This thesis contains three essays. In the first essay, I provide new evidence on the failure of the Q theory of investment. The Q theory implies the state-by-state equivalence of stock returns and investment returns. However in the data, I find that investment and stock returns are negatively correlated. I also show that a production economy with time-to-build can explain these empirical facts. When I compute Q theory based investment returns on simulated data of the time-to-build model, they are uncorrelated with simulated stock returns, as in the data. Moreover, the model replicates the empirical negative correlation between stock returns and investment growth which some researchers have interpreted as evidence for irrational markets. In the second essay, I analyze the equilibrium effects of investment commitment on asset prices when the representative consumer has Epstein-Zin utility. Investment commitment captures the idea that long-term investment projects require not only current expenditures but also commitment to future expenditures. The general equilibrium effects of investment commitment and Epstein-Zin preferences generate endogenously time-varying first and second moments of consumption growth and stock returns. As a result, the first and second moments of excess returns are endogenously counter-cyclical, excess returns are predictable, and the equity premium increases by an order of magnitude. This paper also offers novel empirical findings regarding the predictability of returns. In the real and simulated data, the lagged investment rate helps to forecast the mean and volatility of returns. In the third essay, we embed a structural model of credit risk inside a consumption based model, which allows us to price equity and corporate debt in a single framework. Our key economic assumptions are that the first and second moments of earnings and consumption growth depend on the state of the economy which switches randomly, creating intertemporal risk, which agents prefer to resolve quickly because they have Epstein- Zin-Weil preferences. Our model generates co-movement between aggregate stock return volatility and credit spreads, consistent with the data, and potentially resolves the equity risk premium and credit spread puzzles.

<p>The central puzzles in financial economics commonly include</p><p>violations of the expectations hypotheses, predictability of excess returns, and the levels and volatilities of nominal bond yields, in addition to well-known equity premium and the risk-free rate puzzles.</p><p>Equally surprising is the recent evidence on large moves in asset prices, and the over-pricing of the out-of-the-money index put options relative to standard models. In this work, I argue that the long-run risks type model can successfully explain these features of financial markets. I present robust empirical evidence which supports the main economic channels in the model. Finally, I develop econometric methods to estimate and test the model, and find that it delivers plausible preference and model parameters and provides a good fit to the asset-price and macroeconomic data.</p><p>In the first chapter, which is co-authored with Ravi Bansal, we present a long-run risks based equilibrium model that can quantitatively explain the violations of expectations hypotheses and predictability of returns in bond and currency markets. The key ingredients of the model include a low-frequency predictable component in consumption, time-varying consumption volatility and investor's preferences for early resolution of uncertainty. In this model, varying consumption volatility in the presence of the predictable consumption component leads to appropriate variation in bond yields and the risk premia to provide an explanation for the puzzling violations of the expectations hypothesis. Using domestic and foreign consumption and asset markets data we provide direct empirical support for the economic channels highlighted in the paper.</p><p>In the second chapter, co-authored with Ravi Bansal, we develop a general equilibrium model in which income and dividends are smooth, but asset prices are subject to large moves (jumps). A prominent feature of the model is that the optimal decision of investors to learn the unobserved state triggers large asset-price jumps. We show that the learning choice is critically determined by preference parameters and the conditional volatility of income process. An important prediction of the model is that income volatility predicts future jumps, while the variation in the level of income does not. We find that indeed in the data large moves in returns are predicted by consumption volatility, but not by the changes in the consumption level. In numerical calibrations, we show that the model can quantitatively capture these novel features of the data.</p><p>In the third chapter, I present a long-run risks type model where consumption shocks are Gaussian, and the agent learns about unobserved expected growth from the cross-section of signals. The uncertainty about expected growth (confidence measure), as in the data, is time-varying and subject to jump-like risks. I show that the confidence jump risk channel can quantitatively account for the option price puzzles and large moves in asset prices, without hard-wiring jumps into consumption. Based on two estimation approaches, the model provides a good fit to the option price, confidence measure, returns and consumption data, at the plausible preference and model parameter values.</p> / Dissertation

