<p>The three essays in this dissertation explore the role of fluctuations in aggregate volatility and global temperature as sources of systemic risk. </p><p>The first essay proposes a production-based asset pricing model and provides empirical evidence suggesting that compensation for volatility risk is closely related to an unexplored characteristic of a firm, namely, its reliance on skilled labor. I propose a model in which aggregate growth has time-varying volatility, and linear adjustment costs in labor increase with the skill of a worker. The model predicts that expected returns increase with a firm's reliance on skilled labor, as well as compensation for fluctuations in aggregate uncertainty. Consequently, a rise in aggregate uncertainty predicts an increase in expected returns as well as in cautiousness in hiring and firing. This impact is larger for firms with a high share of skilled workers because their labor is more costly to adjust. I empirically test the implications of the model using occupational estimates to construct a measure of a firm's reliance on skilled labor, and find a positive and statistically significant cross-sectional relation between the reliance on skilled labor and expected returns. Empirical estimates also show that an increase in aggregate uncertainty leads to a rise in expected returns, and this impact is larger for firms which rely heavily on skilled labor; thereby, a firm's exposure to aggregate volatility is positively related to its reliance on skilled labor.</p><p>In the second and third essay, co-authored with Ravi Bansal, we explore the impact of global temperature on financial markets and the macroeconomy. In tho second essay we explore if temperature is an aggregate risk factor that adversely affects economic growth. First, using data on global capital markets we find that the risk-exposure of these returns to temperature shocks, i.e., their temperature beta, is a highly significant variable in accounting for cross-sectional differences in expected returns. Second, using a panel of countries we show that GDP growth is negatively related to global temperature, suggesting that temperature can be a source of aggregate risk. To interpret the empirical evidence, we present a quantitative consumption-based long-run risks model that quantitatively accounts for the observed cross-sectional differences in temperature betas, the compensation for temperature risk, and the connection between aggregate growth and temperature risks. </p><p>The last essay proposes a general equilibrium model that simultaneously models the world economy and global climate to understand the impact of climate change on the economy. We use this model to evaluate the role of temperature in determining asset prices, and to compute utility-based welfare costs as well as dollar costs of insuring against temperature fluctuations. We find that the temperature related utility-costs are about 0.78% of consumption, and the total dollar costs of completely insuring against temperature variation are 2.46% of world GDP. If we allow for temperature-triggered natural disasters to impact growth, insuring against temperature variation raise to 5.47% of world GDP.</p> / Dissertation

Submitted by Lira Rocha da Mota (liramota@gmail.com) on 2017-06-25T23:20:17Z No. of bitstreams: 1 Tese.pdf: 2641768 bytes, checksum: 9529228790bc66dfec234ca17131f8d8 (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2018-09-13T19:00:28Z (GMT) No. of bitstreams: 1 Tese.pdf: 2641768 bytes, checksum: 9529228790bc66dfec234ca17131f8d8 (MD5) / Made available in DSpace on 2018-09-27T13:14:10Z (GMT). No. of bitstreams: 1 Tese.pdf: 2641768 bytes, checksum: 9529228790bc66dfec234ca17131f8d8 (MD5) Previous issue date: 2016-09-19 / Essa tese é composta por três capítulos. O primeiro capítulo é dedicado a estudar o impacto causal de restrições de venda a descoberto sobre retorno de ações. A base de dados utilizada neste trabalho possui todas as transações de aluguel de ações que aconteceram no Brasil no período de Janeiro de 2007 até Junho de 2013. Uma oportunidade de arbitragem fiscal sobre o recebimento de Juros de Capital Próprio (possível até o final de 2015) gera uma variação exógena na taxa de aluguel. Os resultados mostram um aumento médio de 73% das taxas de aluguel em contratos especulativos que é exógeno às características da ação, que por sua vez, causa um retorno acumulado anormal de 0.38% nos oito dias úteis pós a data de ex-dividendo. No segundo capítulo, a mesma base de dados é utilizada para documentar que a opacidade do mercado de balcão, no qual os contratos de aluguel de ações são transacionados, gera dispersão da taxa e aluguel. Os principais resultados são 1. a medida de dispersão de preço é o melhor preditor de retornos quando comparada com medidas tradicionais na literatura relacionadas a venda descoberto: taxa de aluguel média, “short-interest” e “days to cover”; 2. uma nova versão de “short-premium” representada por um portfólio long-short formando com base na dispersão da taxa de aluguel apresenta retorno médio mensal de 1.03% acima do CDI e 0.76% alpha sobre quatro fatores. Finalmente, o terceiro capítulo desafia a prática comum na literatura de formar fatores de risco baseado em características das ações. Neste capítulo é introduzido um método para fazer previsão de covariâncias com alto poder estatístico. Usando esse método é possível construir portfólios que capturam o prêmio associado a