Anatomy of a Sovereign Debt Crisis: Machine Learning, Real-Time Macro Fundamentals, and CDS Spreads
使用LASSO扩展的Fama-MacBeth方法，研究2009-2020年主权CDS利差对宏观指标的时变依赖，发现利差主要反映基本面但关系随时间大幅变化，并识别出与多重均衡一致的宏观敏感性区间。
Abstract We employ a Least Absolute Shrinkage and Selection Operator (LASSO)-based extension of the Fama–MacBeth procedure to characterize the time-varying dependence of sovereign Credit Default Swap (CDS) spreads on macro indicators during the samples 2009–2013 and 2013–2020. While CDS spreads are mainly reflective of fundamentals, this relationship varies substantially over time, leading to price variation that appears unrelated to fundamentals. The estimated LASSO coefficients are used to endogenously identify macro-sensitivity “regimes” of variation, consistently with a multiple-equilibrium view of the sovereign debt markets.