This thesis contains three essays. In the first essay, I provide new evidence on the failure of the Q theory of investment. The Q theory implies the state-by-state equivalence of stock returns and investment returns. However in the data, I find that investment and stock returns are negatively correlated. I also show that a production economy with time-to-build can explain these empirical facts. When I compute Q theory based investment returns on simulated data of the time-to-build model, they are uncorrelated with simulated stock returns, as in the data. Moreover, the model replicates the empirical negative correlation between stock returns and investment growth which some researchers have interpreted as evidence for irrational markets. In the second essay, I analyze the equilibrium effects of investment commitment on asset prices when the representative consumer has Epstein-Zin utility. Investment commitment captures the idea that long-term investment projects require not only current expenditures but also commitment to future expenditures. The general equilibrium effects of investment commitment and Epstein-Zin preferences generate endogenously time-varying first and second moments of consumption growth and stock returns. As a result, the first and second moments of excess returns are endogenously counter-cyclical, excess returns are predictable, and the equity premium increases by an order of magnitude. This paper also offers novel empirical findings regarding the predictability of returns. In the real and simulated data, the lagged investment rate helps to forecast the mean and volatility of returns. In the third essay, we embed a structural model of credit risk inside a consumption based model, which allows us to price equity and corporate debt in a single framework. Our key economic assumptions are that the first and second moments of earnings and consumption growth depend on the state of the economy which switches randomly, creating intertemporal risk, which agents prefer to resolve quickly because they have Epstein- Zin-Weil preferences. Our model generates co-movement between aggregate stock return volatility and credit spreads, consistent with the data, and potentially resolves the equity risk premium and credit spread puzzles.

This thesis contains three essays. In the first essay, I provide new evidence on the failure of the Q theory of investment. The Q theory implies the state-by-state equivalence of stock returns and investment returns. However in the data, I find that investment and stock returns are negatively correlated. I also show that a production economy with time-to-build can explain these empirical facts. When I compute Q theory based investment returns on simulated data of the time-to-build model, they are uncorrelated with simulated stock returns, as in the data. Moreover, the model replicates the empirical negative correlation between stock returns and investment growth which some researchers have interpreted as evidence for irrational markets. In the second essay, I analyze the equilibrium effects of investment commitment on asset prices when the representative consumer has Epstein-Zin utility. Investment commitment captures the idea that long-term investment projects require not only current expenditures but also commitment to future expenditures. The general equilibrium effects of investment commitment and Epstein-Zin preferences generate endogenously time-varying first and second moments of consumption growth and stock returns. As a result, the first and second moments of excess returns are endogenously counter-cyclical, excess returns are predictable, and the equity premium increases by an order of magnitude. This paper also offers novel empirical findings regarding the predictability of returns. In the real and simulated data, the lagged investment rate helps to forecast the mean and volatility of returns. In the third essay, we embed a structural model of credit risk inside a consumption based model, which allows us to price equity and corporate debt in a single framework. Our key economic assumptions are that the first and second moments of earnings and consumption growth depend on the state of the economy which switches randomly, creating intertemporal risk, which agents prefer to resolve quickly because they have Epstein- Zin-Weil preferences. Our model generates co-movement between aggregate stock return volatility and credit spreads, consistent with the data, and potentially resolves the equity risk premium and credit spread puzzles. / Business, Sauder School of / Graduate

No / This paper investigates the equilibrium relationship between taxation, portfolio choice (risk-taking) and capital accumulation. Specifically, it examines how taxes affect risk-taking and capital accumulation. We extend the existing literature by relaxing two crucial assumptions in modelling risk-taking behavior: (i) that the investment opportunity set is fixed and (ii) that there is no distinction between attitudes towards risk and behavior towards intertemporal substitution. We extend the investment opportunity set of individuals through optimally determined human capital; and distinguish intertemporal substitution from attitudes towards risk via a recursive utility function. In the presence of these extensions, the paper successfully derives a closed-form solution to the stochastic growth model with stochastic wage income.

