2022•RePEc: Research Papers in EconomicsOpen access

On asymptotically arbitrage-free approximations of the implied volatility

Masaaki Fukasawa

Open full text 0 citations

Abstract

Following-up Fukasawa and Gatheral (Frontiers of Mathematical Finance, 2022), we prove that the BBF formula, the SABR formula, and the rough SABR formula provide asymptotically arbitrage-free approximations of the implied volatility under, respectively, the local volatility model, the SABR model, and the rough SABR model.

Open-access reader

About this research paper

What this paper is about

Following-up Fukasawa and Gatheral (Frontiers of Mathematical Finance, 2022), we prove that the BBF formula, the SABR formula, and the rough SABR formula provide asymptotically arbitrage-free approximations of the implied volatility under, respectively, the local volatility model, the SABR model, and the rough SABR model.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Following-up Fukasawa and Gatheral (Frontiers of Mathematical Finance, 2022), we prove that the BBF formula, the SABR formula, and the rough SABR formula provide asymptotically arbitrage-free approximations of the implied volatility under, respectively, the local volatility model, the SABR model, and the rough SABR model.

Key concepts: SABR volatility model, Arbitrage, Volatility (finance), Implied volatility, Local volatility, Volatility smile, Volatility swap, Mathematics

Related papers

Back to paper searchBrowse research topicsOriginal source
On asymptotically arbitrage-free approximations of the implied volatility — Research Paper | ScholarLens