JOERN BLOCK, Munich University of Technology
Email: block@wi.tum.de
P. KOELLINGER, DIW Berlin, German Institute for Economic Research
Email: pkoellinger@diw.de

We study a unique sample of 1,547 nascent entrepreneurs in Germany and analyze which factors are associated with their start-up satisfaction. Our results identify a group of nascent entrepreneurs that 'cannot get satisfaction' with their start-up because they did not choose to become entrepreneurs out of free will, but out of long-term unemployment or a lack of better employment alternatives. Overall, financial success is the most important determinant of start-up satisfaction. Yet, achievement of independence and creativity is also highly important, a finding that emphasizes the economic relevance of procedural utility and non-financial incentives.

ANDREW GRODNER, East Carolina University
Email: agrodner@andrewgrodner.com
THOMAS J. KNIESNER, Syracuse University - Department of Economics, Institute for the Study of Labor (IZA)
Email: tkniesne@maxwell.syr.edu

We examine the socially optimal wealth distribution in a two-person two-good model with heterogeneous workers and asymmetric social interactions where only one (social) individual derives positive or negative utility from the leisure of the other (non-social) individual. We show that the interdependence can effectively counter-act the need to transfer wealth to low-wage individuals and may require them to be poorer by all objective measures. We demonstrate that in the presence of social interactions it can be socially desirable to keep substantial wealth inequality.

C. N. V. KRISHNAN, Case Western Reserve University - Department of Banking & Finance
Email: cnk2@cwru.edu
RALITSA PETKOVA, Case Western Reserve University - Department of Banking & Finance
Email: ralitsa.petkova@case.edu
PETER H. RITCHKEN, Case Western Reserve University - Department of Banking & Finance
Email: phr@po.cwru.edu

Investors hold portfolios of assets with different risk-reward profiles for diversification benefits. Conditional on the volatility of assets, diversification benefits can vary over time depending on the correlation structure among asset returns. The correlation of returns between assets has varied substantially over time. To insure against future "low diversification" states, investors might demand securities that offer higher payouts in these states. If this is the case, then investors would pay a premium for securities that perform well in regimes in which the correlation is high. We empirically test this hypothesis and find that correlation carries a significantly negative price of risk, after controlling for asset volatility and other risk factors.

CHRIS EDMOND, New York University
Email: cedmond@stern.nyu.edu

This paper develops a method for solving for the dynamic general equilibrium of a deterministic continuous time overlapping generations model with a finite-horizon life-cycle. The model has isoelastic preferences and allows for general assumptions about individual endowments and demographics. Solving for an equilibrium reduces to solving a nonlinear integral equation. In the special case of log utility, the integral equation is linear and global approximations to a solution are easily computed with linear algebra.

VISHAL NARAYAN, Cornell University - Johnson School of Management
Email: vn28@cornell.edu
SHA YANG, New York University - Leonard N. Stern School of Business
Email: shayang@stern.nyu.edu

Members of online communities access consumption related information posted by several other members. Over time, members are influenced by some members of the community, and develop informal relationships with them. We propose a model for the timing of the establishment of relationships between two community members. We explicitly model the salient properties of dyadic data (data on the timing of relationships formed between a pair of community members) arising due to their inherent interdependence: the correlation between the dyadic observations due to reciprocity, the correlation between sender and receiver specific observations due to the "duality" of roles of each member, and the interaction of the latent characteristics of dyad members due to "homophily". The proposed model outperforms benchmark models in terms of in sample fit and predictive power. Moreover, ignoring the properties of dyadic data leads to biased inferences about the effects of covariates. We explore the factors that influence relationship formation between community members who post product reviews in an existing online community. The amount and valence of the information that the dyad members share, the similarity between the information that they post and their network characteristics affect relationship formation. We demonstrate how the model can be applied to improve the targeting effectiveness of marketing campaigns through agents in online communities.

WENCHAO LIAO, The Graduate Center, City University of New York
Email: wenc.liao@gmail.com

Chapter 1: Trading Treasury Futures: A Vector Autoregressive (VAR) Analysis on the Volume-Volatility Relation

Does trading by hedgers or speculators destabilize the Treasury futures market? And if it is the case, how do they destabilize the market? This is an empirical study on the volume-volatility relation which is central to the market microstructure literature. Vector autoregressive (VAR) analysis is conducted on the 2-, 5-, 10-, and 30-year Treasury futures contracts traded at the Chicago Board of Trade. With some mixed results, it can be roughly concluded that speculators destabilize the Treasury futures market, causing a more turbulent trading pattern as evident in the increased volatility. However, the same can not be said of hedgers; available evidence at best suggests a weak relation between hedging and a decreased price volatility (indicating a market being stabilized). To my knowledge, this study is the first in applying the VAR technique to the context of Treasury futures trading, and the first in comparing the three different volatility measures (intra-day, historical, and GARCH) simultaneously. In addition, GARCH volatility specifications are comprehensively tested and the commonly-used GARCH(1,1) is conveniently arrived at. It is also the first in examining the volume and open interest by constructing two different trading activity series (aggregate and active contract amounts) in the same study, and the results are compared. On the volume-volatility relation, it is among the few that have a specific focus on each individual contract instead of an all-as-one approach. The long period of data (from year 1991 to 2006) is applied to the VAR framework.