No / We compare actual and calibrated values for the foreign exchange risk premium based on the definition in [J. Int. Econ. 32 (1992) 305]. Calibrated values are found from within a dynamic stochastic general equilibrium model of a small open economy consisting of risk averse optimizing agents with unconventional preferences. We find that the equilibrium foreign exchange risk premium is a function of exogenous shocks in the model and is sensitive to assumed attitudes towards risk. Furthermore, various forms of policy uncertainty improve the capacity of the model to generate values closer to those found in the data.

I consider continuous time asset pricing models with stochastic differential utility incorporating decision makers' concern with ambiguity on true probability measure. In order to identify and estimate key parameters in the models, I use a novel econometric methodology developed recently by Park (2008) for the statistical inference on continuous time conditional mean models. The methodology only imposes the condition that the pricing error is a continuous martingale to achieve identification, and obtain consistent and asymptotically normal estimates of the unknown parameters. Under a representative agent setting, I empirically evaluate alternative preference specifications including a multiple-prior recursive utility. My empirical findings are summarized as follows: Relative risk aversion is estimated around 1.5-5.5 with ambiguity aversion and 6-14 without ambiguity aversion. Related, the estimated ambiguity aversion is both economically and statistically significant and including the ambiguity aversion clearly lowers relative risk aversion. The elasticity of intertemporal substitution (EIS) is higher than 1, around 1.3-22 with ambiguity aversion, and quite high without ambiguity aversion. The identification of EIS appears to be fairly weak, as observed by many previous authors, though other aspects of my empirical results seem quite robust. Next, I develop an approach to test for martingale in a continuous time framework. The approach yields various test statistics that are consistent against a wide class of nonmartingale semimartingales. A novel aspect of my approach is to use a time change defined by the inverse of the quadratic variation of a semimartingale, which is to be tested for the martingale hypothesis. With the time change, a continuous semimartingale reduces to Brownian motion if and only if it is a continuous martingale. This follows immediately from the celebrated theorem by Dambis, Dubins and Schwarz. For the test of martingale, I may therefore see if the given process becomes Brownian motion after the time change. I use several existing tests for multivariate normality to test whether the time changed process is indeed Brownian motion. I provide asymptotic theories for my test statistics, on the assumption that the sampling interval decreases, as well as the time horizon expands. The stationarity of the underlying process is not assumed, so that my results are applicable also to nonstationary processes. A Monte-Carlo study shows that our tests perform very well for a wide range of realistic alternatives and have superior power than other discrete time tests.

Cette thèse se positionne au croisement de la théorie de la décision en univers incertain et de la théorie des choix d’investissements irréversibles (options réelles). Elle poursuit trois objectifs principaux :1. Tout d’abord, elle s’inscrit dans un courant de recherche dynamique, notamment en économie et en finance, qui vise à modéliser l’impact de l’ambigüité à laquelle des décideurs sont parfois confrontés lorsqu’ils contemplent des choix aux conséquences irréversibles. 2. Ensuite, elle met l’accent sur la persistance de fortes controverses théoriques portant sur les fondements axiomatiques des modèles de décision face à l’ambigüité. Aussi, nous proposons d’utiliser certaines propriétés des modèles non linéaires pour aborder sous un angle original la représentation de l’ambigüité et des préférences des individus face à celle-ci. En particulier, nous suggérons de ne pas restreindre a priori la nature des préférences individuelles face à l’ambigüité. Pour cela, nous adoptons les fondements de l’approche de Choquet, à savoir tout particulièrement l’emploi de capacités (probabilités non additives) pour pondérer les différentes alternatives ambigües. Tout en proposant ce processus stochastique ambigu, dit Choquet-Brownien, nous soulignons les conditions de l’inévitable arbitrage entre réalisme des hypothèses et souplesse d’utilisation du modèle. D’un point de vue axiomatique, une attention particulière est portée au respect de la cohérence