What Can Go Wrong During Caplet Stripping ?

TL;DR

This paper investigates issues in extracting caplet volatilities from market quotes, identifies causes of instability, and proposes practical solutions to improve robustness and accuracy in real-world applications.

Contribution

It introduces new kernel methods, node placement strategies, positivity enforcement techniques, and data checks to enhance the stability and reliability of caplet stripping procedures.

Findings

Significantly reduces oscillations in caplet volatility extraction.

Ensures robust positive caplet curves with minimal pricing errors.

Provides a fast, stable workflow suitable for practical use.

Abstract

We study exact and near exact extraction of caplet volatilities from market cap quotes and identify why some common choices produce extreme oscillations or negative vols. Interpolation scheme and node placement are shown to be the primary drivers of instability, which can be amplified by isolated bad quotes. We propose practical, production ready remedies: continuous flat-linear and C1 flat-smooth kernels that preserve bootstrap equivalence, midpoint node placement with a global solver, positivity enforcement via an exponential reparametrization or Hyman non-negative C1 splines. We also introduce simple data quality checks. Numerical experiments demonstrate substantially reduced oscillations, robust positive caplet curves, and negligible repricing error, delivering a fast and stable caplet stripping workflow suitable for real-world use.