文档介绍：Long memory models for volatility and high frequency financial
data econometrics
Dmitri Koulikov
Department of Economics
School of Economics and Management
University of Aarhus
Aarhus C, 8000, Denmark
phone: +45 89421577
e-mail: ******@
PhD dissertation submitted to
The Faculty of Social Sciences
University of Aarhus
Completed under supervision of
Professor Niels Haldrup, University of Aarhus
and
Professor Bent Jesper Christensen, University of Aarhus
June 11, 2004
i
Contents
Preface iii
Summary iii
Dask resum´e iv
Chapter 1 Modeling sequences of long memory non-negative covariance
stationary random variables 1
Chapter 2 Long memory ARCH(∞) models: specification and
quasi–maximum likelihood estimation 26
Chapter 3 Non–stationary models for volatility of speculative returns:
with application to foreign exchange data 53
Chapter 4 Conditional heteroscedasticity model for discrete high-frequency
price changes: with application to IBM trades data 73
ii
1 Preface
I wish to thank Niels Haldrup and Bent Jesper Christensen, my PhD thesis supervisors, for their
effort and advice during the period of my study at the Department of Economics, University
of Aarhus. I also wish to use this opportunity to express my gratitude to Niels Haldrup
for stimulating my interest in long memory time series analysis back in 1999, while he was
supervising my masters thesis. I am greatly indebted to Svend Hylleberg for supporting my
masters education at the Department of Economics in 1998–1999, and for his help during my
PhD studies in 2000–2003.
In addition, I would like to thank my teachers and colleagues from the University of Tartu
and Euro Faculty: Raul Eamets, Arne Gotfredsen, Jens A. Larsen, Helje Kaldaru, Tiiu Paas,
Alf Vanags, Morten Hansen and h Smith for inspiring and supporting my early inroads
into the field of econometric and economic research.
2 Summary
This thesis presents contribution to the branch of e