dynamique.3. Enfin, cette thèse vise à encourager une prise en considération plus ambitieuse des sources d’incertitude dans le cadre des options réelles. Alors qu’ils sont présentés comme des outils privilégiés pour affronter le risque, les modèles d’options réelles ont certainement beaucoup à gagner à s’enrichir par la prise en compte également de l’ambigüité. En effet, alors que le risque est largement discuté dans la littérature des options réelles, l’impact de l’ambigüité est très largement ignoré. / The need to elaborate innovative methods to analyze risk and uncertainty has become increasingly obvious over the last decades, especially due the growing perception of the multiplicity of social and economical issues characterized by the weight of uncertainty (natural disasters, ecological risk, financial crises…).This thesis is at the crossroad between decision theory under uncertainty and the irreversible investment theory (real options). Consequently, the main goal of this thesis is three-fold: 1. First, it contributes to the dynamic stream of literature in economics and finance that models the impact of ambiguity that individuals may often face and/or perceive when contemplating irreversible choices.2. Next, this thesis emphasizes that even with the plethora of decision models already dealing with uncertainty, elaborating sound axiomatic foundations largely remains an open question. This leads us to recommending the use of non linear models (such as multiple-priors, Choquet expected utility, robust control, smooth ambiguity), which in turn raises many challenging theoretical and practical obstacles. We explore original ways of addressing some of these issues and suggest the construction of ambiguous stochastic processes in a Choquet expected utility framework (that are called Choquet-Brownian motions): ambiguity preferences are thereby directly embedded into the trajectory of some random variables that may drive a decision, such as the expected cash flows of an investment project or its exit value.3. Finally, this thesis also aims specifically at encouraging the enrichment of real option models. It is striking that only the impact of risk has been widely discussed by the real option theory so far, while the specific impact of ambiguity has been largely ignored. Considering that the real option theory is directly concerned with sources of flexibility, irreversibility and uncertainty in general, ambiguity represents a promising expansion.

Le choix de portefeuille optimal d'actifs a été depuis longtemps et continue d'être un sujet d'intérêt majeur dans le domaine de la finance. L'objectif principal étant de trouver la meilleure façon d'allouer les ressources financières dans un ensemble d'actifs disponibles sur le marché financier afin de réduire les risques de fluctuation du portefeuille et d'atteindre des rendements élevés. Néanmoins, la littérature de choix de portefeuille a connu une avancée considérable à partir du 20ieme siècle avec l'apparition de nombreuses stratégies motivées essentiellement par le travail pionnier de Markowitz (1952) qui offre une base solide à l'analyse de portefeuille sur le marché financier. Cette thèse, divisée en trois chapitres, contribue à cette vaste littérature en proposant divers outils économétriques pour améliorer le processus de sélection de portefeuilles sur le marché financier afin d'aider les intervenants de ce marché. Le premier chapitre, qui est un papier joint avec Marine Carrasco, aborde un problème de sélection de portefeuille avec coûts de transaction sur le marché financier. Plus précisément, nous développons une procédure de test simple basée sur une estimation de type GMM pour évaluer l'effet des coûts de transaction dans l'économie, quelle que soit la forme présumée des coûts de transaction dans le modèle. En fait, la plupart des études dans la littérature sur l'effet des coûts de transaction dépendent largement de la forme supposée pour ces frictions dans le modèle comme cela a été montré à travers de nombreuses études (Dumas and Luciano (1991), Lynch and Balduzzi (1999), Lynch and Balduzzi (2000), Liu and Loewenstein (2002), Liu (2004), Lesmond et al. (2004), Buss et al. (2011), Gârleanu and Pedersen (2013), Heaton and Lucas (1996)). Ainsi, pour résoudre ce problème, nous développons une procédure statistique, dont le résultat est indépendant de la forme des coûts de transaction, pour tester la significativité de ces coûts dans le processus d'investissement sur le marché financier. Cette procédure de test repose sur l'hypothèse que le modèle estimé par la méthode des moments généralisés (GMM) est correctement spécifié. Un test commun utilisé pour évaluer cette hypothèse est