This paper is an empirical analysis of two types of industry clusters: clusters that arise exogenously due to natural advantages, and endogenously formed clusters. The Oil and Gas industry was chosen as an industry that would have exogenously clustered near natural resources. The Bio-Medical industry was chosen as an industry that would have clusters that formed endogenously. The evidence suggests that these two industries have clusters that experience very different firm performance gains and losses in comparison to other nonclustered firms in their industries. The Oil and Gas industry has positive performance and growth associated with firms inside clusters. The Bio-Medical industry has negative performance and growth associated with firms inside clusters.

This dissertation contains two essays in industrial organization and one related to the corporate finance literature. In Chapter 1, I investigate how consumers feedback might affect investments in innovation and quality assurance QA). I also focus on how innovation impacts intertemporal price discrimination. In the model, a monopolist releases a new technology embedded in a durable good. Then, I investigate the determinants of the introduction of a new generation in the second period. In the proposed framework consumers feedback can reduce the firms QA/R&D expenses. However, consumers usually provide feedback through complaints, causing reputation damage. I derive conditions under which the lower “quality” version of the good first generation) will have a higher price than the improved version second generation). Planned obsolescence causes the price schedule to be steeper than predicted by the previous literature on durable goods. The model predicts and explains Apples experience with several products, in particular the iPhone family. In Chapter 2, Aren Megerdichian and I examine a firms decision to raise price overtly by increasing the dollar amount of a good) versus the adoption of hidden price change by decreasing the contents in a goods package). We provide an oligopoly model explaining the hidden price change phenomenon, as well as a comprehensive set of empirical analyses, including demand estimation to assess the impact of hidden price increases on expenditure share and profitability. We focus on the ready-to-eat cereal industry. During July 2007, General Mills decreased the cereal content for 20 out of 23 of their products in our sample of scanner data. We find that some General Mills products gained expenditure share after the hidden price change relative to what the demand system predicts, indicating that a sufficient proportion of consumers did not notice the hidden price change. We also find that some products lost share relative to what the demand system predicts. A key finding is that consumers tend to notice hidden price changes on smaller-sized boxes of cereal, leading them to substitute to larger-sized boxes of cereal. The final chapter is a joint work with Regio Martins. Our conjecture is that regulated firms may be subject to some regulatory practices that can potentially affect the symmetry of the distribution of their future profits. If these practices are anticipated by investors in the stock market, the pattern of asymmetry in the empirical distribution of stock returns may differ among regulated and non-regulated companies. We review recently proposed asymmetry measures that are robust to the empirical features of return data and investigate whether there are any meaningful differences in the distribution of asymmetry between these two groups of companies.

The huge amount of tick-by-tick data provides rich and timely information regarding fluctuations of traded assets and their co-movements. Nevertheless, the existence of market microstructure noise interferes with estimation, especially when the sampling frequency increases to beyond every five minutes. The first chapter introduces the Quasi-Maximum Likelihood Estimator of volatility as a solution to this problem. In theory, the proposed approach is consistent, rate-efficient, and shares the model-free feature with non-parametric alternatives. In practice, it is also convenient and has better small sample performance without any tuning parameters. When measuring covariance and correlation, the fact that the two assets may not trade or otherwise be observed at exactly the same time, known as observation asynchronycity, is another issue that may distort the estimates. The second chapter, jointly written with Yacine Ait-Sahalia and Jianqing Fan, extends the Quasi-Maximum Likelihood Estimator to explore asynchronous and noisy data, with the help of generalized synchronization scheme. A fundamental problem in option pricing is to find explicit pricing formulae or efficient pricing algorithms. However, closed-form pricing formulae are very sparse, whereas numerical or simulation-based methods are computationally expensive and deliver no insight into the structure of the option price. The third chapter fills in the gap between closed-form solutions and numerical methods through expansions of option prices. This approach works with general dynamics without any requirement on affine dynamics or explicit characteristic functions, and quantitatively characterizes the relative importance of model parameters as the option approaches expiration. With closed-form expansions, we translate model features into option prices, such as stochastic interest rate, mean-reverting drift, and self-exciting or skewed jumps. Numerical examples illustrate the accuracy of this approach.

