Conditional Euro Area Sovereign Default Risk

By André Lucas, B. Schwaab, Xin Zhang. 2014

229 citations recorded by the source corpus.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/c6e516d79a518ed4ad0338ed5afa624d0c35bf23

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Citations in the corpus

Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads (2018, methodology): “The first measure of systemic risk we implement is an estimate of the probability that a large number of firms will be in distress, similar to the measure considered by Segioviano and Goodhart (2009), Giesecke and Kim (2011) and Lucas, Schwaab, and Zhang (2014).”
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models (2016, unknown): “2 The ML Estimator (MLE) for spatial models with static dependence parameter was first studied in Ord (1975) in the context of cross-sectional data sets. Lee (2004) derives asymptotic properties of the QML Estimator (QMLE) for n → ∞, and Hillier and Martellosio (2013) investigate its finite sample d”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “Unlike Creal, Koopman, and Lucas (2011), Lucas, Schwaab, and Zhang (2014), and Hansen, Janus, and Koopman (2016), our score is a matrix-valued rather than a vector-valued variable.”
Evaluating the impact of unconventional monetary policy measures: Empirical evidence from the ECB׳s Securities Markets Programme (2016, background): “However, the SMP purchases caused the five-year CDS spreads to move as well (Lucas, Schwaab, and Zhang, 2014), possibly reflecting an arbitrage relationship between the two (Duffie, 1999).”
Time‐Varying Transition Probabilities for Markov Regime Switching Models (2017, background): “For example, Creal et al. (2011) and Lucas et al. (2014) study dynamic volatilities and correlations under fat tails and possible skewness; Harvey and Luati (2014) introduce new models for dynamic changes in levels under fat tails; Creal et al. (2014) investigate score-based mixed measurement…”
Forecasting: theory and practice (2020, methodology): “Time-varying copula construction is possible via (i) an autoregressive or GARCH form of dependence parameters (Patton, 2006a; Lucas et al., 2014), (ii) factor copula construction (Oh and Patton, 2018; Tan et al.”