le J-test proposé par Hansen (1982). Cependant, lorsque le paramètre d'intérêt se trouve au bord de l'espace paramétrique, le J-test standard souffre d'un rejet excessif. De ce fait, nous proposons une procédure en deux étapes pour tester la sur-identification lorsque le paramètre d'intérêt est au bord de l'espace paramétrique. Empiriquement, nous appliquons nos procédures de test à la classe des anomalies utilisées par Novy-Marx and Velikov (2016). Nous montrons que les coûts de transaction ont un effet significatif sur le comportement des investisseurs pour la plupart de ces anomalies. Par conséquent, les investisseurs améliorent considérablement les performances hors échantillon en tenant compte des coûts de transaction dans le processus d'investissement. Le deuxième chapitre aborde un problème dynamique de sélection de portefeuille de grande taille. Avec une fonction d'utilité exponentielle, la solution optimale se révèle être une fonction de l'inverse de la matrice de covariance des rendements des actifs. Cependant, lorsque le nombre d'actifs augmente, cet inverse devient peu fiable, générant ainsi une solution qui s'éloigne du portefeuille optimal avec de mauvaises performances. Nous proposons deux solutions à ce problème. Premièrement, nous pénalisons la norme des poids du portefeuille optimal dans le problème dynamique et montrons que la stratégie sélectionnée est asymptotiquement efficace. Cependant, cette méthode contrôle seulement en partie l'erreur d'estimation dans la solution optimale car elle ignore l'erreur d'estimation du rendement moyen des actifs, qui peut également être importante lorsque le nombre d'actifs sur le marché financier augmente considérablement. Nous proposons une méthode alternative qui consiste à pénaliser la norme de la différence de pondérations successives du portefeuille dans le problème dynamique pour garantir que la composition optimale du portefeuille ne fluctue pas énormément entre les périodes. Nous montrons que, sous des conditions de régularité appropriées, nous maîtrisons mieux l'erreur d'estimation dans le portefeuille optimal avec cette nouvelle procédure. Cette deuxième méthode aide les investisseurs à éviter des coûts de transaction élevés sur le marché financier en sélectionnant des stratégies stables dans le temps. Des simulations ainsi qu'une analyse empirique confirment que nos procédures améliorent considérablement la performance du portefeuille dynamique. Dans le troisième chapitre, nous utilisons différentes techniques de régularisation (ou stabilisation) empruntées à la littérature sur les problèmes inverses pour estimer le portefeuille diversifié tel que définie par Choueifaty (2011). En effet, le portefeuille diversifié dépend du vecteur de volatilité des actifs et de l'inverse de la matrice de covariance du rendement des actifs. En pratique, ces deux quantités doivent être remplacées par leurs contrepartie empirique. Cela génère une erreur d'estimation amplifiée par le fait que la matrice de covariance empirique est proche d'une matrice singulière pour un portefeuille de grande taille, dégradant ainsi les performances du portefeuille sélectionné. Pour résoudre ce problème, nous étudions trois techniques de régularisation, qui sont les plus utilisées : le rigde qui consiste à ajouter une matrice diagonale à la matrice de covariance, la coupure spectrale qui consiste à exclure les vecteurs propres associés aux plus petites valeurs propres, et Landweber Fridman qui est une méthode itérative, pour stabiliser l'inverse de matrice de covariance dans le processus d'estimation du portefeuille diversifié. Ces méthodes de régularisation impliquent un paramètre de régularisation qui doit être choisi. Nous proposons donc une méthode basée sur les données pour sélectionner le paramètre de stabilisation de manière optimale. Les solutions obtenues sont comparées à plusieurs stratégies telles que le portefeuille le plus diversifié, le portefeuille cible, le portefeuille de variance minimale et la stratégie naïve 1 / N à l'aide du ratio de Sharpe dans l'échantillon et hors échantillon. / The optimal portfolio selection problem has been and continues to be a subject of interest in finance. The main objective is to find the best way to allocate the financial resources in a set of assets available on the financial market in order to reduce the portfolio fluctuation risks and achieve high returns. Nonetheless, there has been a strong advance in the literature of the optimal allocation of financial resources since the 20th century with the proposal of several strategies for portfolio selection essentially motivated by the pioneering work of Markowitz (1952)which provides a solid basis for