This thesis consists of two parts, both of which are applications of characteristic functions to finance. In the first part, we propose a general class of models for the simultaneous treatment of equity, corporate bonds, government bonds and derivatives. The noise is generated by a general affine Markov process. The framework allows for stochastic volatility, jumps, the possibility of default and correlations between different assets. We extend the notion of a discounted characteristic function of the log stock price to the case where the underlying can default and show how to calculate it in terms of a coupled system of generalized Riccati equations. This yields an efficient method to compute prices of power payoffs and Fourier transforms. European calls and puts as well as binaries and asset-or-nothing options can then be priced with the fast Fourier transform methods of Carr and Madan 1999) and Lee 2005). Other European payoffs can be approximated by a linear combination of power payoffs and vanilla options. We show the results to be superior to using only power payoffs or vanilla options. We also give conditions for our models to be complete if enough financial instruments are liquidly tradable and study dynamic hedging strategies. As an example we discuss a Heston-type stochastic volatility model with possibility of default and stochastic interest rates. In the second part, we consider pricing models where the stock price is defined as a solution of a stochastic differential equation with jumps. We derive a simple set of sufficient conditions, in order for the characteristic function of the log stock price to be the solution of the corresponding Kolmogorov partial integro-differential equation PIDE). Moreover, we prove that explosions of the solution of the PIDE correspond to explosions of the moments of the stock price. We also analyze under what sufficient conditions the explosion times do not depend on the initial value and provide a counter-example when these do not hold. The results are extended to the case of stochastic interest rates as well as the case of possibility of default.

The research examines what drives Mergers and Acquisitions (M&As) using a theoretical and empirical approach. The theoretical part uses flexible optimal policies which adjust to changes in the market structure following a merger. The empirical part tests the major theoretical predictions to identify determinants of M&As in advanced economies. Chapters 1 and 2 consider M&As among firms in a pollution-intensive sector. Chapter 1 shows that identical polluting firms engage in M&As only if environmental policies are flexible. Chapter 2 shows that the flexibility of environmental policy increases the incentive to merge among heterogeneous firms. In addition, with flexible policy highly polluting firms have the highest incentive to merge than less polluting firms in a given sector. The empirical evidence suggests that the decision of manufacturing firms to engage in M&As is affected by environmental policy and firms may engage in merger deals in anticipation of a change in policy. Chapter 3 shows that with a flexible consumption tax firms in a bigger, more efficient country takeover firms in a smaller, less efficient country. The incentive to merge increases with the efficiency and market size of the host country. The empirical result obtained from 7 OECD countries shows that market size and firm efficiency play a major role in triggering international mergers.

This thesis explores the important link between macroeconomic dynamics and the financial sector. The first essay studies Epstein-Zin preferences, which are found to be able to account for both aggregate macroeconomic dynamics and asset prices. In the first essay, I compare different solution methods for computing dynamic stochastic general equilibrium (DSGE) models with Epstein-Zin preferences and stochastic volatility. I show that perturbation methods are an attractive approach for computing this class of problems. The second essay emphasizes the importance of frictions in the financial market on real economic activities. The model studies the international business cycle co-movements when financial frictions are present. The model can account for the positive and sizable cross-country correlations of output, investment and hours worked in the data.

