Autoregressive Conditional Density Estimation

1994/08/01 by Bruce E. Hansen · 1,628 citations
Economics, Econometrics and Finance · Mathematics · #Autoregressive conditional heteroskedasticity #Autoregressive model #Complex Systems and Time Series Analysis #Conditional expectation #Conditional probability distribution #Conditional variance #Econometrics #Economics #Finance #Financial Risk and Volatility Modeling #Geography #Heteroscedasticity #Liberian dollar #Mathematics #Monetary Policy and Economic Impact #Parametric statistics #Statistics #Treasury #Variance (accounting) #Volatility (finance)

paper · doi:10.2307/2527081

published in International Economic Review 35(3), 705 (Wiley)

openalex publication_date 1994/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

R. F. Engle's autoregressive conditional heteroskedastic model is extended to permit parametric specifications for conditional dependence beyond the mean and variance. The suggestion is to model the conditional density with a small number of parameters, and then model these parameters as functions of the conditioning information. This method is applied to two data sets. The first application is to the monthly excess holding yield on U.S. Treasury securities, where the conditional density used is a Student's t distribution. The second application is to the U.S. Dollar/Swiss Franc exchange rate, using a new skewed Student t conditional distribution. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Citations

• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model

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