7,135 research outputs found

    Spectral Decomposition of Option Prices in Fast Mean-Reverting Stochastic Volatility Models

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    Using spectral decomposition techniques and singular perturbation theory, we develop a systematic method to approximate the prices of a variety of options in a fast mean-reverting stochastic volatility setting. Four examples are provided in order to demonstrate the versatility of our method. These include: European options, up-and-out options, double-barrier knock-out options, and options which pay a rebate upon hitting a boundary. For European options, our method is shown to produce option price approximations which are equivalent to those developed in [5]. [5] Jean-Pierre Fouque, George Papanicolaou, and Sircar Ronnie. Derivatives in Financial Markets with Stochas- tic Volatility. Cambridge University Press, 2000

    Robust hedging of digital double touch barrier options

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    In this dissertation, we present basic idea and key results for model-free pricing and hedging of digital double barrier options. Besides we extend this model to the market with non-zero interest rate by allowing some model-based trading. Moreover we apply this hedging strategies to Heston stochastic volatility model and compare its performances with that of delta hedging strategies in such setting. Finally we further interpret these numerical results to show the advantages and disadvantages of these two types of hedging strategies

    Pricing of the European Options by Spectral Theory

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    We discuss the efficiency of the spectral method for computing the value of the European Call Options, which is based upon the Fourier series expansion. We propose a simple approach for computing accurate estimates. We consider the general case, in which the volatility is time dependent, but it is immediate extend our methodology at the case of constant volatility. The advantage to write the arbitrage price of the European Call Options as Fourier series, is matter of computation complexity. Infact, the methods used to evaluate options of this kind have a high value of computation complexity, furthermore, them have not the capacity to manage it. We can define, by an easy analytical relation, the computation complexity of the problem in the framework of general theory of the ”Function Analysis”, called The Spectral Theory.Options Pricing, Computation Complexity.

    A Mixed PDE/Monte Carlo approach as an efficient way to price under high-dimensional systems

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    We propose to price derivatives modelled by multi-dimensional systems of stochastic di�fferential\ud equations using a mixed PDE/Monte Carlo approach. We derive a stochastic PDE where some of the coeffi�cients are conditional on stochastic ancillary factors. The stochastic\ud PDE is solved with either analytical or �finite diff�erence methods, where we simulate all the ancillary processes using Monte Carlo. The multilevel technique has also been introduced to further reduce the variance. The combined method showed over 80% cost reduction for the same accuracy, in pricing a barrier option in an FX market with stochastic interest rate and volatility (which is usually expensive to work with) , when compared to the pure Monte\ud Carlo simulation

    On the valuation of fader and discrete barrier options in Heston's Stochastic Volatility Model

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    We focus on closed-form option pricing in Hestons stochastic volatility model, in which closed-form formulas exist only for few option types. Most of these closed-form solutions are constructed from characteristic functions. We follow this approach and derive multivariate characteristic functions depending on at least two spot values for different points in time. The derived characteristic functions are used as building blocks to set up (semi-) analytical pricing formulas for exotic options with payoffs depending on finitely many spot values such as fader options and discretely monitored barrier options. We compare our result with different numerical methods and examine accuracy and computational times. --exotic options,Heston Model,Characteristic Function,Multidimensional Fast Fourier Transforms

    Pricing Step Options under the CEV and other Solvable Diffusion Models

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    We consider a special family of occupation-time derivatives, namely proportional step options introduced by Linetsky in [Math. Finance, 9, 55--96 (1999)]. We develop new closed-form spectral expansions for pricing such options under a class of nonlinear volatility diffusion processes which includes the constant-elasticity-of-variance (CEV) model as an example. In particular, we derive a general analytically exact expression for the resolvent kernel (i.e. Green's function) of such processes with killing at an exponential stopping time (independent of the process) of occupation above or below a fixed level. Moreover, we succeed in Laplace inverting the resolvent kernel and thereby derive newly closed-form spectral expansion formulae for the transition probability density of such processes with killing. The spectral expansion formulae are rapidly convergent and easy-to-implement as they are based simply on knowledge of a pair of fundamental solutions for an underlying solvable diffusion process. We apply the spectral expansion formulae to the pricing of proportional step options for four specific families of solvable nonlinear diffusion asset price models that include the CEV diffusion model and three other multi-parameter state-dependent local volatility confluent hypergeometric diffusion processes.Comment: 30 pages, 16 figures, submitted to IJTA
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