Approximate Option Pricing

As increasingly large volumes of sophisticated options are traded in world financial markets, determining a ``fair' price for these options has become an important and difficult computational problem. Many valuation codes use the binomial pricing model, in which the stock price is driven by a random walk. In this model, the value of an n -period option on a stock is the expected time-discounted value of the future cash flow on an n -period stock price path. Path-dependent options are particularly difficult to value since the future cash flow depends on the entire stock price path rather than on just the final stock price. Currently such options are approximately priced by Monte Carlo methods with error bounds that hold only with high probability and which are reduced by increasing the number of simulation runs. In this article we show that pricing an arbitrary path-dependent option is \#-P hard. We show that certain types of path-dependent options can be valued exactly in polynomial time. Asian options are path-dependent options that are particularly hard to price, and for these we design deterministic polynomial-time approximate algorithms. We show that the value of a perpetual American put option (which can be computed in constant time) is in many cases a good approximation of the value of an otherwise identical n -period American put option. In contrast to Monte Carlo methods, our algorithms have guaranteed error bounds that are polynomially small (and in some cases exponentially small) in the maturity n . For the error analysis we derive large-deviation results for random walks that may be of independent interest. Received August 13, 1996; revised April 2, 1997.     

Fast and accurate pricing of discretely monitored barrier options by numerical path integration

Barrier options are financial derivative contracts that are activated or deactivated according to the crossing of specified barriers by an underlying asset price. Exact models for pricing barrier options assume continuous monitoring of the underlying dynamics, usually a stock price. Barrier options in traded markets, however, nearly always assume less frequent observation, e.g. daily or weekly. These situations require approximate solutions to the pricing problem. We present a new approach to pricing such discretely monitored barrier options that may be applied in many realistic situations. In particular, we study daily monitored up-and-out call options of the European type with a single underlying stock. The approach is based on numerical approximation of the transition probability density associated with the stochastic differential equation describing the stock price dynamics, and provides accurate results in less than one second whenever a contract expires in a year or less. The flexibility of the method permits more complex underlying dynamics than the Black and Scholes paradigm, and its relative simplicity renders it quite easy to implement.     

Option Pricing With Modular Neural Networks

This paper investigates a nonparametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogeneous of degree one with respect to the underlying index price and the strike price. When compared to an array of parametric and nonparametric models, the MNN method consistently exerts superior out-of-sample pricing performance. We conclude that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint).     

Derivative Asset Pricing with Transaction Costs: An Extension

This paper examines the optimal perfect hedging (super-replication) of an option by a cash-plus-riskless asset portfolio within the context of the binomial model. The cases discussed here were not covered by the earlier studies of Boyle and Vorst (1992) and Bensaid, Lesne, Pagès and Scheinkman (1992). It is argued that these cases are empirically important, and that there is some indication that they are encountered very often in practice in the Swiss options market. A new algorithm is developed to compute the option price lower bound (bid price) for such cases. It is then shown that, for most such cases, the portfolio hedging the short call when replication is not optimal coincides with the Merton (1973) lower bound.     

A Spectral Element Approximation to Price European Options. II. The Black-Scholes Model with Two Underlying Assets

We develop a Legendre quadrilateral spectral element approximation for the Black-Scholes equation to price European options with two underlying assets. A weak formulation of the equations imposes the boundary conditions naturally along the boundaries where the equation becomes singular. As examples, we apply the method to price European rainbow and basket options. We compare the efficiency for fully implicit and IMEX integration of the equations in time, three iterative solvers and two diagonal preconditioners. Of the choices, we find that GMRES with a fully implicit approximation in time, preconditioned with the mass matrix is the most efficient.     

A new numerical method for pricing fixed-rate mortgages with prepayment and default options

In this paper we consider the valuation of fixed-rate mortgages including prepayment and default options, where the underlying stochastic factors are the house price and the interest rate. The mathematical model to obtain the value of the contract is posed as a free boundary problem associated to a partial differential equation (PDE) model. The equilibrium contract rate is determined by using an iterative process. Moreover, appropriate numerical methods based on a Lagrange–Galerkin discretization of the PDE, an augmented Lagrangian active set method and a Newton iteration scheme are proposed. Finally, some numerical results to illustrate the performance of the numerical schemes, as well as the qualitative and quantitative behaviour of solution and the optimal prepayment boundary are presented.     

