Vasilis Sarafidis

What's new?

July 2026: I am pleased to share a new working paper “Estimation and Inference for Latent Dual Networks Using High-Dimensional IV Screening” (with Arturas Juodis and George Kapetanios). We develop a novel methodology for estimation and inference in high-dimensional panel network models with latent dual structures. The framework allows outcomes to be affected simultaneously by positive and negative interaction channels, accommodating settings in which some interactions reinforce outcomes while others generate competition and displacement effects. The proposed method identifies and estimates the network directly from the structural model using observed data without the need to pre-specify the network. Network recovery is achieved through a sequential instrumental-variable screening procedure. We establish exact support recovery and oracle-equivalent post-selection inference.

June 2026: On 25 June, I will deliver a distinguished lecture titled Network Effects in Corporate Emissions: Beyond Geography and Industry Boundaries at the 10th EFiC Conference in Banking and Corporate Finance, hosted by Essex Business School on 25–26 June 2026. The lecture is part of the special stream on climate finance, which focuses on pricing, modelling, and regulating climate risk in banking and corporate finance. The conference brings together leading academics, practitioners, and policymakers to present state-of-the-art research in banking, corporate finance, financial regulation, and related areas. 

May 2026: I am pleased to announce a Special Issue on Panel Data Analysis (submission deadline: 20 December 2026), co-edited with Badi Baltagi, Kazuhiko Hayakawa, and Degui Li for Econometrics and Statistics. The Special Issue seeks frontier research featuring significant econometric innovations in panel data analysis, including advances in causal inference, heterogeneous panel models, high-dimensional and machine learning methods, network and spatial econometrics, qualitative response models, quantile regression, robust inference, and threshold models. Contributions should offer substantial methodological, theoretical, or computational advances, with particular encouragement given to original methodological developments motivated by substantive applications.

April 2026: The paper "Residual Income Valuation and Stock Returns: Evidence from a Value-to-Price Investment Strategy" (with Ahmad Haboub and Aris Kartsaklas)  has been accepted for publication at The Financial Review. We hypothesize that sorting portfolios by the V/P ratio yields excess returns by capturing persistently undervalued firms. In the US market, high V/P portfolios outperform low V/P ones over one- to three-year horizons. The V/P ratio predicts future returns even after controlling for standard risk factors. Profitability and investment improve the explanatory power of the Fama-French model, particularly for stocks with V/P near 1, but fail to account fully for excess returns in years two and three, especially among high V/P stocks. These high V/P portfolios identify firms significantly mispriced relative to future investment and profitability growth. 

April 2026: The paper “Network Effects in Corporate Emissions: Evidence from a Data-Dependent Spatial Panel Model” (with Stylianos Asimakopoulos, George Kapetanios and Alexia Ventouri) has been accepted for publication in the Journal of Money, Credit and Banking. We study spillover effects in corporate toxic emissions using a heterogeneous panel network of U.S. industrial facilities during 2000–2023. Rather than imposing a network structure a priori, we uncover an unobserved web of influence directly from the data using recent advances in high-dimensional network econometrics. Indirect effects transmitted through the estimated network account for around 28% of the total impact of key firm balance-sheet characteristics. By contrast, distance-based networks generate no statistically discernible spillovers, while a priori firmor industry-based networks substantially overstate within-group spillins relative to the data-driven network. 

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