Long memory models for daily and high frequency commodity futures returns

2007 ◽  
Vol 27 (7) ◽  
pp. 643-668 ◽  
Author(s):  
Richard T. Baillie ◽  
Young-Wook Han ◽  
Robert J. Myers ◽  
Jeongseok Song
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Memory Models ◽  
Commodity Futures

Long memory in commodity futures volatility: A wavelet perspective

2007 ◽  
Vol 27 (5) ◽  
pp. 411-437 ◽  
Author(s):  
John Elder ◽  
Hyun J. Jin
Keyword(s):  
Long Memory ◽  
Commodity Futures

scholarly journals Estimation of mis-specified long memory models

2006 ◽  
Vol 134 (1) ◽  
pp. 257-281 ◽  
Author(s):  
Willa W. Chen ◽  
Rohit S. Deo
Keyword(s):  
Long Memory ◽  
Memory Models

ON PLUG-IN ESTIMATION OF LONG MEMORY MODELS

2005 ◽  
Vol 21 (02) ◽  
Author(s):  
Offer Lieberman
Keyword(s):  
Long Memory ◽  
Memory Models

scholarly journals Estimating dynamic copula dependence using intraday data

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
High Frequency ◽  
Time Varying ◽  
Credit Crisis ◽  
Dynamic Copula ◽  
Tick Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Better Than

AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

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