Sunday

SB67
■ SB67
67- Ellis East- Hyatt
System Reliability and Maintenance
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Nan Chen, Assistant Professor, National University of Singapore,
Department of Industrial and Systems Eng, 1 Engineering Drive 2,
Singapore, 129789, Singapore, isecn@nus.edu.sg
1 - Criticality Index for Degrading Components under
Dynamic Environments
Xiao Liu, liuxiaodnn_1@hotmail.com, Khalifa N. Al-Khalif,
Dingguo Hua, Abdel Magid Hamouda, David Coit, Elsayed Elsayed
Criticality index of a component depends on its degradation path and system
configuration. This presentation investigates the criticality analysis for degrading
components under dynamic environmental conditions. The component
degradation is modeled by a k-dimensional Wiener process, and component fails
when any of these k degradation paths attains its own threshold level. In the
numerical examples, we illustrate the computation of component criticality in
different system configurations.
2 - A Quantile Regression Approach to Analyze Accelerated Life
Testing Data
Nan Chen, Assistant Professor, National University of Singapore,
Department of Industrial and Systems Eng, 1 Engineering Drive 2,
Singapore, 129789, Singapore, isecn@nus.edu.sg
We propose a quantile regression framework to model the accelerated life tests
(ALT) data. The quantile of the failure distribution at usage level can be easily
estimated. Compared with traditional parametric regression methods, quantile
regression is distribution free, efficient in presence of censoring, and is more
flexible in modeling ALT relations. In addition, the method can be used naturally
in the cases when the failure-free life depends on the accelerating factor.
3 - An Integrated Approach to the Design of Statistically Precise
and Energy Efficient Accelerated Tests
Haitao Liao, Associate Professor, University of Arizona, Tucson, AZ,
85721, United States of America, hliao@email.arizona.edu,
Dan Zhang
In this paper, an integrated experimental design approach is introduced with the
objective of maximizing the reliability estimation precision while minizing the
energy consumption of an accelerated test. Relevant statistical inference methods
and required experimental needs and technologies are discussed.
4 - Reliability and Maintenance for Dependent Competing Failure
Processes with Shifting Thresholds
Qianmei Feng, Associate Professor, Houston University, E206
Engineering Bldg 2, Houston University, Houston, TX, 77204-
4008, United States of America, qmfeng@uh.edu, Lei Jiang,
David Coit
We present reliability and maintenance models for systems subject to multiple
dependent competing failure processes with a changing and dependent failure
threshold. Three cases of dependency between shock process and hard failure
threshold level are studied. Reliability models are developed based on degradation
and random shock modeling. Two preventive maintenance policies are also
applied and compared. Then an MEMS example is given to demonstrate the
reliability models and maintenance polices.
■ SB68
68- Suite 312- Hyatt
Finance: Risk Management
Contributed Session
Chair: Woojin Chang, Seoul National University, Gwanak-gu
Gwanak-ro 1, Seoul, Korea, Republic of, changw@snu.ac.kr
1 - Continuous Dynamic Portfolio Choice with Bayesian Regret
Shea Chen, University of California, Berkeley, 301 Main St.,
Unit 34E, San Francisco, CA, 94105, United States of America,
sheachen@berkeley.edu, Andrew Lim
We formulate a continuous-time portfolio choice problem in which the investor is
uncertain about parameters of the model, and the objective function is a Bayesian
version of relative regret. The optimal portfolio is characterized and shown to
involve a “tilted” posterior, where the tilting is defined in terms of a family of
stochastic benchmarks.
INFORMS Phoenix – 2012
102
2 - Forecasting the Stress of Credit Card Portfolios
Mee-chi So, University of Southampton, Southampton
Management School, Southampton, SO17 1BJ, United Kingdom,
m.so@soton.ac.uk
This study aims to develop models to forecast the default rate of credit card
accounts under different economic scenarios. Two modelling approaches were
extended to include the impact of lenders’s actions within the model. The first
approach was a regression model of the aggregate losses based on economic
variables. The second approach was a set of vintage level models which
highlighted the months on books effect on credit losses. A case study using the
models was described.
3 - Credit Rating Migration Model: Markov Switching Process
Woojin Chang, Seoul National University, Gwanak-gu Gwanak-ro
1, Seoul, Korea, Republic of, changw@snu.ac.kr, Sung Yeol Oh
We develop a credit rating migration model based on a discrete-time discretevalued
Markov process depending on hidden Markov chain. We also present a
parameter estimation procedure for the proposed model using the EM algorithm.
The likelihood ratio test shows that our model depending on hidden Markov
chain better describes the credit rating dynamics than a benchmark Markov chain
model.
4 - A Model for Dynamic TaxCredit Utilization
Nilofar Varzgani, PhD Student, Rutgers University, Rutgers
Business School, Newark, NJ, 07102, United States of America,
nilofarv@pegasus.rutgers.edu, Suresh Govindaraj,
Michael Katehakis
We develop a new stochastic dynamic programming model for dynamic tax credit
utilization for a firm that provides maximum expected benefit over a rolling
horizon. The model takes into account earnings and profits, net operating losses,
and credit carryovers. We present computation to demonstrate the benefit the
model provides.
■ SB69
69- Suite 314- Hyatt
Data Driven Portfolio Optimization
Cluster: Optimization in Finance
Invited Session
Chair: Victor DeMiguel, Professor, London Business School, Regent’s
Park, London, NW1 4SA, United Kingdom, avmiguel@london.edu
1 - Computing General Arbitrage Bounds for European Basket
Options via Dantzig-Wolfe Decomposition
Luis Zuluaga, Assistant Professor, Lehigh University,
200 West Packer Ave., Bethlehem, United States of America,
luis.zuluaga@lehigh.edu
We study the problem of computing bounds on the price of a basket option given
the only assumption of absence of arbitrage, and information about prices of
other basket options. We provide a simple way to compute this type of bounds via
a suitable Dantzig-Wolfe decomposition. We illustrate our results by computing
upper and lower arbitrage bounds on gasoline/heating oil crack spread options,
and Alternative Risk Transfer (ART) products.
2 - Performance-based Regularization in Mean-CVaR Portfolio
Optimization
Gah-Yi Vahn, Assistant Professor, London Business School,
Regent’s Park, London, United Kingdom, gvahn@london.edu,
Noureddine El Karoui, Andrew Lim
We introduce performance-based regularization (PBR), a new approach to
addressing estimation risk in data-driven optimization, to mean-CVaR portfolio
optimization. We assume the available log-return data is iid, and detail the
approach for two cases: nonparametric and parametric (the log-return
distribution belongs in the elliptical family). We show that the PBR methods
produce efficient frontiers that are, on average, closer to the population efficient
frontier with less variability.
3 - Size Matters: Optimal Calibration of Shrinkage Estimators for
Portfolio Selection
Alberto Martin-Utrera, Mr, University Carlos III of Madrid,
C/ Madrid, 126-28903, GETAFE, Sp, 28903, amutrera@estecon.uc3m.es
We carry out a comprehensive investigation of shrinkage estimators in the
context of portfolio optimization and we make several contributions to this field.
First, we propose new shrinkage estimators for both the moments of asset returns
and portfolio weights. Second, we propose novel calibration criteria to compute
the optimal shrinkage intensity. Finally, we propose a parametric and a
nonparametric approach to estimate the shrinkage intensity.