Correct implied volatility shapes and reliable pricing in the rough Heston model

ArXiv ID: 2412.16067 “View on arXiv”

Authors: Unknown

Abstract

We use modifications of the Adams method and very fast and accurate sinh-acceleration method of the Fourier inversion (iFT) (S.Boyarchenko and Levendorskiĭ, IJTAF 2019, v.22) to evaluate prices of vanilla options; for options of moderate and long maturities and strikes not very far from the spot, thousands of prices can be calculated in several msec. with relative errors of the order of 0.5% and smaller running Matlab on a Mac with moderate characteristics. We demonstrate that for the calibrated set of parameters in Euch and Rosenbaum, Math. Finance 2019, v. 29, the correct implied volatility surface is significantly flatter and fits the data very poorly, hence, the calibration results in op.cit. is an example of the {"\em ghost calibration"} (M.Boyarchenko and Levendorkiĭ, Quantitative Finance 2015, v. 15): the errors of the model and numerical method almost cancel one another. We explain how calibration errors of this sort are generated by each of popular versions of numerical realizations of iFT (Carr-Madan, Lipton-Lewis and COS methods) with prefixed parameters of a numerical method, resulting in spurious volatility smiles and skews. We suggest a general {"\em Conformal Bootstrap principle"} which allows one to avoid ghost calibration errors. We outline schemes of application of Conformal Bootstrap principle and the method of the paper to the design of accurate and fast calibration procedures.

Keywords: Fourier Inversion (iFT), Implied Volatility Surface, Ghost Calibration, Conformal Bootstrap, Option Pricing

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical tools like fractional calculus, Fourier analysis, and conformal methods, indicating high complexity. It also presents extensive numerical comparisons, error analysis, and claims of accurate backtesting-ready pricing, demonstrating strong empirical rigor.

  flowchart TD
    Start["Research Goal: Correct implied volatility shapes & reliable pricing in rough Heston model"] --> Method["Methodology<br>Adams method + Fast iFT with Sinh-acceleration"]
    
    Method -->|Input| Data["Data<br>Calibrated parameters from Euch & Rosenbaum 2019"]
    
    Data --> Process["Computational Process<br>Calculate thousands of vanilla option prices"]
    
    Process -->|High Speed| Result1["Outcome: Prices in ms with <0.5% relative error"]
    Process -->|Comparison| Result2["Outcome: Correct IV surface is flatter than model output"]
    
    Result1 --> Conclusion["Key Findings<br>1. Ghost Calibration: Errors cancel spuriously<br>2. Conformal Bootstrap principle to avoid errors"]
    Result2 --> Conclusion