A New Look at Oligopoly| Implicit Collusion Through Portfolio Diversification

<p> The global crisis has turned the spotlight on the channels of shock transmission across countries, sectors, and markets. Particular emphasis has been placed on the role of interconnectedness and systemic vulnerabilities in the crisis propagation. The crisis has also rekindled efforts to re-examine the existing financial stability framework, which in many countries includes financial stability reports published by central banks with the aim to monitor systemic risks. I examine three questions that aim to broaden our understanding of the shock transmission channels during the crisis and the effectiveness of financial stability reports as a risk-monitoring tool: (1) What was driving the shock propagation in international interbank markets? (2) Are nonfinancial firms that operate in countries with banking problems more prone to financial distress? (3) Is there an empirical link between the publication of financial stability reports and financial stability?</p>

<p> This paper analyzes and characterizes the dynamics of wealth-share and equilibrium price in a stochastic general equilibrium model with heterogeneous consumers. The characterization enables a comparison between probabilistic learning and price evolution, revealing that prices incorporate "sparse" information efficiently. Results on wealth-share are obtained by comparing traders' optimal investment-consumption plans against their prices. This novel approach extends recent results in the literature by providing a condition that is necessary as well as sufficient for a trader to vanish. The results are applied to survival in iid, survival in large economies, and survival of traders that follow strategies commonly observed in real markets.</p>

<p> This dissertation studies trading and investment in financial markets through the lens of financial econometrics. Chapter 1 develops a continuous-time model of the optimal strategies of high-frequency traders (HFTs) to rationalize their pinging activities - defined as rapid submissions and subsequent cancellations of limit orders inside the bid-ask spread. The current worry is that HFTs ping inside the spread to manipulate the market. In contrast, the HFT in my model uses pinging to control inventory or to chase short-term price momentum without any learning or manipulative motives. I use historical message data to reconstruct limit order books, and characterize the HFT's optimal strategies under the viscosity solution to my model. By gauging the model's implications against data, I show that pinging is not necessarily manipulative and is rationalizable as part of the dynamic trading strategies of HFTs. </p><p> In Chapter 2, joint with Harrison Hong, we use overdispersed Poisson regression models to study social networks in finance. We count an investor's social connections in different cities as proportional to the number of stocks held by this investor that are headquartered in those cities. When connections are formed in an i.i.d. manner, the count of such connections in any city follows a Poisson distribution. Using data from institutional investors' holdings, we find instead overdispersion for a number of cities like San Jose and San Diego, which suggests that investors have non-i.i.d. propensities to be connected to these cities. Overdispersed cities have a large number of graduates from local universities who work in the fund industry. Managers with relatively high non-i.i.d. propensities to have social contacts significantly outperform other managers. </p><p> In Chapter 3, I propose a continuous-time model for the joint stochastic process of asset price and trading volume to study the transmission mechanism from changes in trading volume to price movements at the high-frequency level. A GMM-based estimation procedure is developed based on the model's closed-form moment conditions. I estimate the model on real-world high frequency financial data and find that, jumps in volume have a strong cross-excitation effect on jumps in price. Other implications of the model are also discussed. </p>

