Premium
Density Forecasting with Time‐Varying Higher Moments: A Model Confidence Set Approach
Author(s) -
Wilhelmsson Anders
Publication year - 2013
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1246
Subject(s) - kurtosis , skewness , econometrics , index (typography) , variance (accounting) , set (abstract data type) , economics , conditional variance , statistics , mathematics , computer science , autoregressive conditional heteroskedasticity , accounting , world wide web , programming language , volatility (finance)
Density forecasts contain a complete description of the uncertainty associated with a point forecast and are therefore important measures of financial risk. This paper aims to examine whether the new more complicated models for financial returns that allow for time variation in higher moments lead to better out‐of‐sample density forecasts. Using two decades of daily Standard & Poor's 500 index returns I find that a model with time‐varying conditional variance, skewness and kurtosis produces significantly better density forecasts than the competing models. Copyright © 2011 John Wiley & Sons, Ltd.