portfolio analysis on the financial market. This thesis, divided into three chapters, contributes to this vast literature by proposing various economic tools to improve the process of selecting portfolios on the financial market in order to help stakeholders in this market. The first chapter, a joint paper with Marine Carrasco, addresses a portfolio selection problem with trading costs on stock market. More precisely, we develop a simple GMM-based test procedure to test the significance of trading costs effect in the economy regardless of the form of the transaction cost. In fact, most of the studies in the literature about trading costs effect depend largely on the form of the frictions assumed in the model (Dumas and Luciano (1991), Lynch and Balduzzi (1999), Lynch and Balduzzi (2000), Liu and Loewenstein (2002), Liu (2004), Lesmond et al. (2004), Buss et al. (2011), Gârleanu and Pedersen (2013), Heaton and Lucas (1996)). To overcome this problem, we develop a simple test procedure which allows us to test the significance of trading costs effect on a given asset in the economy without any assumption about the form of these frictions. Our test procedure relies on the assumption that the model estimated by GMM is correctly specified. A common test used to evaluate this assumption is the standard J-test proposed by Hansen (1982). However, when the true parameter is close to the boundary of the parameter space, the standard J-test based on the chi2 critical value suffers from overrejection. To overcome this problem, we propose a two-step procedure to test overidentifying restrictions when the parameter of interest approaches the boundary of the parameter space. In an empirical analysis, we apply our test procedures to the class of anomalies used in Novy-Marx and Velikov (2016). We show that transaction costs have a significant effect on investors' behavior for most anomalies. In that case, investors significantly improve out-of-sample performance by accounting for trading costs. The second chapter addresses a multi-period portfolio selection problem when the number of assets in the financial market is large. Using an exponential utility function, the optimal solution is shown to be a function of the inverse of the covariance matrix of asset returns. Nonetheless, when the number of assets grows, this inverse becomes unreliable, yielding a selected portfolio that is far from the optimal one. We propose two solutions to this problem. First, we penalize the norm of the portfolio weights in the dynamic problem and show that the selected strategy is asymptotically efficient. However, this method partially controls the estimation error in the optimal solution because it ignores the estimation error in the expected return, which may also be important when the number of assets in the financial market increases considerably. We propose an alternative method that consists of penalizing the norm of the difference of successive portfolio weights in the dynamic problem to guarantee that the optimal portfolio composition does not fluctuate widely between periods. We show, under appropriate regularity conditions, that we better control the estimation error in the optimal portfolio with this new procedure. This second method helps investors to avoid high trading costs in the financial market by selecting stable strategies over time. Extensive simulations and empirical results confirm that our procedures considerably improve the performance of the dynamic portfolio. In the third chapter, we use various regularization (or stabilization) techniques borrowed from the literature on inverse problems to estimate the maximum diversification as defined by Choueifaty (2011). In fact, the maximum diversification portfolio depends on the vector of asset volatilities and the inverse of the covariance matrix of assets distribution. In practice, these two quantities need to be replaced by their sample counterparts. This results in estimation error which is amplified by the fact that the sample covariance matrix may be close to a singular matrix in a large financial market, yielding a selected portfolio far from the optimal one with very poor performance. To address this problem, we investigate three regularization techniques, such as the ridge, the spectral cut-off, and the Landweber-Fridman, to stabilize the inverse of the covariance matrix in the investment process. These regularization schemes involve a tuning parameter that needs to be chosen. So, we propose a data-driven method for selecting the tuning parameter in an optimal way. The resulting regularized rules are compared to several strategies such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio.