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 Student at Gies College of Business, UIUC. I am on the 2024-2025 job market. My research focuses on asset pricing, both theoretically and empirically. My job market paper investigates economic and asset pricing implications of firms' network and idiosyncratic volatility spillover. I also work on machine learning in finance, bond ETF market and dealers' network.

Download my CV (updated 2024/09).

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 dynamic evolution of inter-sector idiosyncratic volatility spillovers, induced by production-based input-output connections. The distribution of these spillovers is characterized by two network factors: Risk-Dominance, which measures the concentration of spillovers among sectors, and Risk-Intensity, which quantifies the average spillover magnitude in the economy. Higher Risk-Intensity and lower Risk-Dominance hinder risk diversification and increase aggregate volatility. Empirically, I construct these factors using stock data. I demonstrate their predictability for aggregate volatility and show they are priced as implied by the model. Long-short portfolios based on Risk-Dominance and Risk-Intensity betas generate annual return spreads of +5% and -4%, respectively.

 Download the latest version
Last updated on Oct 12, 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
Networks Factors and Macroeconomic Tail Risk

(Draft to be updated) This paper examines how idiosyncratic risks translate into macroeconomic tail risk through the dynamic evolution of production-based input-output networks. Structural changes in the network disrupt the Generalized Central Limit Theorem, which impedes risk diversification across firms, delays the decay of aggregate volatility to its mean, and leads to the clustering of large macroeconomic tails over time. We show that a dynamic input-output network, combined with low elasticity of input substitution, is sufficient to transform normally distributed idiosyncratic risks into significant macroeconomic tail risk.

Networks Factors and Macroeconomic Tail Risk
Price Manipulation in Corporate Bond ETFs

(Draft to be updated)

Price Manipulation in Corporate Bond ETFs

Contact

Committee