Typically, point forecasting methods are compared and assessed by means of an error
measure or scoring function, with the absolute error and the squared error being key …

Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score
based on the predictive distribution and on the event or value that materializes. A scoring …

Forecast combinations have frequently been found in empirical studies to produce better
forecasts on average than methods based on the ex ante best individual forecasting model …

We propose a framework for out‐of‐sample predictive ability testing and forecast selection
designed for use in the realistic situation in which the forecasting model is possibly …

Fully revised and restructured, Measuring Market Risk, Second Edition includes a new
chapter on options risk management, as well as substantial new information on parametric …

The Second Edition of this best-selling book expands its advanced approach to financial risk
models by covering market, credit, and integrated risk. With new data that cover the recent …

We propose a method for comparing density forecasts that is based on weighted versions of
the continuous ranked probability score. The weighting emphasizes regions of interest, such …

Value at Risk (VaR) forecasts can be produced from conditional autoregressive VaR
models, estimated using quantile regression. Quantile modeling avoids a distributional …

This paper studies a quantile regression dynamic panel model with fixed effects. Panel data
fixed effects estimators are typically biased in the presence of lagged dependent variables …

Volatility has been one of the most active and successful areas of research in time series
econometrics and economic forecasting in recent decades. This chapter provides a selective …