Belinda Chen (陈辰)

Belinda Chen (陈辰)

Ph.D. student in Finance

University of Illinois at Urbana-Champaign

I am a 5th year Finance PhD candidate at Gies College of Business, UIUC. I am on the 2024-2025 job market and will be attending the AFA in January. My research lies at the intersection of macro finance, financial econometrics, and asset pricing. My job market paper, which combines theoretical and empirical analysis, investigates the economic and asset pricing implications of production-based networks and idiosyncratic volatility spillovers. I also work on machine learning in finance, bond ETF market and dealers' network.

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Education

  • Ph.D. in Finance, 2020 - 2025 (expected)

    University of Illinois at Urbana-Champaign

  • MS in Applied Mathematics, 2019 - 2020

    University of Chicago

  • BS in Mathematics, 2015 - 2019

    University of Chinese Academy of Sciences

  • Exchange Program, 2018

    Carnegie Mellon University

Interests
  • Asset Pricing
  • Financial Networks
  • Idiosyncratic/Aggregate Volatility
  • Machine Learning in Finance
  • Bond ETF Market

Job Market Paper

Network Factors for Idiosyncratic Volatility Spillover
I develop a multi-sector model to examine the dynamics of inter-sector idiosyncratic volatility spillovers within a supplier-customer network. I characterize dynamic structure changes of pairwise risk spillovers using two well-defined factors: Risk-Intensity and Risk-Dominance, and demonstrate their impact on aggregate volatility. Risk-Intensity captures the average magnitude of risk spillovers over time, with higher Intensity facilitating the amplification of sector-specific shocks through the network and increasing aggregate volatility. Risk-Dominance measures the extent to which a few sectors dominate risk spillovers. Higher Dominance reflects more limited shock transmission paths, promoting faster risk diversification and quicker mean reversion of aggregate volatility. Empirically, I construct these factors using stock data and demonstrate their predictability for aggregate volatility. I also show that they are priced consistently with the model’s implications: long-short portfolios formed based on Risk-Intensity and Risk-Dominance betas yield annual return spreads of -4% and +5%, respectively.

 See the latest version
Last updated on Nov 18, 2024 0 min read
Network Factors for Idiosyncratic Volatility Spillover

Working Papers

Attention-based Graph Neural Networks in Firm CDS Prediction

Credit Default Swap (CDS) spreads exhibit network effects due to firms' default interdependence. This paper employs Graph Neural Networks (GNNs) to predict CDS spreads by modeling firms as nodes and idiosyncratic volatility spillover measures as directed edges. GNNs capture inter-firm network dynamics, improving prediction accuracy by over 50% compared to traditional models without edge features. We enhance the GNN with node- and edge-attention layers, identifying key nodes (e.g., manufacturing and intermediary firms) and edges (e.g., connections between intermediary, retail trade, or information firms and other firms) as critical to CDS spread prediction.

Attention-based Graph Neural Networks in Firm CDS Prediction
Network Dynamics and Macroeconomic Tail Risk

This paper examines the microfoundation of macroeconomic tail risk, defined as the frequency of extremely negative GDP downturns relative to what is predicted by a normal distribution. We demonstrate that firm-level productivity shocks, even when normally distributed, can be amplified into large macroeconomic tail events through a dynamic production-based input-output network, challenging the traditional view that only sufficiently large firm-level shocks impact the aggregate market. The evolving network structure, driven by technological shocks, hinders risk diversification across firms, delays the mean reversion of aggregate volatility, and causes volatility to cluster from local to global scales over time. In bad times, when idiosyncratic risks are high and firms treat all inputs as complementary, these risk spillovers can accumulate, resulting in severe GDP downturns.

Network Dynamics and Macroeconomic Tail Risk

Work in Progress

  • Price Manipulation in Corporate Bond ETFs
    (with Mahyar Kargar, Sébastien Plante, and John Shim)

Contact

Committee