Universität zu Köln

Kruse, Robinson, Leschinski, Christian and Will, Michael (2019). Comparing Predictive Accuracy under Long Memory, With an Application to Volatility Forecasting. J. Financ. Econom., 17 (2). S. 180 - 229. OXFORD: OXFORD UNIV PRESS. ISSN 1479-8417

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Abstract

This article extends the popular Diebold-Mariano test for equal predictive accuracy to situations when the forecast error loss differential exhibits long memory. This situation can arise frequently since long memory can be transmitted from forecasts and the forecast objective to forecast error loss differentials. The nature of this transmission depends on the (un)biasedness of the forecasts and whether the involved series share common long memory. Further theoretical results show that the conventional Diebold-Mariano test is invalidated under these circumstances. Robust statistics based on a memory and autocorrelation consistent estimator and an extended fixed-bandwidth approach are considered. The subsequent extensive Monte Carlo study provides numerical results on various issues. As empirical applications, we consider recent extensions of the HAR model for the S&P500 realized volatility. While we find that forecasts improve significantly if jumps are considered, improvements achieved by the inclusion of an implied volatility index turn out to be insignificant.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Kruse, Robinson UNSPECIFIED UNSPECIFIED UNSPECIFIED Leschinski, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Will, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-154450
DOI:	10.1093/jjfinec/nby011
Journal or Publication Title:	J. Financ. Econom.
Volume:	17
Number:	2
Page Range:	S. 180 - 229
Date:	2019
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1479-8417
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language STATIONARY FRACTIONAL COINTEGRATION; REALIZED VOLATILITY; ASYMPTOTIC INFERENCE; IMPLIED VOLATILITY; HETEROSKEDASTICITY; MODEL; TESTS; STOCK; RISK; FREQUENCY Multiple languages Business, Finance; Economics Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/15445