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Advances in Financial Econometrics
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Advances in Financial Econometrics

A special issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4698

Special Issue Editor

Dr. Elisa Ossola

E-Mail Website
Guest Editor
Department of Economics, Management and Statistics (DEMS), Università degli Studi di Milano Bicocca, 1, 20126 Milan, Italy
Interests: financial econometrics; factor models; large-scale panels; asset pricing; sustainable finance; ESG indicator

Special Issue Information

Dear Colleagues,

Financial econometrics as a discipline has grown tremendously since the publication of Campbell, Lo, and MacKinlay (1997). The field now sees intersections with disciplines such as computer science, physics, statistics, and mathematics, reflecting the increasing complexity of financial systems. At the same time, the amount of financial data available has grown enormously, offering new opportunities and challenges.

The International Journal of Financial Studies is pleased to invite high-quality submissions for a Special Issue focused on the Advances of Financial Econometrics. This Special Issue aims to collect and promote research in financial econometrics, both methodological and applied, contributing to a deeper understanding and improved prediction of financial phenomena. We also encourage submissions at the intersection of econometrics and macroeconomics, corporate finance, fintech and blockchain, financial intermediation, and financial microstructure.

We welcome contributions on topics including, but not limited to, the following:

  • Asset price dynamics;
  • Optimal portfolio allocation;
  • High-dimensional financial data analysis;
  • Macro-asset pricing;
  • Firm-level risks;
  • The role of artificial intelligence and machine learning in finance;
  • Modeling and forecasting financial markets, with a focus on emerging financial products in digital and sustainable finance.

Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press. 

Dr. Elisa Ossola
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Financial Studies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • asset pricing
  • financial markets
  • markets, forecasting, and models
  • portfolio allocation
  • large-dimensional data
  • machine learning
  • corporate finance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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27 pages, 5577 KB  
Article
The Risk Premia from the European Equity Market: An Application of the Three-Pass Estimation Methodology
by Elisa Ossola and Irina Trifan
Int. J. Financial Stud. 2026, 14(4), 96; https://doi.org/10.3390/ijfs14040096 - 8 Apr 2026
Viewed by 441
Abstract
We develop an empirical application on a large dataset of European stock returns in order to estimate the risk premia. While traditional factor models often struggle with high levels of pricing errors and noisy proxies in fragmented markets, we show that the Three-Pass [...] Read more.
We develop an empirical application on a large dataset of European stock returns in order to estimate the risk premia. While traditional factor models often struggle with high levels of pricing errors and noisy proxies in fragmented markets, we show that the Three-Pass Estimation Method (3PEM) serves as both a robust estimator and a diagnostic tool for factor purification. By assuming the Fama–French five-factor model as the baseline model, we first show that the 3PEM yields risk premium estimates for the European market that are more economically plausible and statistically robust than those obtained using the traditional two-pass estimation method (2PEM). Moreover, our results show that the 3PEM is able to detect noise in tradable factors. Furthermore, the 3PEM is used to denoise the observed factors, providing purified versions that better capture the systematic components of risk. We also identify both noisy factors and denoised factor series that improve the estimation of stock-level exposures and expected returns. Full article
(This article belongs to the Special Issue Advances in Financial Econometrics)
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25 pages, 882 KB  
Article
Do Technical Indicators Enhance the Predictability of the Equity Market Risk Premium? Evidence from Korea
by Hyunah Lee and Sungju Chun
Int. J. Financial Stud. 2026, 14(4), 78; https://doi.org/10.3390/ijfs14040078 - 31 Mar 2026
Viewed by 525
Abstract
Prior empirical studies suggest that technical indicators may contain information useful for predicting the equity market risk premium and may complement forecasting models based on macroeconomic variables. This paper examines the predictive power of technical indicators in conjunction with macroeconomic variables in the [...] Read more.
Prior empirical studies suggest that technical indicators may contain information useful for predicting the equity market risk premium and may complement forecasting models based on macroeconomic variables. This paper examines the predictive power of technical indicators in conjunction with macroeconomic variables in the Korean market, focusing on whether technical indicators enhance the predictability of the equity market risk premium. Using monthly data from October 2000 to December 2023, this study evaluates the performance of individual variables and groups of macroeconomic variables and/or technical indicators by extracting principal components and estimating predictive regressions. Both in-sample and out-of-sample tests are conducted to assess the economic implications of the principal component predictive regressions. Contrary to findings from the U.S. and China, the results show that technical indicators in Korea exhibit weak predictive power at a monthly frequency when considered in isolation. However, combining technical indicators with macroeconomic variables substantially improves predictability. In-sample regressions based on principal components extracted from the combined information set yield higher explanatory power than models based solely on macroeconomic variables or technical indicators. Out-of-sample results further confirm that incorporating technical indicators into macroeconomic information leads to meaningful gains in forecasting accuracy for the Korean equity market risk premium. Full article
(This article belongs to the Special Issue Advances in Financial Econometrics)
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33 pages, 4726 KB  
Article
Interpretable Deep Learning for REIT Return Forecasting: A Comparative Study of LSTM, TVP–VAR Proxy, and SHAP-Based Explanations
by Eddy Suprihadi, Nevi Danila, Zaiton Ali and Gede Pramudya Ananta
Int. J. Financial Stud. 2026, 14(3), 73; https://doi.org/10.3390/ijfs14030073 - 12 Mar 2026
Viewed by 715
Abstract
Forecasting returns in Real Estate Investment Trust (REIT) markets remains challenging because REIT performance is shaped by nonlinear and time-varying interactions with macro-financial conditions. This study evaluates the forecasting performance of Long Short-Term Memory (LSTM) neural networks relative to a TVP–VAR proxy implemented [...] Read more.
Forecasting returns in Real Estate Investment Trust (REIT) markets remains challenging because REIT performance is shaped by nonlinear and time-varying interactions with macro-financial conditions. This study evaluates the forecasting performance of Long Short-Term Memory (LSTM) neural networks relative to a TVP–VAR proxy implemented as an expanding window VAR for weekly U.S. U.S. REIT returns. All models are assessed within a harmonized experimental framework that applies consistent data preprocessing, feature construction, and strictly time-ordered out-of-sample evaluation. The results indicate that the baseline LSTM model delivers modest but more stable error-based performance than the TVP–VAR proxy, with improvements concentrated in RMSE and MAE, while evidence for directional predictability is weak and not consistently distinguishable from benchmark performance. To enhance transparency, SHapley Additive exPlanations (SHAPs) are used to interpret the LSTM forecasts. The attribution analysis highlights recent REIT returns, global equity indicators—particularly the Hang Seng Index—and crude oil prices as influential predictors, and shows that their contributions vary across volatility regimes, consistent with time-varying spillovers and changing risk transmission. Overall, the study positions LSTM forecasting combined with SHAP-based interpretation as a transparent and reproducible framework for comparative evaluation and driver analysis in weekly REIT returns, rather than as a strong directional timing tool. Full article
(This article belongs to the Special Issue Advances in Financial Econometrics)
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12 pages, 666 KB  
Article
Has IPO Market Structure Fundamentally Changed? Evidence from Negative Binomial Regression with Structural Breaks
by Michael D. Herley
Int. J. Financial Stud. 2026, 14(1), 6; https://doi.org/10.3390/ijfs14010006 - 5 Jan 2026
Viewed by 1127
Abstract
This paper introduces Bai-Perron structural break detection combined with negative binomial regression to model overdispersed U.S. IPO count data. Using monthly data from 1995 to 2024, we identify five breaks that partition IPO activity into six distinct regimes, each with fundamentally different variance [...] Read more.
This paper introduces Bai-Perron structural break detection combined with negative binomial regression to model overdispersed U.S. IPO count data. Using monthly data from 1995 to 2024, we identify five breaks that partition IPO activity into six distinct regimes, each with fundamentally different variance characteristics. We then employ negative binomial regression that incorporates these breaks. IPO data show substantial overdispersion (variance-to-mean ratios: 2.77 to 33.74). The negative binomial model reveals that market uncertainty (as measured by the VIX) and financing costs (as indicated by 10-year Treasury rates) reduce IPO activity, while lagged IPO volume drives activity in the current period. Regime-specific likelihood ratio tests reveal that statistically significant overdispersion first emerges during the 2008 financial crisis, subsides during the post-recession period, and returns with unprecedented intensity after May 2020. An OLS model without the identified structural breaks incorrectly suggests positive interest rate effects. Full article
(This article belongs to the Special Issue Advances in Financial Econometrics)
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Review