The 2007-2009 financial crisis has shed light on the importance of contagion and systemic risk, and revealed the lack of adequate indicators for measuring and monitoring them. This dissertation addresses these issues and leads to several recommendations for the design of an improved assessment of systemic importance, improved rating methods for structured finance securities, and their use by investors and risk managers. Using a complete data set of all mutual exposures and capital levels of financial institutions in Brazil in 2007 and 2008, we explore in chapter 2 the structure and dynamics of the Brazilian financial system. We show that the Brazilian financial system exhibits a complex network structure characterized by a strong degree of heterogeneity in connectivity and exposure sizes across institutions, which is qualitatively and quantitatively similar to the statistical features observed in other financial systems. We find that the Brazilian financial network is well represented by a directed scale-free network, rather than a small world network. Based on these observations, we propose a stochastic model for the structure of banking networks, representing them as a directed weighted scale free network with power law distributions for in-degree and out-degree of nodes, Pareto distribution for exposures. This model may then be used for simulation studies of contagion and systemic risk in networks. We propose in chapter 3 a quantitative methodology for assessing contagion and systemic risk in a network of interlinked institutions. We introduce the Contagion Index as a metric of the systemic importance of a single institution or a set of institutions, that combines the effects of both common market shocks to portfolios and contagion through counterparty exposures. Using a directed scale-free graph simulation of the financial system, we study the sensitivity of contagion to a change in aggregate network parameters: connectivity, concentration of exposures, heterogeneity in degree distribution and network size. More concentrated and more heterogeneous networks are found to be more resilient to contagion. The impact of connectivity is more controversial: in well-capitalized networks, increasing connectivity improves the resilience to contagion when the initial level of connectivity is high, but increases contagion when the initial level of connectivity is low. In undercapitalized networks, increasing connectivity tends to increase the severity of contagion. We also study the sensitivity of contagion to local measures of connectivity and concentration across counterparties—the counterparty susceptibility and local network frailty—that are found to have a monotonically increasing relationship with the systemic risk of an institution. Requiring a minimum aggregate) capital ratio is shown to reduce the systemic impact of defaults of large institutionsï¼› we show that the same effect may be achieved with less capital by imposing such capital requirements only on systemically important institutions and those exposed to them. In chapter 4, we apply this methodology to the study of the Brazilian financial system. Using the Contagion Index, we study the potential for default contagion and systemic risk in the Brazilian system and analyze the contribution of balance sheet size and network structure to systemic risk. Our study reveals that, aside from balance sheet size, the network-based local measures of connectivity and concentration of exposures across counterparties introduced in chapter 3, the counterparty susceptibility and local network frailty, contribute significantly to the systemic importance of an institution in the Brazilian network. Thus, imposing an upper bound on these variables could help reducing contagion. We examine the impact of various capital requirements on the extent of contagion in the Brazilian financial system, and show that targeted capital requirements achieve the same reduction in systemic risk with lower requirements in capital for financial institutions. The methodology we proposed in chapter 3 for estimating contagion and systemic risk requires visibility on the entire network structure. Reconstructing bilateral exposures from balance sheets data is then a question of interest in a financial system where bilateral exposures are not disclosed. We propose in chapter 5 two methods to derive a distribution of bilateral exposures matrices. The first method attempts to recover the balance sheet assets and liabilities “sample by sample”. Each sample of the bilateral exposures matrix is solution of a relative entropy minimization problem subject to the balance sheet constraints. However, a solution to this problem does not always exist when dealing with sparse sample matrices. Thus, we propose a second method that attempts to recover the assets and liabilities “in the mean”. This approach is the analogue of the Weighted Monte Carlo method introduced by Avellaneda et al. 2001). We first simulate independent samples of the bilateral exposures matrix from a relevant prior distribution on the network structure, then we compute posterior probabilities by maximizing the entropy under the constraints that the balance sheet assets and liabilities are recovered in the mean. We discuss the pros and cons of each approach and explain how it could be used to detect systemically important institutions in the financial system. The recent crisis has also raised many questions regarding the meaning of structured finance credit ratings issued by rating agencies and the methodology behind them. Chapter 6 aims at clarifying some misconceptions related to structured finance ratings and how they are commonly interpreted: we discuss the comparability of structured finance ratings with bond ratings, the interaction between the rating procedure and the tranching procedure and its consequences for the stability of structured finance ratings in time. These insights are illustrated in a factor model by simulating rating transitions for CDO tranches using a nested Monte Carlo method. In particular, we show that the downgrade risk of a CDO tranche can be quite different from a bond with same initial rating. Structured finance ratings follow path-dependent dynamics that cannot be adequately described, as usually done, by a matrix of transition probabilities. Therefore, a simple labeling via default probability or expected loss does not discriminate sufficiently their downgrade risk. We propose to supplement ratings with indicators of downgrade risk. To overcome some of the drawbacks of existing rating methods, we suggest a risk-based rating procedure for structured products. Finally, we formulate a series of recommendations regarding the use of credit ratings for CDOs and other structured credit instruments. Keywords: bilateral exposures, collateralized debt obligation, contagion, copula, credit derivatives, credit rating, default clustering, default risk, domino effects, macro-prudential regulation, random graph, relative entropy, scale-free, small-world, systemic risk, structured finance, transition probabilities.

