Philipp Otto

Variable-Projection Sparse Functional Principal Component Analysis: Interpretable Functional Dimensionality Reduction with Applications to Raman Spectral Data

Functional principal component analysis (FPCA) provides low-rank representations of functional data but generally produces dense components, making it difficult to identify the localised regions contributing to dominant modes of variation. This limitation is particularly relevant in Raman spectroscopy, where spectra ar...

Learning Volatility Dependence Networks in UK Equity Markets using Penalised Spatiotemporal ARCH Models

Spatiotemporal ARCH models capture temporal volatility persistence and cross-sectional dependence but typically require a predefined spatial weight matrix. This is restrictive in financial markets, where the dependence network is rarely known. We develop a LASSO-penalised quasi-maximum likelihood estimator that jointly...

Service-effort trade-offs in large-scale bike-sharing rebalancing: A hierarchical reinforcement learning framework with imitation-based warm-starting