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31 pages, 1617 KB  
Review
Creative Accounting Practices and Their Perceived and Actual Impact on Financial Reporting: Evidence from Romanian Listed Companies
by Adriana Horaicu, Victor Munteanu, Marilena-Roxana Zuca, Gabriel Cucui, Luiza Ionescu, Mihaela-Denisa Coman and Aura-Oana Mustățea
Int. J. Financial Stud. 2026, 14(2), 28; https://doi.org/10.3390/ijfs14020028 - 2 Feb 2026
Viewed by 1024
Abstract
This study investigates creative accounting practices and their effects on reported financial position, performance, and risk indicators in Romanian listed companies. Using a mixed research design, the analysis combines a perception-based survey of financial–accounting professionals with a scenario-based financial case study, allowing for [...] Read more.
This study investigates creative accounting practices and their effects on reported financial position, performance, and risk indicators in Romanian listed companies. Using a mixed research design, the analysis combines a perception-based survey of financial–accounting professionals with a scenario-based financial case study, allowing for a comparison between perceived and actual effects of discretionary accounting techniques. The survey results indicate that professionals perceive creative accounting practices as having a significant influence on financial reporting outcomes, particularly in areas characterized by high managerial discretion, such as provisions, depreciation policies, inventory valuation methods, asset revaluation, and capitalization of research and development expenditures. The empirical case study confirms that these techniques generate observable changes in key financial indicators; however, the magnitude and direction of their effects vary across accounting methods and reporting periods. A key contribution of this study lies in highlighting a discrepancy between perceived and measured effects of creative accounting. While practitioners accurately identify the accounting areas most exposed to discretion, the empirical results suggest that the financial impact of creative accounting practices is often more moderate and context-dependent than commonly assumed. In addition, a descriptive assessment of fraud risk indicators suggests that extensive use of discretionary accounting practices may be associated with elevated risk exposure, without constituting direct evidence of fraudulent behavior. Full article
(This article belongs to the Special Issue Advances in Financial Econometrics)
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