Abstract

This paper develops a systematic method for calculating approximate prices for a wide range of securities implying the tools of spectral analysis, singular and regular perturbation theory. Price options depend on stochastic volatility, which may be multiscale, in the sense that it may be driven by one fast-varying and one slow-varying factor. The found the approximate price of two-barrier options with multifactor volatility as a schedule for own functions. The theorem of estimation of accuracy of approximation of option prices is established. Explicit formulas have been found for finding the value of derivatives based on the development of eigenfunctions and eigenvalues of self-adjoint operators using boundary-value problems for singular and regular perturbations. This article develops a general method of obtaining a guide price for a broad class of securities. A general theory of derivative valuation of options generated by diffusion processes is developed. The algorithm of calculating the approximate price is given. The accuracy of the estimates is established. The theory developed is applied to a diffusion operator, which is decomposed by eigenfunctions and eigenvalues. The purpose of the article is to develop an algorithm for finding the approximate price of two-barrier options and to find explicit formulas for finding the value of derivatives based on the development of self-functions and eigenvalues of self-adjoint operators using boundary-value problems for singular and regular perturbations. Price finding is reduced to the problem solving of eigenvalues and eigenfunctions of a certain equation. The main advantage of our pricing methodology is that, by combining methods in spectral theory, regular perturbation theory, and singular perturbation theory, we reduce everything to equations to find eigenfunctions and eigenvalues.

Highlights

  • Spectral theory was widely used in the second half of the 20th century by many economists

  • In recent years spectral analysis has become an increasingly popular tool for use in financial mathematics to analyze diffusion models which are based on the expansion of eigenfunctions and eigenvalues of linear operators

  • Spectral theory as well as stochastic volatility models has become an indispensable tool in financial mathematics, for the matter of that, two barrier option prices are subjected to Brownian motion and are correlated with volatility [6]

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Summary

INTRODUCTION

Spectral theory was widely used in the second half of the 20th century by many economists. In recent years spectral analysis has become an increasingly popular tool for use in financial mathematics to analyze diffusion models which are based on the expansion of eigenfunctions and eigenvalues of linear operators. Among the scientific problems that can be solved by applying spectral methods: predicting option prices, [5] securities interest rates [11], modeling the volatility of financial assets [4]. Assets estimation problems are solved analytically by methods of spectral theory [5]. Spectral theory as well as stochastic volatility models has become an indispensable tool in financial mathematics, for the matter of that, two barrier option prices are subjected to Brownian motion and are correlated with volatility [6]. The study of stochastic volatility, volatility assets in particular, underlies the derivative and is controlled by nonlocal diffusion. We will work with infinitesimal generators of three-dimensional diffusion

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