Next Article in Journal
A New Family of Consistent and Asymptotically-Normal Estimators for the Extremal Index
Next Article in Special Issue
Forecasting Interest Rates Using Geostatistical Techniques
Previous Article in Journal
A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts
Article Menu

Export Article

Open AccessArticle
Econometrics 2015, 3(3), 610-632; doi:10.3390/econometrics3030610

Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting

New Economic School, 100A Novaya Street, Skolkovo, Moscow, 143026, Russia
Author to whom correspondence should be addressed.
Academic Editors: Fredj Jawadi, Tony S. Wirjanto and Nuttanan Wichitaksorn
Received: 22 June 2015 / Revised: 22 July 2015 / Accepted: 4 August 2015 / Published: 10 August 2015
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
View Full-Text   |   Download PDF [623 KB, uploaded 10 August 2015]   |  


Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the quality of estimation of the volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions are more precisely estimated when no variance targeting is employed. Bias properties are exacerbated for a heavier-tailed distribution of standardized returns, while the distributional asymmetry has little or moderate impact, these phenomena tending to be more pronounced under variance targeting. Some effects further intensify if one uses ML based on a leptokurtic distribution in place of normal QML. The sample size has also a more favorable effect on estimation precision when no variance targeting is used. Thus, if computational costs are not prohibitive, variance targeting should probably be avoided. View Full-Text
Keywords: GARCH; variance targeting; non-normality; heavy tails; skewness; quasi-maximum likelihood GARCH; variance targeting; non-normality; heavy tails; skewness; quasi-maximum likelihood

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Anatolyev, S.; Khrapov, S. Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting. Econometrics 2015, 3, 610-632.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Econometrics EISSN 2225-1146 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top