Abstract

The rough Heston model has recently been shown to be extremely consistent with the observed empirical data in the financial market. However, the shortcoming of the model is that the conventional numerical method to compute option prices under it requires great computational effort due to the presence of the fractional Riccati equation in its characteristic function. In this study, we contribute by providing an efficient method while still retaining the quality of the solution under varying Hurst parameter for the fractional Riccati equations in two ways. First, we show that under the Laplace–Adomian-decomposition method, the infinite series expansion of the fractional Riccati equation’s solution corresponds to the existing expansion method from previous work for at least up to the fifth order. Then, we show that the fourth-order Padé approximants can be used to construct an extremely accurate global approximation to the fractional Riccati equation in an unexpected way. The pointwise approximation error of the global Padé approximation to the fractional Riccati equation is also provided. Unlike the existing work of third-order global Padé approximation to the fractional Riccati equation, our work extends the availability of Hurst parameter range without incurring huge errors. Finally, numerical comparisons were conducted to verify that our methods are indeed accurate and better than the existing method for computing both the fractional Riccati equation’s solution and option prices under the rough Heston model.

Highlights

  • Financial option pricing theory heavily relies on the development of the Black–Scholes model [1], which has several assumptions

  • It is important to realize that in the setting of option pricing under the rough Heston model, the application of the fractional Adams method to the fractional Riccati equation is undesirable in the real life calibration and computation due to its excessive computational cost needed (total time complexity of O(n a N 2 ) where n a is the number of space steps for inversion of characteristic function

  • We have provided an extensive literature review relevant to the fractional Riccati equation and rough Heston model

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Summary

Introduction

Financial option pricing theory heavily relies on the development of the Black–Scholes model [1], which has several assumptions. It is noted that the Padé approximation method performs better than the corresponding truncated Taylor series as usual, but it is not necessarily compatible for large t This is related to the study from [19] where the authors have utilized global rational approximants in obtaining the fractional Riccati equation’s solution by matching two series asymptotic expansions’ functions, as the expansion functions are similar to the Mittag–Leffler function and its asymptotic function. We employ the methods from [53] in approximating the fractional Riccati equation’s solution, and subsequently we were able compute option prices under the rough Heston model in an extremely efficient and accurate manner.

The Rough Heston Model
The Model
Characteristic Function of the Rough Heston Model
Analytical Approximation
Preliminaries
Laplace–Adomian-Decomposition Method
The Fractional Adams–Bashforth–Moulton
Expansions for the Solution of the Fractional Riccati Equation
Small Time Series Expansion
Large Time Series Expansion
The Global Padé Approximation
Definition
Approximation Errors
Third-Order Global Padé Approximant
Fourth-Order Global Padé Approximant
Numerical Experiments
Comparisons of Methods on the Fractional Riccati Equation
Option Pricing
Findings
Summary and Future Research
Full Text
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