PDE formulation of some SABR/LIBOR market models and its numerical solution with a sparse grid combination technique

SABR models have been used to incorporate stochastic volatility to LIBOR market models (LMM) in order to describe interest rate dynamics and price interest rate derivatives. From the numerical point of view, the pricing of derivatives with SABR/LIBOR market models (SABR/LMMs) is mainly carried out with Monte Carlo simulation. However, this approach could involve excessively long computational times. For first time in the literature, in the present paper we propose an alternative pricing based on partial differential equations (PDEs). Thus, we pose original PDE formulations associated to the SABR/LMMs proposed by Hagan and Lesniewsk (2008), Mercurio and Morini (2009) and Rebonato and White (2008). Moreover, as the PDEs associated to these SABR/LMMs are high dimensional in space, traditional full grid methods (like standard finite differences or finite elements) are not able to price derivatives over more than three or four underlying interest rates. In order to overcome this curse of dimensionality, a sparse grid combination technique is proposed. A comparison between Monte Carlo simulation results and the ones obtained with the sparse grid technique illustrates the performance of the method.     

Analysis of the SSAP Method for the Numerical Valuation of High-Dimensional Multivariate American Securities

We consider a new numerical method developed by Barraquand and Martineau for the pricing of American securities where the payoff depends on several sources of uncertainty. This method utilizes Monte Carlo simulation and is referred to as Stratified State Aggregation along the Payoff (SSAP). Since there are no other methods that so effectively reduce the dimensionality of high-dimensional problems, the SSAP method has generated significant interest. Numerical results are presented showing that, if a sufficiently large number of time steps are used, in the cases of the two-dimensional maximum and minimum options, SSAP typically prices to within 6 cents of the true price. However, we show that if the security depends on two or more sources of uncertainty, then the price obtained by the SSAP method will not, in general, converge to the correct theoretical price, due in large part to incorrect exercise decisions being made. We analyze the exercise regions in the cases of the two-dimensional maximum and minimum options and show how SSAP makes incorrect exercise decisions. Suggestions for improving SSAP pricing accuracy by choosing a partition other than the payoff are discussed. Received October 3, 1997; revised February 10, 1998.     

A predictor-corrector scheme based on the ADI method for pricing American puts with stochastic volatility

In this paper, we introduce a new numerical scheme, based on the ADI (alternating direction implicit) method, to price American put options with a stochastic volatility model. Upon applying a front-fixing transformation to transform the unknown free boundary into a known and fixed boundary in the transformed space, a predictor-corrector finite difference scheme is then developed to solve for the optimal exercise price and the option values simultaneously. Based on the local von Neumann stability analysis, a stability requirement is theoretically obtained first and then tested numerically. It is shown that the instability introduced by the predictor can be damped, to some extent, by the ADI method that is used in the corrector. The results of various numerical experiments show that this new approach is fast and accurate, and can be easily extended to other types of financial derivatives with an American-style exercise.Another key contribution of this paper is the proposition of a set of appropriate boundary conditions, particularly in the volatility direction, upon realizing that appropriate boundary conditions in the volatility direction for stochastic volatility models appear to be controversial in the literature. A sound justification is also provided for the proposed boundary conditions mathematically as well as financially.     

Polynomial Algorithms for Pricing Path-Dependent Interest Rate Instruments

In this paper we study algorithms for pricing of interest rate instruments using recombining tree (scenario lattice) interest models. The price is defined as expected discounted cash flow. If the cash-flow generated by the instrument depends on the full or partial history of interest rates (path-dependent contracts), then pricing algorithms are typically of exponential complexity. We show that for some models, including product form cash-flows, additive cash-flows, delayed cash-flows and limited path-dependent cash-flows, polynomial pricing algorithms exist.     

