The paper introduces a GPU‑accelerated regularized estimation framework for the logarithmic vector multiplicative error model (log‑vMEM) with multivariate Gamma errors, designed to handle high‑dimensional positive‑valued financial time series. It proposes three hierarchical lag structures—componentwise, own‑other, and elementwise—combined with group lasso, group MCP, group SCAD, and adaptive group lasso penalties to induce sparsity and capture heterogeneous lag dynamics. Parameter estimation uses an alternating block‑coordinate algorithm with sequential Gauss‑Seidel updates, while GPU‑accelerated numerical quadrature mitigates the computational load of evaluating the multivariate Gamma likelihood; simulation studies assess parameter recovery and lag selection, and a real‑data application on Microsoft’s realized volatility demonstrates the approach in practice.
By Rohan Hemant Chhatre, Chiranjit Dutta, Nalini Ravishanker, Sumanta Basu
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: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
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: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
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
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
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.
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:2603. 00968v2 Announce Type: replace-cross Abstract: The Nash-Sutcliffe efficiency ($\text{NSE}$) is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series.
By Hristos Tyralis, Georgia Papacharalampous
arXiv:2606. 02138v1 Announce Type: cross Abstract: Out of distribution (OOD) events in multivariate time series forecasting are rare but often dominate real world risk, making average case forecasting insufficient for reliable deployment.
By Xudong Zhang, Jierui Lei, Jiacheng Li, Lingdong Shen, Jian Cui, Haina Tang
arXiv:2608.30446v1 Announce Type: cross
Abstract: Small-cap-inclusive equity universes contain recently listed and intermittently traded securities, so enforcing a common look-back discards a substan...
By Christian Bongiorno, Lorenzo Villassero