This thesis analyzes models of financial markets that incorporate the possibility of arbitrage opportunities. The first part demonstrates how explicit formulas for optimal trading strategies in terms of minimal required initial capital can be derived in order to replicate a given terminal wealth in a continuous-time Markovian context. Towards this end, only the existence of a square-integrable market price of risk rather than the existence of an equivalent local martingale measure) is assumed. A new measure under which the dynamics of the stock price processes simplify is constructed. It is shown that delta hedging does not depend on the “no free lunch with vanishing risk” assumption. However, in the presence of arbitrage opportunities, finding an optimal strategy is directly linked to the non-uniqueness of the partial differential equation corresponding to the Black-Scholes equation. In order to apply these analytic tools, sufficient conditions are derived for the necessary differentiability of expectations indexed over the initial market configuration. The phenomenon of “bubbles,” which has been a popular topic in the recent academic literature, appears as a special case of the setting in the first part of this thesis. Several examples at the end of the first part illustrate the techniques contained therein. In the second part, a more general point of view is taken. The stock price processes, which again allow for the possibility of arbitrage, are no longer assumed to be Markovian, but rather only Ito processes. We then prove the Second Fundamental Theorem of Asset Pricing for these markets: A market is complete, meaning that any bounded contingent claim is replicable, if and only if the stochastic discount factor is unique. Conditions under which a contingent claim can be perfectly replicated in an incomplete market are established. Then, precise conditions under which relative arbitrage and strong relative arbitrage with respect to a given trading strategy exist are explicated. In addition, it is shown that if the market is quasi-complete, meaning that any bounded contingent claim measurable with respect to the stock price filtration is replicable, relative arbitrage implies strong relative arbitrage. It is further demonstrated that markets are quasi-complete, subject to the condition that the drift and diffusion coefficients are measurable with respect to the stock price filtration.

This dissertation analyzes the role of institutional investors in corporate governance. The first essay studies the effect of potential proxy contests on corporate policies. I find that when the likelihood of a proxy contest increases, companies exhibit increases in leverage, dividends, and CEO turnover. In addition, companies decrease R\&D, capital expenditures, stock repurchases, and executive compensation. Following these changes, there is an improvement in profitability. The second essay investigates the optimal contract with an informed money manager. Motivated by simple structure of portfolio managers’ compensation and complex risk structure of returns, I show that it may be optimal for the principal to stay unaware about the true risk structure of returns. The third essay analyzes the biases related to self-reporting in the hedge funds databases by matching the quarterly equity holdings of a complete list of 13F-filing hedge fund companies to the union of five major commercial databases of self-reporting hedge funds between 1980 and 2008.

Responding to the allegedly biased research reports issued by large investment banks, the Global Research Analyst Settlement and related regulations went to great lengths to weaken the conflicts of interest faced by investment bank analysts. In this paper, I investigate the effects of these changes on small and large investor confidence and on trading profitability. Specifically, I examine abnormal trading volumes generated by small and large investors in response to security analyst recommendations and the resulting abnormal market returns generated. I find an overall increase in investor confidence in the post-regulation period relative to the pre-regulation period consistent with a reduction in existing conflicts of interest. The change in confidence observed is particularly striking for small traders. I also find that small trader profitability has increased in the post-regulation period relative to the pre-regulation period whereas that for large traders has decreased. These results are consistent with the Securities and Exchange Commission’s primary mission to protect small investors and maintain the integrity of the securities markets.