868 research outputs found
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The mathematical modelling and numerical solution of options pricing problems
Accurate and efficient numerical solutions have been described for a selection of financial options pricing problems. The methods are based on finite difference discretisation coupled with optimal solvers of the resulting discrete systems. Regular Cartesian meshes have been combined with orthogonal co-ordinate transformations chosen for numerical accuracy rather than reduction of the differential operator to constant coefficient form. They allow detailed resolution in the regions of interest where accuracy is most desired, and grid coarsening where there is least interest. These transformations are shown to be effective in producing accurate solutions on modest computational grids. The spatial discretisation strategy is chosen to meet accuracy requirements as sell as to produce coefficient matrices with favourable sparsity and stability properties.
In the case of single factor European options, a modified Crank-Nicolson, second order accurate finite difference scheme is presented, which uses adaptive upwind differences when the mesh Peclet conditions are violated. The resulting tridiagonal system of equations is solved using a direct solver. A careful study of grid refinement displays convergence towards the true solution and demonstrates a high level of accuracy can be obtained with this approach. Laplace inversion methods are also implemented as an alternative solution approach for the one-factor European option. Results are compared to those produced by the direct solver algorithm and are shown to be favourable.
It is shown how Semi-Lagrange time-integration can solve the path-dependent Asian pricing problem, by integrating out the average price term and simplifying the finite difference equations into a parameterised Black-Scholes form. The implicit equations that result are unconditionally stable, second order accurate and can be solved using standard tridiagonal solvers. The Semi-Lagrange method is shown to be easily used in conjunction with co-ordinate transformations applied in both spatial directions. A variable time-stepping scheme is implemented in the algorithm. Early exercise is also easily incorporated, the resulting linear complementarity problem can be solved using a projection or penalty method (the penalty method is shown to be slightly more efficient). Second order accuracy has been confirmed for Asian options that must be held to maturity. A comparison with published results for continuous-average-rate put and call options, with and without early exercise, shows that the method achieves basis point accuracy and that Richardson extrapolation can also be applied
A hybrid approach for the implementation of the Heston model
We propose a hybrid tree-finite difference method in order to approximate the
Heston model. We prove the convergence by embedding the procedure in a
bivariate Markov chain and we study the convergence of European and American
option prices. We finally provide numerical experiments that give accurate
option prices in the Heston model, showing the reliability and the efficiency
of the algorithm
High dimensional American options
Pricing single asset American options is a hard problem in mathematical finance. There are no closed form solutions available (apart from in the case of the perpetual option), so many approximations and numerical techniques have been developed. Pricing multi–asset (high dimensional) American options is still more difficult.
We extend the method proposed theoretically by Glasserman and Yu (2004) by employing regression basis functions that are martingales under geometric Brownian motion. This results in more accurate Monte Carlo simulations, and computationally cheap lower and upper bounds to the American option price. We have implemented these models in QuantLib, the open–source derivatives pricing library. The code for many of the models discussed in this thesis can be downloaded from quantlib.org as part of a practical pricing and risk management library.
We propose a new type of multi–asset option, the “Radial Barrier Option” for which we find analytic solutions. This is a barrier style option that pays out when a barrier, which is a function of the assets and their correlations, is hit. This is a useful benchmark test case for Monte Carlo simulations and may be of use in approximating multi–asset American options. We use Laplace transforms in this analysis which can be applied to give analytic results for the hitting times of Bessel processes.
We investigate the asymptotic solution of the single asset Black–Scholes–Merton equation in the case of low volatility. This analysis explains the success of some American option approximations, and has the potential to be extended to basket options
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From Continuous to Discrete: Studies on Continuity Corrections and Monte Carlo Simulation with Applications to Barrier Options and American Options
This dissertation 1) shows continuity corrections for first passage probabilities of Brownian bridge and barrier joint probabilities, which are applied to the pricing of two-dimensional barrier and partial barrier options, and 2) introduces new variance reduction techniques and computational improvements to Monte Carlo methods for pricing American options.
The joint distribution of Brownian motion and its first passage time has found applications in many areas, including sequential analysis, pricing of barrier options, and credit risk modeling. There are, however, no simple closed-form solutions for these joint probabilities in a discrete-time setting. Chapter 2 shows that, discrete two-dimensional barrier and partial barrier joint probabilities can be approximated by their continuous-time probabilities with remarkable accuracy after shifting the barrier away from the underlying by a factor. We achieve this through a uniform continuity correction theorem on the first passage probabilities for Brownian bridge, extending relevant results in Siegmund (1985a). The continuity corrections are applied to the pricing of two-dimensional barrier and partial barrier options, extending the results in Broadie, Glasserman & Kou (1997) on one-dimensional barrier options. One interesting aspect is that for type B partial barrier options, the barrier correction cannot be applied throughout one pricing formula, but only to some barrier values and leaving the other unchanged, the direction of correction may also vary within one formula.
