This dissertation contributes to the recent and diverse literature on the relation between downside risk and asset prices. In chapter one, we use a famous quote among professional investors, "focus on the downside, and the upside will take care of itself", to motivate a representative consumer-investor who only cares about the downside. The consumption-based asset pricing model that emerges from this idea explains the main existing puzzles found within the asset pricing literature. These include the equity premium and the risk-free rate puzzles, the countercyclicality of the equity premium and the procyclicality of the risk-free rate. The model is parsimonious, requiring only three preference-related parameters: the time discount factor, the elasticity of intertemporal substitution, and the downside risk aversion. When we use the model to understand the relation between returns and consumption in the US, we find that the fitted parameter values are consistent with what is expected from the micro foundations. In chapter two, we show that the model proposed in chapter one can also explain the financial puzzles in other developed countries. This is an important step in the empirical validation of the model. The estimated parameters are robust across highly capitalized countries and qualitatively close to the ones obtained for the US. Moreover, the risk measure under the quantile utility model can better justify the differences in risk premia across countries when compared to the risk measure under the expected utility model. In chapter three, we evaluate the effect of margin requirements on asset prices, an additional channel for the relation between downside risk and prices. We provide evidences of the existence of an aggregate margin-related premium in the economy. In particular, we show that (i) a margin-related factor is able to predict future excess returns of the S&P 500 and (ii) stocks with high betas on the margin-related factor pay on average higher returns compared those with low margin betas. These result are important not only to understand asset prices, but also the unconventional polices implemented by the Fed during the great recession of 2007-2010. Although data on margin requirements for the S&P 500 futures are publicly available, it is in general very hard to obtain information on margins for other assets. Given that, we also propose a nonparametric model for estimating margins as a function of the asset's value at risk. This is theoretically justifiable and has good empirical results.

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.

The dramatic growth of commodity index investment over the last decade has caused a heated debate regarding its impact on commodity prices among legislators, practitioners and academics. This paper focuses on the unique rolling activity of commodity index investors in the commodity futures markets and shows that the price impact due to this rolling activity is both statistically and economically significant. Two simple trading strategies, devised to exploit this market anomaly, yielded excess returns with positive skewness and annual Sharpe ratios as high as 4.4 in the period January 2000 to March 2010. The profitability of these trading strategies is decreasing in the amount of arbitrage capital employed in the futures markets and increasing in the size of index funds' investment relative to the total size of futures markets. Due to the price impact, index investors forwent on average 3.6\% annual return, a 48\% higher Sharpe ratio of the return, and billions of dollars over this period.

The purpose of this dissertation is to understand the risks embedded in Carry Trades. For this, we use a broad range of stochastic volatility (SV) models, estimate them using Bayesian techniques via Markov chain Monte Carlo methods, and analyze various risk measures using these estimation results. Many researchers have tried to explain the risk factors deriving Carry returns with standard risk models (factor models, Sharp ratios etc.). However, the high negative conditional skewness of Carry Trades hints the existence of jumps and shows that they have non normal returns, suggesting looking only at first two moments such as sharp ratios or using standard risk models are not enough to understand their risks. Therefore, we investigate Carry risks by delving into its SV and jump components and separate out their effects for a more thorough analysis. We also compare these results with other market portfolios (S&P 500, Fama HML, Momentum, Gold, AUD/USD, Euro/USD, USD/JPY, DXY, Long Rate Carry and Delta Short Rate Carry) to be able to judge the riskiness of Carry relative to other investment alternatives. We then introduce a new model diagnostic method, which overcomes the flaws of the previous methods used in the literature. This is important since model selection is a central question in SV literature, and although various methods were suggested earlier, they do not provide a reliable measure of fit. Using this new diagnostic method, we select the best-fitted SV model for each portfolio and use their estimation results to carry out the risk analysis. We find that the extremes of volatility, direct negative impact of volatilities on returns, percent of overall risk due to jumps considering both returns and vols, and negative skewness are all more pronounced for Carry Trades than for other portfolios. This shows that Carry risks are more complicated than other portfolios. Hence, we are able to remove a layer from the Carry risks by analyzing its jump and SV components in more depth. We also present the rolling correlations of these portfolio returns, vols, and jumps to understand if they co-move and how these co-movements change over time. We find that despite being dollar-neutral, Carry is still prone to dollar risk. DXY-S&P appear to be negatively correlated after 2003, when dollar becomes a safe-haven investment. S&P-AUD are very positively correlated since both are risky assets, except during currency specific events such as central bank interventions. MOM becomes negatively correlated with Carry during crisis and recovery periods since MOM yields positive returns in crisis and its returns plunge in recovery. Carry-Gold are mostly positively correlated, which might be used to form more enhanced trading and hedging strategies. Carry-S&P are mostly very positively correlated, and their jump probability correlations peak during big financial events. Delta Carry, on the other hand, distinguishes from other portfolios as a possible hedging instrument. It is not prominently correlated to any of the portfolios. These correlations motivate us to search for common factors deriving the 11 portfolios under consideration. We find through the Principal Component Analysis that there are four main components to explain their returns and two main components to explain their vols. Moreover, the first component in volatility is the common factor deriving all risky asset vols, explaining 75% of the total variance. To model this dynamic relationship between these portfolios, we estimate a multivariate normal Markov switching (MS) model using them. Then we develop a dynamic trading strategy, in which we use the MS model estimation results as input to the mean-variance optimization to find the optimal portfolio weights to invest in at each period. This trading strategy is able to dynamically diversify between the portfolios, and having a sharp ratio of 1.25, it performs much better than the input and benchmark portfolios. Finally, MS results indicate that Delta Carry has the lowest variance and positive expected return in both states of the MS model. This supports our findings from risk analysis that Delta Carry performs well during volatile periods, and vol elevations have a direct positive impact on its returns.

