Modeling and Forecasting of Financial Markets

A special issue of Economies (ISSN 2227-7099). This special issue belongs to the section "Macroeconomics, Monetary Economics, and Financial Markets".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 422

Special Issue Editor


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Guest Editor
Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Interests: market microstructure; empirical finance; international finance
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Special Issue Information

Dear Colleagues,

This Special Issue aims to present contemporary advances in the modeling and forecasting of financial markets, offering a platform for empirical research that enhances our understanding of market behavior, and improves decision-making in finance. As financial systems become more complex and data-rich, the need for robust forecasting tools and accurate models has never been more critical, especially for academics, policymakers, and practitioners alike.

The focus of this Special Issue lies in exploring innovative approaches for financial market analysis, including time-series econometrics, machine learning, artificial intelligence, and other quantitative methods. Emphasis will be placed on both traditional financial indicators and alternative data sources, covering diverse markets such as equities, fixed income, commodities, and foreign exchange.

The scope of this Special Issue includes topics such as volatility modeling, return predictability, market microstructure, risk forecasting, and algorithmic trading strategies. Interdisciplinary and data-driven contributions that provide novel insights or practical forecasting applications are particularly encouraged.

This Special Issue is situated within a well-established literature on financial econometrics and market predictability. However, it distinguishes itself by addressing recent methodological advancements and the growing role of big data, thus bridging classical theories with modern computational techniques.

Prof. Dr. Kentaro Iwatsubo
Guest Editor

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Keywords

  • time-series econometrics
  • machine learning
  • artificial intelligence
  • forecasting
  • algorithmic trading strategies

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Published Papers (1 paper)

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Research

33 pages, 352 KB  
Article
The Weakest Link: Sibling Dynamics and Bank Failures in Multi-Bank Holding Companies
by Nilufer Ozdemir
Economies 2026, 14(2), 43; https://doi.org/10.3390/economies14020043 - 30 Jan 2026
Viewed by 203
Abstract
This paper examines bank failures during the subprime mortgage crisis, emphasizing sibling dynamics within multi-bank holding companies (MBHCs). While traditional risk indicators effectively predict failures for one bank holding companies (OBHCs), they exhibit limited explanatory power for MBHCs, where internal capital markets and [...] Read more.
This paper examines bank failures during the subprime mortgage crisis, emphasizing sibling dynamics within multi-bank holding companies (MBHCs). While traditional risk indicators effectively predict failures for one bank holding companies (OBHCs), they exhibit limited explanatory power for MBHCs, where internal capital markets and interdependencies across affiliates shape risk outcomes. We extend the standard failure framework by incorporating group-level characteristics that capture sibling network structure and the distribution of risk across affiliates. Using pre-crisis data from 2006 to 2007, we show that group structure significantly influences failure risk. Larger sibling networks reduce individual bank failure risk through diversification, while greater size dispersion across affiliates increases vulnerability by constraining internal resource allocation. Beyond these aggregate effects, we introduce a weakest link approach that identifies the most distressed affiliate based on extreme tail risk in capitalization, asset quality, liquidity, earnings, and income volatility, capturing organizational fragility that aggregate measures miss. Concentrated vulnerabilities at a single affiliate significantly amplify failure risk throughout the holding company, even after controlling for traditional bank-level fundamentals and parent-level characteristics. These findings, derived from the 2007–2010 crisis, a severe stress test of holding company structures, identify organizational dynamics: resource competition among siblings and concentrated vulnerabilities at the weakest affiliate. Supervisory frameworks should explicitly account for within-group interdependencies rather than relying solely on individual bank metrics or aggregate indicators when monitoring bank holding company structures. Full article
(This article belongs to the Special Issue Modeling and Forecasting of Financial Markets)
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