Chapter 2: Volume and Volatility: The Relation, Two Models, and Regulating Market Squeezes of Treasury Futures Trading

I review in Chapter 2 the classes of models in theorizing the volume-volatility relation. The strategic trading models of private information by Kyle (1985) and differences of opinion model of public information by Harris and Raviv (1993) are discussed. I propose that the differences of opinion model are more suitable for trading behavior in the Treasury futures market, since virtually all diligent analysts are exposed to the same set of public data (such as Federal Open Market Committee announcements, Fed watchers' studied guesses, or quarterly GDP growth rates and monthly nonfarm payrolls for general economic outlook), but they nonetheless infer their interest rate expectations with different interpretations of data. Unlike the cases in stock or corporate bond markets where differential accesses to private information are essential, differences in interpretation are more significant in Treasury futures trading and private information is less distinctive for individual contracts. The main predictions of the Harris and Raviv model are compared with my VAR results from Chapter 1; it is especially notable that the positively autocorrelated volume pattern is confirmed by the surprisingly strong VAR evidence. I then conclude with the recent episode (09/2006) of market squeeze warnings to Treasury primary dealers. Implications on Treasury futures trading in terms of regulating the market are discussed.

KOBI AKO ABAYOMI, Duke University - Department of Statistics
Email: kaa21@duke.edu
UPMANU LALL, affiliation not provided to SSRN
Email: ulall@yahoo.com
VICTOR H. DE LA PENA, Columbia University - Department of Statistics
Email: vp@stat.columbia.edu

We propose a parametric version of Independent Component Analysis (ICA) via Copulas - families of multivariate distributions that join univariate margins to multivariate distributions. Our procedure exploits the role for copula models in information theory and in measures of association, specifically: the use of copulae densities as parametric mutual information, and as measures of association on the rank statistics.

The copula approach offers a unified view of component analysis procedures, in particular, by parameterizing multivariate dependence. ICA then, via the copula, is a generalization of Principal Component Analysis (PCA) - where the copula model may be non-Gaussian. Generally, the goal is to orthogonalize a measure of multivariate dispersion, yielding an orthogonal basis for a multivariate data set. The flexibility of the copula approach allows for parameterizations of non-gaussian, non-monotone dependence. Additionally, we note a possible use for the Copula approach in generalized component extraction procedures (such as Canonical Correlation Analysis). We apply one version of the CICA approach to the 2002 Environmental Sustainability Index (ESI), an aggregation of 64 environmental variables on 142 countries.

ZHIDONG BAI, Northeast Normal University
Email: stabaizd@nus.edu.sg
HUIXIA LIU, Northeast Normal University
Email: g0304882@nus.edu.sg
WING-KEUNG WONG, Hong Kong Baptist University
Email: awong@hkbu.edu.hk

Levy and Levy (2002, 2004) and others extend the Stochastic Dominance (SD) theory for risk averters and risk seekers by developing the prospect SD (PSD) and Markowitz SD (MSD) theory for investors with S-shaped and reverse S-shaped utility functions. Davidson and Duclos (2000) and others develop SD test for risk averters while Wong, et al.~(2007a) develop SD test for risk seekers. In this paper, we extend their work by developing new statistics for both PSD and MSD of the first three orders. One could then utilize these statistics to identify assets of preferences for investors with S-shaped and reverse S-shaped utility functions.

To illustrate the usefulness of our proposed statistics, we utilize the SD test statistics to study the preferences of investors with the corresponding S-shaped and reverse S-shaped utility functions viz-a-viz returns of traditional stocks and internet stocks before and after the internet bubble.

JORDI MONDRIA, University of Toronto - Department of Economics
Email: jordi.mondria@utoronto.ca
THOMAS WU, University of California, Santa Cruz - Department of Economics
Email: thomaswu@ucsc.edu
YI ZHANG, University of California, Santa Cruz
Email: yiz@soe.ucsc.edu

Few studies have successfully examined the empirical impact of limited information processing on real economic variables. The challenge relies, of course, on the difficulty of measuring an economic agent's degree of attention/inattention paid to different types of information. This paper overcomes such challenge exploring a unique dataset containing the "search/click-through" behavior of internet search engine users. We analyzes the effect of attention allocation on international investment decisions by combining U.S. data on portfolio holdings of foreign securities with the attention allocated by 657,426 America Online customers in search queries towards these countries. We find evidence that: (i) agents tend to search more information about countries where they hold more assets, and (ii) agents tend to invest more in countries where they process more information.

CHRISTIAN CECH, University of Applied Sciences of bfi Vienna
Email: christian.cech@fh-vie.ac.at

Empirical results from several studies indicate that changes in interest rates and changes in credit spreads are negatively related in the short run. These findings are further investigated by examining the dependence structure between interest rate and credit risk factor changes that are computed from sovereign and corporate bond indices. Several copulas (Gaussian, Student t, BB1, and Frank copula) are calibrated and their goodness-of-fit is compared. No clear pattern of the dependence structure can be observed as it varies substantially with the duration and - concerning the credit risk factor changes - the rating of the obligors. The Student t copula's fit in terms of the AIC goodness-of-fit measure is superior to that of all other copulas. The null hypothesis of a specific copula being the true copula can be rejected for the Student t copula in the least cases. Additionally employing a likelihood-ratio test, the null hypothesis of a Gaussian copula can be rejected in favour of a Student t copula. The Gaussian copula seems to underestimate the probability of joint strong risk factor changes, while the Student t copula seems to overestimate it.