FEDS Paper: Linear and Nonlinear Econometric Models versus Machine-Learning Models: Evidence from Realized-Volatility Forecasting(Revised)

Rehim KilicThis paper examines which representations of persistence and nonlinearity are most useful for forecasting realized volatility and whether machine learning adds value beyond econometric models designed for long memory and regime dependence. We compare HAR, ARFIMA, threshold HAR, smooth-transition HAR, and Markov-switching HAR with XGBoost and several neural-network models for the S&P 500 and 40 U.S. equities.

FEDS Paper: Risks and Uncertainty in Monetary Policy

Tobias Adrian, Domenico Giannone, Matteo Luciani, and Mike WestCentral banks monitor macroeconomic risk through two traditions: scenario analysis, regularly used since the mid-1990s, and distributional forecasting, practiced since the late 1960s. The two are complementary but separate: scenarios provide narratives without probabilities, while predictive distributions provide probabilities with limited economic interpretation.

Pages

Subscribe to Front page feed