<p> This dissertation consists of three essays that examine the role that financial intermediaries played in the housing market and the credit default swap market in the years leading up to the U.S. financial crisis in 2008. The first essay studies the impact that increased mortgage lending during the 2004-2006 housing boom period had on future 2007-2009 home price crashes in Florida. The second essay focuses on the predictability of home prices prior to 2006 and looks at how bank lending across different metropolitan statistical areas responded to past increases in home prices in 2006. The third essay turns to the credit default swap market and investigates how the market perceived investment bank counterparty risk by analyzing credit default swap prices during the time period leading up to and immediately after the collapse of Bear Stearns in 2008.</p><p> In the first essay, "Loan Supply and Home Price Crashes: Evidence from Florida," I examine whether neighborhoods that experienced higher loan growth during the recent housing bubble later experienced larger home price crashes when the bubble burst. I use microeconomic data on mortgages and home sales to answer this question in Florida. A simple OLS regression reveals that, within Florida's metropolitan statistical areas, a 1 percent increase in 2004 to 2006 census tract loan growth is associated with a relatively modest 0.1 percent decline in 2007 to 2010 census tract home prices. However, this exercise ignores the fact that loan growth may be driven by demand as well as supply factors. Using information on bank lending practices outside of Florida, I am able to isolate changes in loan growth that are uncorrelated with local demand. From the more sophisticated analysis, I find that a 1 percent increase in earlier loan growth actually results in a 0.9 percent decline in home prices. I find some evidence that supply-driven loan growth led to greater home price crashes by pushing home prices up to unsustainable levels. These results contribute to an understanding of the relationship between credit booms and financial crises by linking loan growth to asset prices.</p><p> In the second essay, "The Subprime Crisis and House Price Appreciation," (co-authored with William Goetzmann and Liang Peng) we argue that econometric analysis of housing price indexes before 2006 generated forecasts of future long-term price growth and low estimated probabilities of extreme price decreases. These forecasts of future increases in home-loan collateral values may have affected both the demand and the supply of mortgages. Standard time series models using repeat-sales indexes suggest that positive trends had a long half-life. Expectations based on such models support expectations that could lead to an asset bubble. Analysis of data from the HMDA loan database and LoanPerformance.com at the MSA level and at the loan level substantiates the effects of past price trends on the demand and supply of subprime mortgages. On the demand side, at the MSA level, past home price increases are associated with more subprime applications, higher loan to income ratios and lower loan to value ratios of applications for both prime and subprime mortgages. This is consistent with the notion that households not only borrowed more but also invested more in home equity conditional on greater past house price increases. On the supply side, past home price appreciation had a significantly greater impact on the approval rate of subprime applications than the approval rate of prime applications. Loan level analysis indicates that past home price appreciation increased the approval rate of subprime applications but did not affect the approval rate of prime applications. Further, approved HMDA subprime loans had higher loan to income ratios in MSAs with greater past house price trends.</p><p> In the last essay, "Counterparty Risk in the Credit Default Swap Market," I use a panel data set of credit default swap (CDS) spreads that covers 157 companies over 188 weeks from 2005 to 2008 to test whether counterparty risk in the CDS market, as measured by the financial health of the major CDS dealers, has an effect on market-wide CDS spreads. The results suggest that after controlling for changes in firm and market conditions, a rise in counterparty risk increases CDS spreads of investment grade firms. Counterparty risk's ability to explain changes in CDS spreads of investment grade firms remains the same before and after the Bear Stearns crisis, suggesting that perhaps investors were unprepared when Lehman Brothers was not saved by the Fed and went bankrupt six months later.</p>

Thesis (Ph.D.)--Indiana University, Kelley School of Business, 2007. / Source: Dissertation Abstracts International, Volume: 68-09, Section: A, page: 3999. Advisers: Charles Trzcinka; Andrey Ukhov. Title from dissertation home page (viewed May 5, 2008).

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. / Source: Dissertation Abstracts International, Volume: 70-06, Section: A, page: . Adviser: Anne Villamil. Includes bibliographical references. Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--Brown University, 2008. / Thesis advisor : Oded Galor. Vita. Includes bibliographical references (leaves 97-106).

<p> This dissertation focuses on two aspects of factor models, testing and forecasting. For testing, we investigate a more general high-dimensional testing problem, with an emphasis on panel data models. Specifically, we propose a novel technique to boost the power of testing a high-dimensional vector against sparse alternatives. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers, whereas more powerful tests such as thresholding and extreme-value tests require either stringent conditions or bootstrap to derive the null distribution, and often suffer from size distortions. Based on a screening technique, we introduce a ''power enhancement component", which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. As a byproduct, the power enhancement component also consistently identifies the elements that violate the null hypothesis. </p><p> Next, we consider forecasting a single time series using many predictors when nonliearity is present. We develop a new methodology called sufficient forecasting, by connecting sliced inverse regression with factor models. The sufficient forecasting correctly estimates projections of the underlying factors and provides multiple predictive indices for further investigation. We derive asymptotic results for the estimate of the central space spanned by these projection directions. Our method allows the number of predictors larger than the sample size, and therefore extends the applicability of inverse regression. Numerical experiments demonstrate that the proposed method improves upon a linear forecasting model. Our results are further illustrated in an empirical study of macroeconomic variables, where sufficient forecasting is found to deliver additional predictive power over conventional methods.</p>

Thesis (Ph.D.)--Brown University, 2008. / Source: Dissertation Abstracts International, Volume: 69-06, Section: A, page: 2373.