Hedging an option portfolio with minimum transaction lots: A fuzzy goal programming problem

Options are designed to hedge against risks to their underlying assets such as stocks. One method of forming option-hedging portfolios is using stochastic programming models. Stochastic programming models depend heavily on scenario generation, a challenging task. Another method is neutralizing the Greek risks derived from the Black–Scholes formula for pricing options. The formula expresses the option price as a function of the stock price, strike price, volatility, risk-free interest rate, and time to maturity. Greek risks are the derivatives of the option price with respect to these variables. Hedging Greek risks requires no human intervention for generating scenarios. Linear programming models have been proposed for constructing option portfolios with neutralized risks and maximized investment profit. However, problems with these models exist. First, feasible solutions that can perfectly neutralize the Greek risks might not exist. Second, models that involve multiple assets and their derivatives were incorrectly formulated. Finally, these models lack practicability because they consider no minimum transaction lots. Considering minimum transaction lots can exacerbate the infeasibility problem. These problems must be resolved before option hedging models can be applied further. This study presents a revised linear programming model for option portfolios with multiple underlying assets, and extends the model by incorporating it with a fuzzy goal programming method for considering minimum transaction lots. Numerical examples show that current models failed to obtain feasible solutions when minimum transaction lots were considered. By contrast, while the proposed model solved the problems efficiently.     

High-accuracy finite-difference methods for the valuation of options

A finite-difference scheme often employed for the valuation of options from the Black–Scholes equation is the Crank–Nicolson (CN) scheme. The CN scheme is second order in both time and asset. For a rapid valuation with a reasonable resolution of the option price curve, it requires extremely small steps in both time and asset. In this paper, we present high-accuracy finite-difference methods for the Black–Scholes equation in which we employ the fourth-order L-stable Simpson-type (LSIMP) time integration schemes developed earlier and the well-known Numerov method for discretization in the asset direction. The resulting schemes, called LSIMP–NUM, are fourth order in both time and asset. The LSIMP–NUM schemes obtained can provide a rapid, stable and accurate resolution of option prices, allowing for relatively large steps in both time and asset. We compare the computational efficiency of the LSIMP–NUM schemes with the CN and Douglas schemes by considering valuation of European options and American options via the linear complementarity approach.     

Evaluating strategic options using decision-theoretic planning

The purpose of this research is to examine whether decision-theoretic planning techniques can be used to help managers evaluate strategic options in complex and uncertain environments. Firms faced with choices such as whether to acquire a start-up, develop a new product, or invest in updated production technology continue to make decisions based on unreliable heuristics, “gut feel” or misleading financial measures such as net present value (NPV). In this paper we show that decision-theoretic planning techniques originally developed for robot planning permit us to gain the insights provided by real options analysis without working within the restrictions of models designed to price financial options or incurring the overhead of constructing huge decision trees. A biotechnology licensing problem similar to those addressed elsewhere in the real options literature is used to illustrate the methodology and demonstrate its feasibility.     

No Arbitrage Between Economies and Correlation Risk Management

The first goal of this paper is to clarify the implications of the no arbitrage assumption in the context of several countries and extend to a general setting of continuous-time finance and stochastic interest rates results which were more or less present in classical finance models such as the international APT (see Solnik (1983)). In particular, the remarkable relationship between the risk premia in two different countries and the sole volatility of the exchange rate is easily derived. Secondly, we examine the pricing and hedging of cross-currencies options when interest rates are stochastic in all countries. The dependence of risk neutrality arguments on the reference numéraire as developed in Geman (1989) becomes particularly clear in the case of several currencies.     

Circulant preconditioning technique for barrier options pricing under fractional diffusion models

In recent years, considerable literature has proposed the more general class of exponential Lévy processes as the underlying model for prices of financial quantities, which thus better explain many important empirical facts of financial markets. Finite moment log stable, Carr–Geman–Madan–Yor and KoBoL models are chosen from those above-mentioned models as the dynamics of underlying equity prices in this paper. With such models pricing barrier options, one kind of financial derivatives is transformed to solve specific fractional partial differential equations (FPDEs). This study focuses on numerically solving these FPDEs via the fully implicit scheme, with the shifted Grünwald approximation. The circulant preconditioned generalized minimal residual method which converges very fast with theoretical proof is incorporated for solving resultant linear systems. Numerical examples are given to demonstrate the effectiveness of the proposed preconditioner and show the accuracy of our method compared with that done by the Fourier cosine expansion method as a benchmark.     

