Abstract/Details

Extreme risk analysis of dynamic interdependent infrastructures and sectors of the economy with applications to financial modeling


2006 2006

Other formats: Order a copy

Abstract (summary)

This dissertation is built on the Inoperability Input-Output Model (IIM), which is an extension to the original Nobel-Prize winning Leontief Input-Output (I-O) model. The IIM transforms the I-O model to an inoperability form so that the ripple effects of an initial perturbation caused by disasters or attacks can be calculated. A dynamic extension to the IIM is introduced in this dissertation. The Dynamic Inoperability Input-Output Model (DIIM) incorporates the concept of industry resilience coefficient to measure the pace of recovery for each industrial sector on the event of an attack. With the DIIM, the recovery paths of the economic sectors are described as functions of time. Inoperability, economic loss, and cumulative economic loss of any sector can be calculated based on the DIIM. In this dissertation, probabilistic perturbations and recovery times that follow certain statistical distributions such as triangular distribution are applied to the IIM and the DIIM. This allows a more accurate input to the model that comes from either expert opinions or empirical databases. Other important aspects of the IIM and the DIIM are also addressed in the dissertation. In particular, modeling various types of recoveries of indirectly affected sectors is discussed in details depending on specific risk scenarios and characteristics of the economic sectors themselves. On occasions when the structure of the economy is changed due to the disasters or the results of risk management options, an analytical framework is given in the dissertation to show how to change the interdependency matrix accordingly for a more accurate representation of the model. The stochastic extension on the DIIM allows the analyst to capture the randomness of the recovery for each economic sector. Finally, a discussion of connecting the macroeconomics to the financial modeling is presented in the dissertation. In particular, a paradigm of applying the industry-type interdependency information to enhance the estimates of portfolio Value-at-Risk (VaR) under the industrial extreme events is introduced in the dissertation. This method will expand the current VaR method to capture the industry-level interdependency besides the correlations among assets in a portfolio given an extreme event.

Indexing (details)


Subject
Systems design;
Economics;
Operations research;
Finance;
Models;
Risk assessment;
Disasters;
Studies;
Macroeconomics;
Economic recovery;
Losses
Classification
0790: Systems design
0501: Economics
0796: Operations research
0508: Finance
Identifier / keyword
Social sciences; Applied sciences; Economic sectors; Extreme risk; Financial modeling; Infrastructures; Interdependent
Title
Extreme risk analysis of dynamic interdependent infrastructures and sectors of the economy with applications to financial modeling
Author
Lian, Chenyang
Number of pages
171
Publication year
2006
Degree date
2006
School code
0246
Source
DAI-B 67/05, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780542700538
Advisor
Haimes, Yacov Y.; Santos, Joost R.
University/institution
University of Virginia
University location
United States -- Virginia
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3218407
ProQuest document ID
304962734
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304962734
Access the complete full text

You can get the full text of this document if it is part of your institution's ProQuest subscription.

Try one of the following:

  • Connect to ProQuest through your library network and search for the document from there.
  • Request the document from your library.
  • Go to the ProQuest login page and enter a ProQuest or My Research username / password.