The paper introduces scalable regularized estimation techniques for logarithmic multiplicative error models (log‑vMEM) applied to multivariate positive‑valued financial time series. It employs hierarchical lag structures and various group penalties, using a blockwise coordinate descent algorithm with Gauss‑Seidel updates to efficiently estimate parameters. GPU‑accelerated quadrature integration is used to overcome the main computational bottleneck, and the methods are evaluated through extensive simulations and applied to intraday realized volatility data for Microsoft.
By Rohan Hemant Chhatre, Chiranjit Dutta, Nalini Ravishanker, Sumanta Basu
The paper introduces μs’ autotune, an automatic tuning strategy for the Lasso that optimizes a penalized Gaussian log‑likelihood over regression coefficients and noise standard deviation. Extensive simulations on regression and VAR models show that autotune is faster and yields better generalization and model selection, especially in low signal‑to‑noise regimes. The method also delivers a new noise‑standard‑deviation estimator, a visual diagnostic for sparsity, and is demonstrated on a real‑world financial dataset, with an accompanying R package available on GitHub.
By Tathagata Sadhukhan, Ines Wilms, Stephan Smeekes, Sumanta Basu
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang
arXiv:2608. 06340v1 Announce Type: cross Abstract: Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series.
By Daniel Paulin, Victor Elvira
Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series. Yet their moving-average term captures with a few parameters what a pure autoregression matches only with many lags.
arXiv:2411. 17136v2 Announce Type: replace-cross Abstract: Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations.
By Qianli Zhao, Chao Wang, Richard Gerlach, Giuseppe Storti, Lingxiang Zhang
arXiv:2610.28256v1 Announce Type: cross
Abstract: Sparse variational Gaussian processes (SVGPs) are a widely used, principled approach to scalable Gaussian process modelling. Their success stems from...
By Matteo Hoxha
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
By Yifan Hu, Yuante Li, Peiyuan Liu, Yuxia Zhu, Naiqi Li, Tao Dai, Shu-tao Xia, Dawei Cheng, Changjun Jiang
The paper introduces a feature selection framework for high‑dimensional financial regression that uses Monte Carlo Tree Search with Generalized Nested Rollout Policy Adaptation. It treats feature selection as a sequential decision process and evaluates candidate subsets by out‑of‑sample validation performance, incorporating subset size and multicollinearity into a lexicographic objective. Applied to a NASDAQ‑100 weekly return regression with 1,072 engineered features, the method outperforms an optimized Lasso baseline, achieving an out‑of‑sample R² of 0.6961 versus 0.6313.
By Cameron Mouangue, Tristan Cazenave
The paper presents an online algorithm for multivariate distributional regression tailored to high‑dimensional probabilistic electricity price forecasting. It models conditional means, variances, and dependence structures of 24‑hour day‑ahead prices using coordinate descent and LASSO regularization, enabling scalable estimation in large covariate spaces. A regularized estimation path allows early stopping to prevent overfitting, and the method delivers interpretable, well‑calibrated joint prediction intervals, outperforming benchmarks on German market data.
By Simon Hirsch
arXiv:2609.04239v2 Announce Type: replace
Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial...
By Seunghan Lee, Jaehoon Lee, Jun Seo, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Wonbin Ahn
arXiv:2601. 07687v3 Announce Type: replace-cross Abstract: Recent advances in nonlinear shrinkage yield asymptotically optimal cleaners for large covariance matrices and have been extended to empirical cross-covariances via singular-value shrinkage.
By Efstratios Manolakis, Christian Bongiorno, Rosario Nunzio Mantegna