Informs 2015

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INFORMS Philadelphia – 2015
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3 - The Impact of Facebook on Offline Sales: Evidence from the U.S.
Automobile Industry
Yen-yao Wang, Michigan State University, N270A Business
College Complex, East Lansing MI 48824, United States of
America , Anjana Susarla,
Vallabh Sambamurthy
This study examines the dynamic interactions between firm-generated content,
user-generated content, and offline car sales in the U.S. automobile industry. We
collected the official Facebook pages of 31 car companies in the U.S., and
supplemented the data from these firms’ traditional media efforts, and offline car
sales from 2009 to 2014. A panel vector autoregressive (PVAR) model is
conducted to examine our research framework. Implications for researchers and
managers are discussed.
4 - An Investigation of Factors that Influence the Success of Social
Commerce Platforms
Suning Zhu, , Jiahe Song, Pei Xu
This paper introduces a descriptive framework for understanding factors that
influence the success of social commerce platforms. It identifies three main classes
of influencing factors (self-expression, communication, and style) and
characterizes the individual factors in each class.The resultant framework can be
used by researchers for reference of theories and hypothesis generation, and by
practitioners for benchmarking social commerce practices.
SC06
06-Room 306, Marriott
Computational Methods in Options Pricing and
Portfolio Selection
Sponsor: Financial Services
Sponsored Session
Chair: Liming Feng, Associate Professor, University of Illinois at
Urbana-Champaign, 104 S Mathews Ave, Urbana, IL, 61801, United
States of America,
Co-Chair: Xuewei Yang, Associate Professor, Nanjing University, School
of Management and Engineering, #22, Hankou Road, Gulou District,
Nanjing, 210093, China,
1 - Investor Behavior and Turbo Warrant Pricing
Xuewei Yang, Associate Professor, Nanjing University, School of
Management and Engineering, #22, Hankou Road, Gulou
District, Nanjing, 210093, China, Xindan Li,
Avanidhar Subrahmanyam
Turbo warrants are options that can be called back when underlying prices reach
a threshold. We find that investors treat turbo warrants like lotteries in that they
prefer those with low prices, high volatilities, and high skewness, and prefer
trading them when underlying prices are near callback thresholds. As a result,
turbo warrants are overpriced: during 2012, investors lost 1.82 billion HKD
(US$235 million) by trading turbo warrants written on the Hang Seng Index.
2 - Asymptotic Expansions of Discretely Monitored Barrier Options
under Stochastic Volatility Models
Chao Shi, Assistant Professor, Shanghai University of Finance and
Economics, 100 Wudong Road, Yangpu District, Shanghai,
200433, China, Ning Cai, Chenxu Li
This paper proposes an algorithm for pricing discretely monitored barrier options
under stochastic volatility models. It turns out that the celebrated Hilbert
transform recursion algorithm proposed by Feng and Linetsky (2008) becomes
the leading term and building block in our expansion formula under stochastic
volatility models. Our expansions are automatic and fast. Numerical results show
that our algorithm is efficient and robust.
3 - Distributions with Analytic Characteristic Functions in
Financial Modeling
Runqi Hu, PhD Student, University of Illinois at Urbana-
Champaign, 104 S Mathews Ave, Urbana, IL, 61801, United
States of America, Liming Feng
In this talk, we consider a class of distributions with characteristic functions that
are analytic in a horizontal strip in the complex plane. Such distributions can be
inverted from their characteristic functions very efficiently using simple rules with
exponentially decaying approximation errors. The results can be applied in
accurate valuation of options in models with jumps and stochastic volatility.
Numerical examples illustrate the effectiveness of the schemes.
4 - Robust Portfolio Selection with Fixed Transaction Cost
Yufei Yang, PhD Candidate, Singapore University of Technology
and Design, Pillar of Engineering Systems and Design,
8 Somapah Road, Singapore, 48732, Singapore,
, Selin Damla Ahipasaoglu,
Jingnan Chen
We study a robust mean-variance portfolio selection problem under fixed
transaction cost. We provide a novel analysis on the portfolio composition and a
closed-form formula is derived to unify various types of portfolios. We uncover
the impact of the uncertainty level and fixed transaction cost to the position
change of each asset.
SC07
07-Room 307, Marriott
Big Risks, Big Data
Cluster: Risk Management
Invited Session
Chair: Paul Glasserman, Columbia Business School, 3022 Broadway,
Uris Hall, New York, United States of America,
1 - Large-dimensional Factor Modeling Based on
High-frequency Observations
Markus Pelger, Assistant Professor, Management Science &
Engineering, Stanford University, Huang Engineering Center, 475
Via Ortega, Stanford, CA, 94305, United States of America,
I provide a statistical theory and empirically estimate an unknown factor structure
based on financial high-frequency data for a large cross-section. I develop an
estimator for the number of factors and consistent and asymptotically mixed-
normal estimators of the loadings and factors for a large number of cross-sectional
and high-frequency observations. In an extensive empirical study of the U.S.
equity market I identify four continuous and one jump factor that explain most of
the variation.
2 - Price Contagion through Balance Sheet Linkages
Agostino Capponi, Columbia, Mudd 313, New York, NY, 10027,
United States of America,
We study price linkages between assets held by financial institutions that
maintain fixed capital structures over time. Our analysis suggests that regulatory
policies aimed at stabilizing the system by imposing capital constraints on banks
may have unintended consequences: banks’ deleveraging activities may amplify
asset return shocks and lead to large fluctuations in realized returns. We show
that these effects can be mitigated by encouraging banks to hold liquid, rather
than illiquid, assets.
3 - Incorporating GICS and High-Frequency Data into Portfolio
Allocation and Risk Estimation
Jianqing Fan, Princeton, Dept of Operations Res & Fin Eng,
Princeton University, Princeton, NJ, 08544, United States of
America, Alexander Furger, Dacheng Xiu
We document a striking block-diagonal pattern in the factor residual covariances
of the S&P 500 constituents, after sorting the assets by their assigned GICS codes.
We propose combining a location-based thresholding approach based on sector
inclusion with the Fama-French and SDPR sector ETF’s. We investigate the
performance of our estimators in a portfolio allocation study. We provide
justification for the empirical results by jointly analyzing the in-fill and diverging
dimension asymptotics.
4 - Estimating the Correlation Matrix of Credit Default Swaps for
Market Risk Management
Richard Neuberg, Columbia University, 1255 Amsterdam Avenue,
Dept of Statistics, 10th Floor, New York, NY, 10027, United States
of America, , Paul Glasserman
We propose a portfolio perspective to better understand the properties of
correlation matrix estimators and loss functions for market risk management. We
find the commonly used latent factor model to systematically misestimate the risk
of certain portfolios. The normal likelihood appears more appropriate than
Frobenius’ and Stein’s loss. We derive specific loss functions. We assess a range of
estimators using CDS data. We also study implied CDS correlations using
distance-to-default processes.
SC06