característica, mas que faz o "hedge" de grande parte do risco associado ao fator. Quando essa metodologia é aplicada aos cinco-fatores de Fama e French (2015), é possível construir um portfólio ortogonal aos fatores com Sharpe-ratio de 0.84. / This thesis is composed by three chapters. The first chapter aims to estimate the causal impact of short-selling restrictions on returns. Taking advantage of a unique dataset that unifies all stock loan transactions in Brazil from January 2007 to June 2013, we exploit a source of exogenous variation in loan fees provided by a tax-arbitrage opportunity that existed in Brazil until end 2015. We show that exogenous relative increase in lending fees for speculative reasons is on average 73%, causing an average 0.38% cumulative abnormal return on the 8-trading days following the ex-dividend day. In the second chapter, we show that the over-the-counter (OTC) generates loan fee dispersion across loan contracts for the same stock and the same day. We find that loan fee dispersion is the best predictor of the cross-section of stock returns, when compared to traditional short-sale related measures in the literature: loan fee average, short-interest or days to cover. We document a newer version of the “short premium": the small dispersion-minus-large dispersion (sDmlD) portfolio of stocks has a monthly average excess return of 1.03% and a 0.79% four-factor alpha. Finally, in the third chapter we challenge the common practice in the literature to create factor-portfolios by sorting on stocks characteristics. We introduce a high statistical power methodology to forecast future covariances that is able to select a set of portfolios which capture the characteristic premia, but hedge out much of factor risk. We apply our methodology to the Fama and French (2015) five-factors, and construct a portfolio orthogonal to their factor with annualized Sharpe-ratio of 0.84.

Yes / A key prediction of the Capital Asset Pricing Model (CAPM) is that idiosyncratic risk is not priced by investors because in the absence of frictions it can be fully diversified away. In the presence of constraints on diversification, refinements of the CAPM conclude that the part of idiosyncratic risk that is not diversified should be priced. Recent empirical studies yielded mixed evidence with some studies finding positive correlation between idiosyncratic risk and stock returns, while other studies reported none or even negative correlation. We revisit the problem whether idiosyncratic risk is priced by the stock market and what are the probable causes for the mixed evidence produced by other studies, using monthly data for the US market covering the period from 1980 until 2013. We find that one-period volatility forecasts are not significantly correlated with stock returns. The mean-reverting unconditional volatility, however, is a robust predictor of returns. Consistent with economic theory, the size of the premium depends on the degree of 'knowledge' of the security among market participants. In particular, the premium for Nasdaq-traded stocks is higher than that for NYSE and Amex stocks. We also find stronger correlation between idiosyncratic risk and returns during recessions, which may suggest interaction of risk premium with decreased risk tolerance or other investment considerations like flight to safety or liquidity requirements. The difference between the correlations of the idiosyncratic volatility estimators used by other studies and the true risk metric the mean-reverting volatility is the likely cause for the mixed evidence produced by other studies. Our results are robust with respect to liquidity, momentum, return reversals, unadjusted price, liquidity, credit quality, omitted factors, and hold at daily frequency. / National Research Foundation of Korea (2016S1A2A2912265)

Diese Arbeit besteht aus vier Essays, die empirische und methodische Beiträge zu den Gebieten der Finanzmarktökonomik und der Makroökonomik liefern. Der erste Essay beschäftigt sich mit der Spezifikation der Investoren verfügbaren Informationsmenge in Tests bedingter Kapitalmarktmodelle. Im Speziellen schlägt es die Verwendung dynamischer Faktoren als Instrumente vor. Diese fassen per Konstruktion die Information in einer Vielzahl von Variablen zusammen und stellen daher intuitive Maße für die Investoren zur Verfügung stehenden Informationen dar. Es wird gezeigt, dass so die Schätzfehler bedingter Modelle im Vergleich zu traditionellen, auf einzelnen Indikatoren beruhenden Modellvarianten substantiell verringert werden. Ausgehend von Ergebnissen, dass die Zentralbank zur Festlegung des kurzfristigen Zinssatzes eine große Menge an Informationen berücksichtigt, wird im zweiten Essay im Rahmen eines affinen Zinsstrukturmodells eine ähnliche Idee verwandt. Speziell wird die Dynamik des kurzfristigen Zinses im Rahmen einer Faktor-Vektorautoregression modelliert. Aufbauend auf dieser dynamischen Charakterisierung der Geldpolitik wird dann die Zinsstruktur unter der Annahme fehlender Arbitragemöglichkeiten hergeleitet. Das resultierende Modell liefert bessere Vorhersagen US-amerikanischer Anleihenzinsen als eine Reihe von Vergleichsmodellen. Der dritte Essay analysiert die Vorhersagekraft der Zinsstrukturkomponenten "level", "slope", und "curvature" im Rahmen eines dynamischen Faktormodells für makroökonomische und Zinsdaten. Das Modell wird mit einem Metropolis-within-Gibbs Sampling Verfahren geschätzt, und