Staff View
Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization

Descriptive

TitleInfo
Title
Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization
Name (type = personal)
NamePart (type = family)
Yan
NamePart (type = given)
Xi
NamePart (type = date)
1985-
DisplayForm
Xi Yan
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Chen
NamePart (type = given)
Rong
DisplayForm
Rong Chen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Xiao
NamePart (type = given)
Han
DisplayForm
Han Xiao
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Tan
NamePart (type = given)
Zhiqiang
DisplayForm
Zhiqiang Tan
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Lin
NamePart (type = given)
Ming
DisplayForm
Ming Lin
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact); (encoding = w3cdtf); (keyDate = yes)
2019
DateOther (type = degree); (qualifier = exact); (encoding = w3cdtf)
2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
This dissertation focuses on developing new statistical methods for analyzing and modeling financial time series. The first part of this dissertation discusses modeling of functional and distributional time series, assuming the series are driven by a finite dimensional underlying feature process. Functional time series are commonly observed in finance and difficult to model mainly due to high dimensionality. We instead focus on a low-dimensional latent feature process and connect it with the original functional time series through generalized state-space models. A state-space model assumes that observations are driven by an underlying dynamic state process and is widely used in many fields. We propose a generalized two-stage Sequential Monte Carlo (SMC) joint estimation framework to model functional time series driven by its feature process through state-space models and perform on-line estimations and predictions. In order to improve computation efficiency, we also implement parallel computing and re-design computation algorithms to integrate with non-linear optimization and SMC calculations.

Two financial applications are presented to demonstrate the robustness and efficiency of our proposed framework. The first application aims to extract and model the daily implied risk neutral densities from observed call option prices. We view the underlying risk neutral density as a functional time series driven by its feature process and model it with a parametric mixed log-normal distribution through a state-space model. We conduct both simulation and empirical studies and compare prediction performance of our models with that of random walk models. Empirically, the proposed models improves prediction performance significantly. The second financial application studies daily cross-sectional distribution of 1000 largest market capitalization stock returns from year 1991 to year 2002. Similarly we view cross-sectional distribution as a functional time series driven by its feature process and model it with a four-parameter generalized skewed $t$-distribution. Using proposed two-stage SMC joint estimation framework, we build models separately for different market conditions, including the dot-com crisis. In both bearish and bullish markets, prediction performances of our models gain substantial improvement comparing with random walk models.

The second part of dissertation presents a new portfolio optimization strategy for minimum variance portfolios with constraints on short-sale and transaction costs. Unlike traditional mean-variance theory, our method minimizes only the portfolio risk and uses analysts' consensus ratings to pre-screen stocks. An empirical study of S&P 500 stocks from year 1990 to year 2009 is conducted to demonstrate effectiveness. We show that portfolios constructed and optimized using our strategy deliver considerable improvement of performance in terms of Sharpe ratio comparing with benchmark portfolios and portfolios in past literatures.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
Subject (authority = local)
Topic
Functional time series
Subject (authority = LCSH)
Topic
Time-series analysis
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10359
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xi, 100 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-nrp3-qq60
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
YAN
GivenName
XI
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-27 18:33:36
AssociatedEntity
Name
XI YAN
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2021-10-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2021.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
ApplicationName
pdfTeX-1.40.18
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-27T18:17:27
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-27T18:17:27
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2020