• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 74
  • 16
  • 1
  • Tagged with
  • 77
  • 77
  • 40
  • 25
  • 25
  • 19
  • 15
  • 14
  • 11
  • 11
  • 10
  • 10
  • 9
  • 9
  • 8
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
31

Statistical inference for FIGARCH and related models. / CUHK electronic theses & dissertations collection

January 2007 (has links)
A major objective of this thesis is to study the statistical inference problem for GARCH-type models, including fractionally-integrated (FI) GARCH, fractional (F) GARCH, long-memory (LM) GARCH, and non-stationary GARCH models. / Among various types of generalizations to the ARCH models, fractionally-integrated (FI) GARCH model proposed in Baillie et al. (1996) and Bollerslev and Mikkelson (1996) is one of the most interesting ones as it offered many challenging theretical problems. / Parameters in the ARCH-type models are commonly estimated using the quasi-maximum likelihood estimator (QMLE). To establish consistency and asymptotic normality of the QMLE, one usually has to impose stringent assumptions, see Robinson and Zaffaroni (2006) and Straumann (2005). They have to assume that a stationary solution to the true model exists and this solution has some finite moments. These two assumptions are too restrictive to be applied to FIGARCH models. Formal results of the asymptotic properties of the QMLE of the FIGARCH models are still not available. Progresses on asymptotic theory of QMLE have only been made on certain models that resemble the FIGARCH model, including the FGARCH model of Ding and Granger (1996) and Robinson and Zaffaroni (2006), the LM-GARCH model of Robinson and Zaffaroni (1997) and the non-stationary ARCH model, but not the FIGARCH model itself. / This study attempts to solve the FIGARCH problem and extend the current findings on FGARCH, LM-GARCH and non-stationary GARCH models. We show that if the fractional parameter d is known, the QMLE for the parameters are strongly consistent and asymptotically normal. The results of LM-GARCH (0, d, 0) model in Konlikov (2003a,b) will be generalized to encompass the LM-GARCH(p, d, q) models. We also furnish a general result for non-stationary GARCH (p, q) models, extending the results of Jensen and Rahbek (2004) on weak consistency and asymptotic normality of the QMLE of the non-stationary GARCH (1, 1) models. / Ng, Chi Tim. / "June 2007." / Adviser: Chan Ngai Hang. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0398. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references. / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
32

Fully modified least squares estimation and vector autoregression of models with seasonally integrated processes.

January 1997 (has links)
by Gilbert Chiu-sing Lui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 112-117). / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Models and Assumptions --- p.4 / Chapter 3. --- Asymptotics of FM-SEA Estimators --- p.15 / Chapter 3.1. --- Model without Determinstic Trends --- p.15 / Chapter 3.2. --- Model with Determinstic Trends --- p.27 / Chapter 4. --- Asymptotics of FM-SEA Estimators of VAR System --- p.33 / Chapter 4.1. --- General Model --- p.33 / Chapter 4.2. --- Model with d = 4 --- p.44 / Chapter 5. --- Monte Carlo Experimental Results --- p.49 / Chapter 6. --- Conclusion --- p.54 / Chapter 7. --- Mathematical Appendix --- p.56 / Chapter 8. --- References --- p.112
33

A sensitivity study on identification schemes of the structural vector autoregression /

Zhang, Wei, January 2001 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 107-109). Also available on the Internet.
34

Comparison of estimates of autoregressive models with superimposed errors

Chong, Siu-yung. January 2001 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 89-94).
35

A sensitivity study on identification schemes of the structural vector autoregression

Zhang, Wei, January 2001 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 107-109). Also available on the Internet.
36

Bank equity and the monetary transmission mechanism /

Sumner, Steven W. January 2003 (has links)
Thesis (Ph. D.)--University of California, San Diego, 2003. / Vita. Includes bibliographical references.
37

Statistical inference for the APGARCH and threshold APGARCH models

Chen, Qiming, 陈启明 January 2011 (has links)
published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
38

Comparison of estimates of autoregressive models with superimposed errors

莊少容, Chong, Siu-yung. January 2001 (has links)
published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
39

Optimal asset allocation under GARCH model

許偉才, Hui, Wai-choi. January 2000 (has links)
published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
40

An examination of stock market properties : vector autoregression approach /

Jeon, Kyung-Seong, January 1997 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 147-152). Also available on the Internet.

Page generated in 0.1041 seconds