This dissertation is composed of three chapters. In Chapter 1 I look at the role of real exchange rates in the asset pricing of currencies. I construct portfolios based on signals about the real exchange rate and analyze the returns of these portfolios as they relate to traditional asset pricing factors and especially how they correlate with carry trade portfolios. Deviations from long term averages of real exchange rates are found to be predictors of crash risk. I also show that there is significant information in real exchange rate signals that does not seem to be priced. In addition to demonstrating this in outright currency markets I provide evidence suggesting that this is also the case in options markets. A relationship between real exchange rates and the VIX volatility over long periods is also demonstrated. The distribution of returns depends on state variables. For currencies an important variable is the deviations of real exchange rates from their long run means and for stocks the market-to-book ratio serves a similar purpose. Chapter 2 introduces a variant of a mixture of normals that allows for dependence of this kind. The model is estimated using Markov chain Monte Carlo. The results clearly indicate conditionality on the state variables and how high prices of assets predict negative skewness (large losses) for as well as negative returns. The Icelandic financial collapse, which occurred in the fall of 2008, is without precedent. Never before in modern history has an entire financial system of a developed country collapsed so dramatically. Chapter 3 describes the country's path towards financial liberalization and the economic background that lead to an initially flourishing banking sector. In doing so the paper elaborates on the economic oversights that were made during the financial build-up of the country and how such mistakes contributed to the crash. The focus is thus on identifying the main factors that contributed to the financial collapse and on drawing conclusions about how these missteps could have been avoided. Also summarized are the mistakes that followed in the attempted rescue phase after the disaster had struck. The paper discusses these issues from a general perspective in order to provide an overview of the pitfalls that any fast growing market may be exposed to. The paper concludes that the economic collapse was primarily home-brewed and a consequence of an unbound, risk-seeking banking sector and ineffective (or non-existent) actions of the Icelandic authorities.

