Modern Challenges and Innovations in Financial Econometrics

Topic Information

Dear Colleagues,

Financial econometrics lies at the crossroads of statistics, economics, and finance, providing an essential tool for understanding increasingly complex and data-rich markets. The growing availability of high-frequency and alternative data, combined with advances in computation and artificial intelligence, has transformed this field—while simultaneously introducing new challenges. This Topic aims to explore recent methodological and empirical developments that address issues such as model instability, nonstationarity, high dimensionality, and structural breaks. We invite the submission of contributions that propose innovative econometric frameworks; integrate machine learning techniques; and enhance forecasting, risk modeling, and decision-making in financial contexts. Both theoretical papers and applied studies that demonstrate the practical relevance of new methods are welcome. By fostering dialog between researchers and practitioners, this Topic seeks to advance robust, scalable, and interpretable approaches that can meet the demands of modern financial analysis.

Dr. Agnieszka Szmelter-Jarosz
Dr. Hamed Nozari
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Econometrics econometrics	1.4	2.4	2013	33.4 Days	CHF 1500	Submit
Economies economies	2.3	5.2	2013	23.3 Days	CHF 1800	Submit
Forecasting forecasting	4.2	7.1	2019	23.8 Days	CHF 1800	Submit
Journal of Risk and Financial Management jrfm	-	5.5	2008	18.3 Days	CHF 1600	Submit

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Published Papers (2 papers)

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All Econometrics Economies JRFM Forecasting

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Open AccessArticle

Causal-Structure-Based Cryptocurrency Price Direction Prediction Model

by Yuantai Cui and Hiroaki Fukunishi

Forecasting 2026, 8(4), 58; https://doi.org/10.3390/forecast8040058 (registering DOI) - 7 Jul 2026

Abstract

In the highly volatile cryptocurrency market, trading decision support based on price prediction remains a challenging task. Although machine learning and deep learning techniques have been widely applied to cryptocurrency price prediction, many existing approaches rely on correlation-based black-box models, which limits interpretability [...] Read more.

In the highly volatile cryptocurrency market, trading decision support based on price prediction remains a challenging task. Although machine learning and deep learning techniques have been widely applied to cryptocurrency price prediction, many existing approaches rely on correlation-based black-box models, which limits interpretability and robustness. In this study, we employed a NOTEARS-Linear-based Prediction Model (NLBPM) that directly incorporated causal structures inferred through a causal discovery method as structural constraints within the prediction model. Unlike conventional approaches that focus primarily on minimizing prediction error, the NLBPM emphasized return maximization as its objective function, thereby prioritizing practical economic value. Using Bitcoin as a case study, we constructed a model to predict the direction of price movement four hours ahead and evaluated its performance using a rolling-window scheme with a one-month sliding window. Analysis of the inferred causal structures showed that the returns improved when trades were executed only during rolling-window trials in which specific directed edges to the target variable were detected. Based on this finding, we proposed a causal filter strategy that restricts trading to periods in which specific directed edges to the target variable are detected. In the data period analyzed in this study, the selected edge was the one from the opening price (Open) to the target variable. Backtesting experiments incorporating a transaction fee of 0.1% demonstrated that, while the benchmark LSTM model achieved a negative monthly average return of −3.20% and the NLBPM without filtering yielded −0.72%, the NLBPM with the Open filter attained a higher monthly average return of 10.35%. This study supports the usefulness of using inferred causal structure for cryptocurrency trading decision support. Full article

(This article belongs to the Topic Modern Challenges and Innovations in Financial Econometrics)

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35 pages, 2050 KB  

Open AccessArticle

Leakage-Controlled Horizon-Specific Model Selection for Daily Equity Forecasting: An Automated Multi-Model Pipeline

by Francisco Augusto Nuñez Perez, Francisco Javier Aguilar Mosqueda, Adrian Ramos Cuevas, Jaqueline Muñoz Beltran and Jose Cruz Nuñez Perez

Forecasting 2026, 8(2), 34; https://doi.org/10.3390/forecast8020034 - 20 Apr 2026

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Abstract

Short-horizon equity forecasting remains challenging because daily prices are noisy, heavy-tailed, and subject to structural breaks and regime shifts. We develop a fully automated, reproducible, and leakage-controlled multi-model pipeline for daily forecasting with horizon-specific configuration selection. The task is formulated as predicting cumulative [...] Read more.

Short-horizon equity forecasting remains challenging because daily prices are noisy, heavy-tailed, and subject to structural breaks and regime shifts. We develop a fully automated, reproducible, and leakage-controlled multi-model pipeline for daily forecasting with horizon-specific configuration selection. The task is formulated as predicting cumulative H-day log-returns from OHLCV-derived information and converting them to implied price forecasts. All model families share a homologated design: causal feature construction, a strictly chronological split with an explicit purging rule to prevent label-window overlap for multi-day targets, training-only robustification (winsorization and adaptive clipping), and a unified metric suite computed consistently in return and price spaces. The framework benchmarks transparent baselines (zero- and mean-return), gradient-boosted trees (XGBoost), and deep temporal models (LSTM and CNN/TCN). Lookback length $L\in \{60,180,500\}$ is selected via an internal walk-forward procedure on the pre-evaluation block, and final performance is reported on an external hold-out segment (last 15% of instances). Experiments on daily data for MT, DELL, and the S&P 500 index (through 3 February 2026) show that all families achieve similarly strong price-level fit at $H=1$, largely driven by persistence in the price process, while separation across families becomes more visible at $H=5$. However, predictive performance in return space remains weak, with $R^2$ close to zero or negative, and Diebold–Mariano tests do not provide consistent evidence of statistical superiority over naive benchmarks. Under an operational rule that minimizes hold-out RMSE on the price scale, selected models are asset- and horizon-dependent, supporting horizon-wise selection rather than a single global architecture. Overall, the primary contribution lies in the proposed leakage-controlled evaluation and benchmarking framework rather than in demonstrating consistent predictive gains in financial time series forecasting. Full article

(This article belongs to the Topic Modern Challenges and Innovations in Financial Econometrics)

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