Name of Quantlet: Replication-R-Code-Adjusted-Range-SN-Relevant-Hypotheses-FTS

Published in: Adjusted-Range Self-Normalization for Relevant Hypotheses in Functional Time Series

Description: R code accompanying the manuscript "Adjusted-Range Self-Normalization for Relevant Hypotheses in Functional Time Series".
Implements fully functional (no KL/FPCA truncation) inference for relevant hypotheses in weakly dependent functional time series, with:
(i) quadratic self-normalization benchmark (Dette et al.-style),
(ii) adjusted-range self-normalization (proposed),
(iii) Monte Carlo calibration for the classical/degenerate case Delta = 0,
(iv) simulation studies (size and power under multiple dependence structures),
(v) empirical illustration based on trade-level Bitcoin options data that constructs daily constant-maturity implied-volatility smiles
and applies relevant mean change-point testing with economically interpretable “relevance” statements.

Submitted: 14 February 2026

Keywords: 
- Relevant hypothesis
- Functional time series
- Weak dependence
- Self-normalization
- Adjusted range
- Mean function
- Covariance operator
- Relevant change-point detection
- Monte Carlo critical values
- Bitcoin options
- Implied-volatility smile
- R

Author: Zhuo Lin, Jiajing Sun, Wolfgang Karl Härdle, Meiting Zhu