In Chapter 3 we introduce new variance reduction techniques and computational improvements to Monte Carlo methods for pricing American-style options. For simulation algorithms that compute lower bounds of American option values, we apply martingale control variates and introduce the local policy enhancement, which adopts a local simulation to improve the exercise policy. For duality-based upper bound methods, specifically the primal-dual simulation algorithm (Andersen and Broadie 2004), we have developed two improvements. One is sub-optimality checking, which saves unnecessary computation when it is sub-optimal to exercise the option along the sample path; the second is boundary distance grouping, which reduces computational time by skipping computation on selected sample paths based on the distance to the exercise boundary. Numerical results are given for single asset Bermudan options, moving window Asian options and Bermudan max options. In some examples the computational time is reduced by a factor of several hundred, while the confidence interval of the true option value is considerably tighter than before the improvements
Pricing methods for American options
Bibliography: leaves 89-94.This thesis is about the comparison of Pricing models for the valuation of American Options. Three classes of numerical approaches are considered. These are Lattice Methods, Analytic Approximations and Monte Carlo Simulation. Methods will be contrasted in terms of accuracy and speed of the computed American option price. One particular method utilises regression when estimating the American option price. For this approach the impact of outliers and multicollinearity is examined and alternative regression models fitted. Monte Carlo Simulation is implemented to calculate early exercise probabilities of American options in the South African market. Results are compared for both call and put options. A test set of 3550 options is simulated with parameters mirroring the South African economy. On this set, the accuracy of all methods is assessed relative to a benchmark price, which is computed by a convergent lattice approach. Finally, American Symmetry is used to evaluate both put and call options
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General lattice methods for arithmetic Asian options
In this research, we develop a new discrete-time model approach with flexibly changeable driving dynamics for pricing Asian options, with possible early exercise, and a fixed or floating strike price. These options are ubiquitous in financial markets but can also be recast in the framework of real options. Moreover, we derive an accurate lower bound to the price of the European Asian options under stochastic volatility. We also survey theoretical aspects; more specifically, we prove that our tree method for the European Asian option in the binomial model is unconditionally convergent to the continuous-time equivalent. Numerical experiments confirm smooth, monotonic convergence, highly precise performance, and robustness with respect to changing driving dynamics and contract features
Numerical Methods for Real Options in Telecommunications
This thesis applies modern financial option valuation methods to the problem of telecommunication network capacity investment decision timing. In particular, given a cluster of base stations (wireless network with a certain traffic capacity per base station), the objective of this thesis is to determine when it is optimal to increase capacity to each of the base stations of the cluster. Based on several time series taken from the wireless and bandwidth industry, it is argued that capacity usage is the major uncertain component in telecommunications. It is found that price has low volatility when compared to capacity usage. A real options approach is then applied to derive a two dimensional partial integro-differential equation (PIDE) to value investments in telecommunication infrastructure when capacity usage is uncertain and has temporary sudden large variations. This real options PIDE presents several numerical challenges. First, the integral term must be solved accurately and quickly enough such that the general PIDE solution is reasonably accurate. To deal with the integral term, an implicit method is suggested. Proofs of timestepping stability and convergence of a fixed point iteration scheme are presented. The correlation integral is computed using a fast Fourier transform (FFT) method. Techniques are developed to avoid wrap-around effects. This method is tested on option pricing problems where the underlying asset follows a jump diffusion process. Second, the absence of diffusion in one direction of the two dimensional PIDE creates numerical challenges regarding accuracy and timestep selection. A semi-Lagrangian method is presented to alleviate these issues. At each timestep, a set of one dimensional PIDEs is solved and the solution of each PIDE is updated using semi-Lagrangian timestepping. Crank-Nicolson and second order backward differencing timestepping schemes are studied. Monotonicity and stability results are derived. This method is tested on continuously observed Asian options. Finally, a five factor algorithm that captures many of the constraints of the wireless network capacity investment decision timing problem is developed. The upgrade decision for different upgrade decision intervals (e. g. monthly, quarterly, etc. ) is studied, and the effect of a safety level (i. e. the maximum allowed capacity used in practice on a daily basis—which differs from the theoretical maximum) is investigated
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