Überraschungsänderungen der drei Komponenten werden mit Hilfe von Null- und Vorzeichenrestriktionen identifiziert. Die Analyse offenbart, dass der "curvature"-Faktor informativer in Bezug auf die zukünftige Entwicklung der Zinsstruktur und der gesamtwirtschaftlichen Aktivität ist als bislang vermutet. Der vierte Essay legt eine monatliche Chronologie der Konjunkturzyklen im Euro-Raum vor. Zunächst wird mit Hilfe einer verallgemeinerten Interpolationsmethode eine monatliche Zeitreihe des europäischen BIP konstruiert. Anschließend wird auf diese Zeitreihe ein Datierungsverfahren angewandt, das kurze und flache Konjunkturphasen ausschließt. / This thesis consists of four essays of independent interest which make empirical and methodological contributions to the fields of financial economics and macroeconomics. The first essay deals with the proper specification of investors’ information set in tests of conditional asset pricing models. In particular, it advances the use of dynamic factors as conditioning variables. By construction, dynamic factors summarize the information in a large number of variables and are therefore intuitively appealing proxies for the information set available to investors. The essay demonstrates that this approach substantially reduces the pricing errors implied by conditional models with respect to traditional approaches that use individual indicators as instruments. Following previous evidence that the central bank uses a large set of conditioning information when setting short-term interest rates, the second essay employs a similar insight in a model of the term structure of interest rates. Precisely, the dynamics of the short-term interest rate are modelled using a Factor-Augmented Vector-Autoregression. Based on this dynamic characterization of monetary policy, the term structure of interest rates is derived under the assumption of no-arbitrage. The resulting model is shown to provide superior out-of-sample forecasts of US government bond yields with respect to a number of benchmark models. The third essay analyzes the predictive information carried by the yield curve components level, slope, and curvature within a joint dynamic factor model of macroeconomic and interest rate data. The model is estimated using a Metropolis-within-Gibbs sampling approach and unexpected changes of the yield curve components are identified employing a combination of zero and sign restrictions. The analysis reveals that the curvature factor is more informative about the future evolution of the yield curve and of economic activity than has previously been acknowledged. The fourth essay provides a monthly business cycle chronology for the Euro area. A monthly series of Euro area real GDP is constructed using an interpolation routine that nests previously suggested approaches as special cases. Then, a dating routine is applied to the interpolated series which excludes business cycle phases that are short and flat.

Cette thèse, divisée en trois chapitres, contribue à la vaste et récente littérature sur l'évaluation des actifs et la finance climatique. Le premier chapitre contribue à la littérature sur la finance climatique tandis que les deux derniers contribuent à la littérature sur l'évalutaion des actifs. Le premier chapitre analyse comment les politiques environnementales visant à réduire les émissions de carbone affectent les prix des actifs et la consommation des ménages. En utilisant de nouvelles données, je propose une mesure des émissions de carbone du point de vue du consommateur et une mesure du risque de croissance de la consommation de carbone. Les mesures sont basées sur des informations sur la consommation totale et l'empreinte carbone de chaque bien et service. Pour analyser les effets des politiques environnementales, un modèle de risques de long terme est développé dans lequel la croissance de la consommation comprend deux composantes: le taux de croissance de la consommation de carbone et le taux de croissance de la part de la consommation de carbone dans la consommation totale. Ce chapitre soutient que le risque de long terme de la croissance de la consommation provient principalement de la croissance de la consommation de carbone découlant des politiques et des actions visant à réduire les émissions, telles que l'Accord de Paris et la Conférence des Nations Unies sur le changement climatique (COP26). Mon modèle aide à détecter le risque de long terme dans la consommation des politiques climatiques tout en résolvant simultanément les énigmes de la prime de risque et de la volatilité, et en expliquant la coupe transversale des actifs. La décomposition de la consommation pourrait conduire à identifier les postes de consommation les plus polluants et à construire une stratégie d'investissement minimisant ou maximisant un critère environnemental de long terme. Le deuxième chapitre (co-écrit avec René Garcia et Caio Almeida) étudie le rôle des facteurs non linéaires indépendants dans la valorisation des actifs. Alors que la majorité des facteurs d'actualisation stochastique (SDF) les plus utilisés qui expliquent la coupe transversale des rendements boursiers sont obtenus à partir des composantes principales linéaires, nous montrons dans ce deuxième chapitre que le fait de permettre la substitution de certaines composantes principales linéaires par des facteurs non linéaires indépendants améliore systématiquement la capacité des facteurs d'actualisation stochastique de valoriser