Different fields of economics have historically tended to focus on firms' strategies in isolation. In contrast, a lot of the recent work explores how various aspects of firm behavior interact with each other. This dissertation contributes to this growing literature by studying the interdependences of organizational and financial policies within firms in different contexts. The first essay studies the interactions between acquisition decisions of multinationals and innovation decisions in the subsidiaries they buy. My coauthors Maria Guadalupe and Catherine Thomas, and I use a rich panel dataset of Spanish manufacturing firms and a propensity score reweighting estimator to show that multinational firms acquire the most productive domestic firms, which, on acquisition, conduct more product and process innovation (simultaneously adopting new machines and organizational practices) and adopt foreign technologies, leading to higher productivity. The proposed model of endogenous selection and innovation in heterogeneous firms can explain both the observed selection patterns and the innovation decisions. The innovation upon acquisition is further shown in the data to be associated with the increased market scale provided by the parent firm, thereby highlighting the role of foreign ownership in increasing the benefits from innovation. This work has potentially important implications for the evolution of within-industry productivity distributions. Under the mechanism described in the paper, foreign entry may lead to divergence of productivity and contribute to the stylized fact of large and persistent productivity differences even within narrowly defined industries. I further use this rich dataset in my second essay to establish a causal relationship between the use of flexible contractual arrangements with labor and capital structure of the firm. Using the exogenous inter-temporal variation from government subsidies, I find that hiring more temporary workers leads firms to have more debt. Since temporary workers, unlike permanent ones, can be fired at a much lower cost during their contract duration, or their contracts may be not extended upon expiration, a firm can more easily meet its interest payments and avoid bankruptcy when faced with a negative shock. I interpret this result as evidence of flexible workforce decreasing operating leverage which, in turn, promotes financial leverage. This study therefore contributes to the literature exploring the interactions between firm employment decisions and corporate policies by providing evidence for a new channel - the one of flexible employment contracts. Given the overwhelming extent of labor reforms in continental Europe in recent years that are aimed at offering more job security to workers, it is important to understand how such policies would affect firms, and for that it is necessary to model the interdependences of firms' strategies. Finally, my third essay looks at a different type of firms - hedge funds. Although, they do not produce goods in a strict sense of the word, they provide valuable services to investors by smartly investing into large selections of assets. Hedge funds are a very interesting type of financial firms to study due to their lower regulation and reporting standards that enable them to use some know-how trading strategies and potentially outperform other investors. A part of such outperformance can be explained by higher risks born by certain hedge funds, which outlines the broad question we explore in this paper with my coauthor Sergiy Gorovyy. We use a proprietary dataset obtained from a fund of funds to study the risk premia associated with hedge fund transparency, liquidity, complexity, and concentration over the period from April 2006 to March 2009. We are able to directly measure these qualitative characteristics by using the internal grades that the fund of funds attached to all the funds it invested in, and that represent the unique information that cannot be obtained from quantitative data alone. Consistent with factor models of risk premium, we find that during the normal times low-transparency, low-liquidity, low-complexity, and high-concentration funds delivered a return premium, with economic magnitudes of 5% to 10% per year, while during bad states of the economy, these funds experienced significantly lower returns. We also offer a novel explanation for why highly concentrated funds command a risk premium by revealing that it is mostly prevalent among the non-transparent funds where investors are unaware about the exact risks they are facing and hence cannot diversify them away. The large an significant return premium associated with more secretive, less transparent hedge funds has an important policy implication with respect to whether hedge funds should be required to disclose the information regarding their trades and positions, especially in the light of the recent regulatory changes, including the Dodd-Frank Wall Street Reform Act passed in July 2010, the consequences of which are yet to be evaluated.

This dissertation analyzes hedge fund leverage and its determinants, investigates optimal hedge fund manager behavior induced by hedge fund contracts, and uncovers an evidence of a hedge fund transparency risk premium. The first essay investigates the leverage of hedge funds in the time series and cross-section. Hedge fund leverage is found to be counter-cyclical to the leverage of listed financial intermediaries. Changes in hedge fund leverage tend to be more predictable by economy-wide factors than by fund-specific characteristics. In particular, decreases in funding costs and increases in market values both forecast increases in hedge fund leverage. Decreases in fund return volatilities predict future increases in leverage. In the second essay, I investigate hedge fund compensation from an investor's point of view in a model with a risk neutral fund manager who can continuously rebalance the fund's holdings. I solve for the optimal leverage level in a fund that has a compensation contract with a high-water mark and hurdle rate provisions where management and performance fees are paid at discrete time moments. The compensation contract induces risk-loving behavior with managers often choosing the maximum leverage. Third essay investigates risk premia associated with hedge fund transparency, liquidity, complexity, and concentration over the period from April 2006 to March 2009. Consistent with factor models of risk, we find that during normal times low-transparency, low-liquidity, and high-concentration funds delivered a return premium, with economic magnitudes of 5% to 10% per year, while during bad states of the economy, these funds experienced significantly lower returns. We also offer a novel explanation for why highly concentrated funds command a risk premium by revealing that the risk premium is mostly prevalent among non-transparent funds where investors are unaware about the exact risks they are facing and hence cannot diversify them away.

