What Can Go Wrong During Caplet Stripping ?

2026-05-06

AI Summary

This study addresses the severe oscillations and spurious negative values commonly encountered when extracting caplet volatilities from market cap quotes. To this end, it proposes a robust stripping framework that integrates several key innovations: continuity-preserving flat-linear and C¹ flat-smooth kernels that maintain bootstrap equivalence, midpoint node placement, exponential reparameterization, Hyman’s non-negative C¹ spline constraints, and a global optimization solver. Embedded within this framework is a data quality diagnostics mechanism to further enhance reliability. The combined approach effectively suppresses volatility curve oscillations, rigorously enforces positivity, and achieves negligible repricing errors, thereby delivering an efficient, stable, and practically viable methodology for caplet volatility extraction.

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.

Problem

• caplet stripping
• volatility extraction
• negative volatility
• oscillations
• interpolation instability

Innovation

• caplet stripping
• volatility interpolation
• non-negative splines
• bootstrap equivalence
• data quality checks