high-order five-point stencil finite difference scheme. For linear models, the time-stepping is dealt with by using an exponential time integration scheme with CarathodoryFejr approximations to efficiently evaluate the product of a matrix exponential with a vector of option prices. Nonlinear BlackScholes equations are solved using an efficient iterative scheme coupled with a Richardson extrapolation. Our numerical experiments clearly demonstrate the high-order accuracy of the proposed finite difference method, making the latter a method of choice for solving both linear and nonlinear partial differential equations in financial engineering problems.

A TWO-FACTOR JUMP-DIFFUSION MODEL FOR PRICING CONVERTIBLE BONDS WITH DEFAULT RISK

The current literature on convertible bonds (CBs) comprises only models where the stock price and the interest rate are governed by pure-diffusion processes. This paper fills a gap by developing and implementing a two-factor model where both underlying factors follow jump-diffusion processes, and which also incorporates default risk. We derive the partial integro-differential equation satisfied by the CB price in our model, and solve it by a spectral method based on Chebyshev discretizations and Clenshaw–Curtis quadratures. The conversion, call, and put constraints give rise to a linear complementarity problem, which is solved by an operator-splitting (OS) method. Through numerical experiments, we investigate the effects that the various parameters have on the CB price. In particular, our numerical experiments show that jumps in the stock price have a significant impact on the CB price, while jumps in the interest rate tend to have a minor effect on the price. In general, the dynamics of the stock price have more impact in pricing the CB than the dynamics of the interest rate.

Option pricing under a Markov modulated model using a cubic B-spline collocation method

In this paper, we consider the extension of the cubic B-spline collocation method to price path-dependent and exotic options when the price dynamics of the underlying asset are governed by a Markovian process. In this setting, the classical Black-Scholes model is generalised to incorporate Markov-switching regime-switching properties which account for the influence of economic factors on asset price dynamics. Our numerical results presented using the Black-Scholes two regime-switching model demonstrate that the cubic B-spline collocation method not only yields second order convergent prices and hedging parameters, but it is also more accurate when the problem is convectively dominated.

A hybrid ENO reconstruction with limiters for systems of hyperbolic conservation laws

We consider a new class of essentially non-oscillatory (ENO) piecewise polynomial reconstructions together with interpolants based on Monotone Upwind Schemes for Conservation Laws. We improve the second-order ENO polynomial reconstruction by choosing an additional point inside the stencil in order to obtain the highest accuracy when combined with various non-linear limiters. The resulting algorithms are based on only one stencil selection, and we show that they can be efficiently implemented with largest allowable CFL numbers using optimal strong stability-preserving Runge-Kutta time evolution methods. The numerical results indicate that in some cases the schemes yield errors smaller in magnitude as compared to the fourth-order ENO scheme.