Normalized particle swarm optimization for complex chooser option pricing on graphics processing unit

An option is a financial instrument that derives its value from an underlying asset, for example, a stock. There are a wide range of options traded today from simple and plain (European options) to exotic (chooser options) that are very difficult to evaluate. Both buyers and sellers continue to look for efficient algorithms and faster technology to price options for better profit and to beat the competition. There are mathematical models like the Black–Scholes–Merton model used to price options approximately for simple and plain options in the form of closed form solution. However, the market is flooded with various styles of options, which are difficult to price, and hence there are many numerical techniques proposed for pricing. The computational cost for pricing complex options using these numerical techniques is exorbitant for reasonable accuracy in pricing results. Heuristic approaches such as particle swarm optimization (PSO) have been proposed for option pricing, which provide same or better results for simple options than that of numerical techniques at much less computational cost. In this study, we first map the PSO parameters to option pricing parameters. Analyzing the characteristics of PSO and option pricing, we propose a strategy to normalize some of the parameters, which helps in better understanding of the sensitivity of these and other parameters on option pricing results. We then avail of the inherent concurrency of the PSO algorithm while searching for an optimum solution, and design an algorithm for implementation on a modern state-of-the-art graphics processor unit (GPU). Our implementation makes use of the architectural features of GPU in accelerating the computing performance while maintaining accuracy on the pricing results.     

European option pricing under fuzzy environments

The application of fuzzy sets theory to the Black–Scholes formula is proposed in this article. Owing to the vague fluctuation of financial markets from time to time, the risk‐free interest rate, volatility, and the price of underlying assets may occur imprecisely. In this case, it is natural to consider the fuzzy interest rate, fuzzy volatility, and fuzzy stock price. The form of “Resolution Identity” in fuzzy sets theory will be invoked to propose the fuzzy price of European options. Under these assumptions, the European option price at time t will turn into a fuzzy number. This will allow a financial analyst to choose the European price at his (her) acceptable degree of belief. To obtain the belief degree, the optimization problems have to be solved. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 89–102, 2005.     

Modeling and Simulation of an Artificial Stock Option Market

Since their introduction in 1973, options have become an important and very popular financial instrument. However, despite much research performed on the subject, the effects of option trading on the underlying asset market are still debated. Both empirical and theoretical studies have failed to point out how price volatility and volumes of the underlying asset are affected. In this paper we present the first study on the effects of an option market related to an underlying stock market, using an artificial financial market based on heterogeneous agents. We modeled a realistic European option using two market models. The microstructure of the first model is kept as simple as possible, being composed only of random traders. The second model is more complex and realistic, involving the presence of various kinds of trading strategies (random, fundamentalist and chartist). We show that the introduction of options, in the proposed models, tends to decrease the volatility of the underlying stock price. Moreover, the traders’ wealth can be strongly affected by the use of option hedging.     

Fuzzy pricing of geometric Asian options and its algorithm

Owing to the fluctuations of the financial market, input data in the options pricing formula cannot be expected to be precise. This paper discusses the problem of pricing geometric Asian options under the fuzzy environment. We present the fuzzy price of the geometric Asian option under the assumption that the underlying stock price, the risk-free interest rate and the volatility are all fuzzy numbers. This assumption makes the financial investors to pick any geometric Asian option price with an acceptable belief degree. In order to obtain the belief degree, the interpolation search algorithm has been proposed. Some numerical examples are presented to illustrate the rationality and practicability of the model and the algorithm. Finally, an empirical study is performed based on the real data. The empirical study results indicate that the proposed fuzzy pricing model of geometric Asian option is a useful tool for modeling the imprecise problem in the real world.     

Solving complex PIDE systems for pricing American option under multi-state regime switching jump–diffusion model

Based on exponential time differencing approach, an efficient second order method is developed for solving systems of partial integral differential equations. The method is implemented to solve American options under multi-state regime switching with jumps. The method is seen to be strongly stable ($L$-stable) and avoids any spurious oscillations caused by non-smooth initial data. The predictor–corrector nature of the method makes it highly efficient in solving nonlinear PIDEs in each regime with different volatilities and interest rates. Penalty method approach is applied to handle the free boundary constraint of American options. Numerical results are presented to illustrate the performance of the method for American options under Merton’s jump–diffusion models. Padé approximation of matrix exponential functions and partial fraction splitting technique are applied to construct computationally efficient version of the method. Efficiency, accuracy and reliability of the method are compared with those of the existing methods available in the literature.