My dissertation aims at understanding the dynamics of asset prices empirically. It contains three chapters. Chapter One provides an estimator for the conditional expectation function using a partially misspecified model. The estimator automatically detects the dimensions along which the model quality is good (poor). The estimator is always consistent, and its rate of convergence improves toward the parametric rate as the model quality improves. These properties are confirmed by both simulation and empirical application. Application to the pricing of Treasury options suggests that the cheapest-to-deliver practice is an important source of misspecification. Chapter Two examines the informational content of credit default swap (CDS) net notional for future stock and CDS prices. Using the information on CDS contracts registered in DTCC, a clearinghouse, I construct CDS-to-debt ratios from net notional, that is, the sum of net positive positions of all market participants, and total outstanding debt issued by the reference entity. Unlike the ratio using the sum of all outstanding CDS contracts, this ratio directly indicates how much of debt is insured with CDS and therefore, is a natural measure of investors concern on a credit event of the reference entity. Empirically, I find cross-sectional evidence that the current increase in CDS to- debt ratios can predict a decrease in stock prices and an increase in CDS premia of the reference firms in the next week. Greater predictability for firms with investment grade credit ratings or low CDS-to debt ratios suggests that investors pay more attention to firms in good credit conditions than those regarded as junk or already insured considerably with CDS. Chapter Three tests the relationship between credit default swap net notional and put option prices. Given motivation that both CDS and put options are used not only as a type of insurance but also for negative side bets, both contemporaneous and predictive analysis are performed for put option returns and changes in implied volatilities with time-to-maturities of 1, 3, and 6 months. The results show that there is no empirical evidence that CDS net notional and put option prices are closely connected.

Building on insights from the economics of superstars, I develop an efficient method for estimating the skill of mutual fund managers. Outliers are especially helpful for disentangling skill from luck when I explicitly model the cross-sectional distribution of managerial skill using a flexible and realistic function. Forecasted performance is dramatically improved relative to standard regression estimates: an investor selecting (avoiding) the best (worst) decile of funds would improve risk-adjusted performance by 2% (3%) annually. The distribution of skill is found to be fat-tailed and positively skewed, providing a theoretical explanation for the convexity of fund flows.

This dissertation delves into the relation between venture capital and innovation. The existing literature usually addresses this question by using industry-level data. In contrast, the analysis here relies on data at the company level on patents invented in venture-backed companies. The dissertation has four parts. The first part, a paper coauthored with my advisors Bruce Kogut and Morten Sorensen, examines the relation between venture capital and the rate and quality of companies' innovative activity. We compare the number of patent filings, and the quality of innovations, before and after companies are first financed by a venture capital investor. As an attempt to control for the endogeneity of venture capital investments we exploit amendment by the Texas Legislature that freed public state pension funds in Texas to invest in venture capital. Our results suggest that venture funding increases the rate of companies' innovative activity. Interestingly, we also find that venture capital is associated with a decrease in the quality of companies' research output. The second part estimates the effect of venture capital on the diffusion of knowledge. I compare citations to patents invented in venture-backed companies to those of comparable patents invented elsewhere. To isolate the causal effect, I exploit time variation in the assets of state pension funds that allocate capital to venture capital. This variation provides a valid instrument if the effect of changes in innovation opportunities within a state is uniform across local patents in the same technology-class and vintage-year. I find that after venture funding annual citations to a given patent increase 19% relative to the citations of comparable patents. Additional results are consistent with two mechanisms: venture capital investors certify the value of patents to the general public and facilitate communication among companies in their portfolios. The third part of this dissertation explores whether the strategic interaction of companies in the same venture capital network affects the direction of companies' innovative activity. Theoretically, this effect is not clear. Whereas the presence of common investors can stir companies' research in the same direction by facilitating knowledge spillovers, competition for the same financial resources may undermine the incentives of companies in the same venture capital network to collaborate, or even work in similar areas. To test this question empirically I use the propensity of patent citations among pairs of companies as a measure of the similarity in companies' research. To reduce concerns of strategic investment by venture capital investors, I control in the estimation for the technological similarity and geographical co-location of companies. Consistent with venture capitalists facilitating the diffusion of knowledge across the companies they finance I find that companies in the same venture capital network produce similar innovations. Interestingly, I also find that this convergence in innovation is only true for companies that are not competing for the same financial resources, specifically, those pairs of companies that are geographically distant or work in different technological areas and industries. Results suggest that the optimal strategy for companies that are competing for the same financial resources is to differentiate and pursue different lines of research. Finally, the Appendix describes in detail the construction of the dataset.