Robust Faber-Schauder Approximation Based on Discrete Observations of an Antiderivative

• 成果类型: Article
• 署名作者: Han, Xiyue; Schied, Alexander
• 署名单位: University of Waterloo
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X; 1526-5471
• DOI: 10.1287/moor.2022.0328
• 发表日期: 2026-08
• 关键词: wavelet expansion; quadratic spline interpolation; robust approximation; inverse problem; Error analysis; volatility estimation

摘要

We study the problem of reconstructing the Faber-Schauder coefficients of a continuous functionf from discrete observations of its antiderivative F. For instance, this question arises in financial mathematics when estimating the roughness of volatility from the integrated volatility of an asset price trajectory. Our approach starts with mathematically formulating the reconstruction problem through piecewise quadratic spline interpolation. We then provide a closed-form solution and an in-depth error analysis. These results lead to some surprising observations, which also throw new light on the classical topic of quadratic spline interpolation itself: They show that the well-known instabilities of this method can be located exclusively within the final generation of estimated Faber-Schauder coefficients, which suffer from nonlocality and strong dependence on the initial value. By contrast, all other Faber-Schauder coefficients depend only locally on the data, are independent of the initial value, and admit uniform error bounds. We thus conclude that a robust and well-behaved estimator for our problem can be obtained by simply dropping the final-generation coefficients from the estimated Faber-Schauder coefficients.