ORA Thesis: "Essays in panel data and financial econometrics" - uuid:c970f380-9644-4439-a272-7427ef66ac44

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Reference: Cavit Pakel, (2012). Essays in panel data and financial econometrics. DPhil. University of Oxford.

Citable link to this page: http://ora.ox.ac.uk/objects/uuid:c970f380-9644-4439-a272-7427ef66ac44
 
Title: Essays in panel data and financial econometrics

Abstract:

This thesis is concerned with volatility estimation using financial panels and bias-reduction in non-linear dynamic panels in the presence of dependence.

Traditional GARCH-type volatility models require large time-series for accurate estimation. This makes it impossible to analyse some interesting datasets which do not have a large enough history of observations. This study contributes to the literature by introducing the GARCH Panel model, which exploits both time-series and cross-section information, in order to make up for this lack of time-series variation. It is shown that this approach leads to gains both in- and out-of-sample, but suffers from the well-known incidental parameter issue and therefore, cannot deal with short data either. As a response, a bias-correction approach valid for a general variety of models beyond GARCH is proposed. This extends the analytical bias-reduction literature to cross-section dependence and is a theoretical contribution to the panel data literature. In the final chapter, these two contributions are combined in order to develop a new approach to volatility estimation in short panels. Simulation analysis reveals that this approach is capable of removing a substantial portion of the bias even when only 150-200 observations are available. This is in stark contrast with the standard methods which require 1,000-1,500 observations for accurate estimation. This approach is used to model monthly hedge fund volatility, which is another novel contribution, as it has hitherto been impossible to analyse hedge fund volatility, due to their typically short histories. The analysis reveals that hedge funds exhibit variation in their volatility characteristics both across and within investment strategies. Moreover, the sample distributions of fund volatilities are asymmetric, have large right tails and react to major economic events such as the recent credit crunch episode.


Digital Origin:Born digital
Type of Award:DPhil
Level of Award:Doctoral
Awarding Institution: University of Oxford
Notes:This thesis is not currently available via ORA.
About The Authors
institutionUniversity of Oxford
facultySocial Sciences Division - Economics
oxfordCollegeUniversity College
 
Contributors
Prof Neil Shephard More by this contributor
RoleSupervisor
 
Bibliographic Details
Issue Date: 2012
Copyright Date: 2012
Identifiers
Urn: uuid:c970f380-9644-4439-a272-7427ef66ac44
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Member of collection : ora:thesis
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Copyright Holder: Cavit Pakel
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