la coupe transversale des actifs. Nous utilisons les 25 portefeuilles de Fama-French, cinquante portefeuilles d'anomalies et cinquante anomalies plus les termes d'interaction basés sur les caractéristiques pour tester l'efficacité des facteurs dynamiques non linéaires. Le SDF estimé à l'aide d'un mélange de facteurs non linéaires et linéaires surpasse ceux qui utilisent uniquement des facteurs linéaires ou des rendements caractéristiques bruts en termes de performance mesurée par le R-carré hors échantillon. De plus, le modèle hybride - utilisant à la fois des composantes principales non linéaires et linéaires - nécessite moins de facteurs de risque pour atteindre les performances hors échantillon les plus élevées par rapport à un modèle utilisant uniquement des facteurs linéaires. Le dernier chapitre étudie la prévisibilité du rendement des anomalies à travers les déciles à l'aide d'un ensemble de quarante-huit variables d'anomalie construites à partir des caractéristiques de titres individuels. Après avoir construit les portefeuilles déciles, cet article étudie leur prévisibilité en utilisant leurs propres informations passées et d'autres prédicteurs bien connus. Les analyses révèlent que les rendements des portefeuilles déciles sont persistants et prévisibles par le ratio de la valeur comptable sur la valeur de marché de l'entreprise, la variance des actions, le rendement des dividendes, le ratio des prix sur les dividendes, le taux de rendement à long terme, le rendement des obligations d'entreprise, le TED Spread et l'indice VIX. De plus, une stratégie consistant à prendre une position longue sur le décile avec le rendement attendu le plus élevé et à prendre une position courte sur le décile avec le rendement attendu le plus bas chaque mois donne des rendements moyens et un rendement par risque bien meilleurs que la stratégie traditionnelle fondée sur les déciles extrêmes pour quarante-cinq des quarante-huit anomalies. / This thesis, divided into three chapters, contributes to the vast and recent literature on asset pricing, and climate finance. The first chapter contributes to the climate finance literature while the last two contribute to the asset pricing literature. The first chapter analyzes how environmental policies that aim to reduce carbon emissions affect asset prices and household consumption. Using novel data, I propose a measure of carbon emissions from a consumer point of view and a carbon consumption growth risk measure. The measures are based on information on aggregate consumption and the carbon footprint for each good and service. To analyze the effects of environmental policies, a long-run risks model is developed where consumption growth is decomposed into two components: the growth rate of carbon consumption and the growth rate of the share of carbon consumption out of total consumption. This paper argues that the long-run risk in consumption growth comes mainly from the carbon consumption growth arising from policies and actions to curb emissions, such as the Paris Agreement and the U.N. Climate Change Conference (COP26). My model helps to detect long-run risk in consumption from climate policies while simultaneously solving the equity premium and volatility puzzles, and explaining the cross-section of assets. The decomposition of consumption could lead to identifying the most polluting consumption items and to constructing an investment strategy that minimizes or maximizes a long-term environmental criterion. The second chapter (co-authored with René Garcia, and Caio Almeida) studies the role of truly independent nonlinear factors in asset pricing. While the most successful stochastic discount factor (SDF) models that price well the cross-section of stock returns are obtained from regularized linear principal components of characteristic-based returns we show that allowing for substitution of some linear principal components by independent nonlinear factors consistently improves the SDF's ability to price this cross-section. We use the Fama-French 25 ME/BM-sorted portfolios, fifty anomaly portfolios, and fifty anomalies plus characteristic-based interaction terms to test the effectiveness of the nonlinear dynamic factors. The SDF estimated using a mixture of nonlinear and linear factors outperforms the ones using solely linear factors or raw characteristic returns in terms of out-of-sample R-squared pricing performance. Moreover, the hybrid model --using both nonlinear and linear principal components-- requires fewer risk factors to achieve the highest out-of-sample performance compared to a model using only linear factors. The last chapter studies anomaly return predictability across deciles using a set of forty-eight anomaly variables built using individual stock characteristics. After constructing the decile portfolios, this paper studies their predictability using their own past information, and other well-known predictors. The analyses reveal that decile portfolio returns are persistent and predictable by book-to-market, stock variance, dividend yield, dividend price ratio, long-term rate of return, corporate bond return, TED Spread, and VIX index. Moreover, a strategy consisting of going long on the decile with the highest expected return and going short on the decile with the lowest expected return each month gives better mean returns and Sharpe ratios than the traditional strategy based on extreme deciles for forty-five out of forty-eight anomalies.