Financial institutions

FEDS Paper: Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting

Hyung Joo Kim and Dong Hwan OhDespite documented heterogeneity in volatility dynamics across the option surface, standard implied volatility forecasting models apply homogeneous parameters throughout. We introduce a machine-learning framework that uses regression trees to partition the surface along both moneyness and maturity dimensions, identifying data-driven regions where distinct forecasting models perform best.

FEDS Paper: Cyclical Fluctuations, Financial Frictions, and Productivity Differences across Firms

Luca Guerrieri, Jinill Kim, and Arsenii MishinWithin narrowly defined industries, the most productive firms produce far more than the least productive from the same inputs, and this dispersion widens in downturns. We build a tractable representative-agent model in which financial frictions—adverse selection and moral hazard—make firms sort endogenously into lenders, strategic defaulters, and producers.

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