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Working Paper
Regular Variation of Popular GARCH Processes Allowing for Distributional Asymmetry
Linear GARCH(1,1) and threshold GARCH(1,1) processes are established as regularly varying, meaning their heavy tails are Pareto like, under conditions that allow the innovations from the, respective, processes to be skewed. Skewness is considered a stylized fact for many financial returns assumed to follow GARCH-type processes. The result in this note aids in establishing the asymptotic properties of certain GARCH estimators proposed in the literature.
Working Paper
Soft Landing or Stagflation? A Framework for Estimating the Probabilities of Macro Scenarios
Amid ongoing trade policy shifts and geopolitical uncertainty, concerns about stagflation have reemerged as a key macroeconomic risk. This paper develops a probabilistic framework to estimate the likelihood of stagflation versus soft landing scenarios over a four-quarter horizon. Building on Bekaert, Engstrom, and Ermolov (2025), the model integrates survey forecasts, structural shock decomposition, and a non-Gaussian BEGE-GARCH approach to capture time-varying volatility and skewness. Results suggest that the probability of stagflation was elevated at around 30 percent in late 2022, while ...
Working Paper
Financial Stress and Equilibrium Dynamics in Money Markets
Interest rate spreads are widely-used indicators of funding pressures and market functioning in money markets. Using weekly data from 2002 to 2015, we analyze money market dynamics in a long-run equilibrium framework where commonly-monitored spreads serve as error correction terms. We find strong evidence for nonlinearities with respect to levels of the spreads. We provide point and interval estimates for spread thresholds that quantify funding pressure points from a long-run perspective. Our results indicate significant asymmetry in the adjustment toward long-run equilibrium. We show that ...
Working Paper
When Tails Are Heavy: The Benefits of Variance-Targeted, Non-Gaussian, Quasi-Maximum Likelihood Estimation of GARCH Models
In heavy-tailed cases, variance targeting the Student's-t estimator proposed in Bollerslev (1987) for the linear GARCH model is shown to be robust to density misspecification, just like the popular Quasi-Maximum Likelihood Estimator (QMLE). The resulting Variance-Targeted, Non-Gaussian, Quasi-Maximum Likelihood Estimator (VTNGQMLE) is shown to possess a stable limit, albeit one that is highly non-Gaussian, with an ill-defined variance. The rate of convergence to this non-standard limit is slow relative √n and dependent upon unknown parameters. Fortunately, the sub-sample bootstrap is ...