This dissertation studies the impact of multiple pre-specified sources of risk in the return of three non-overlapping groups of countries, through an Arbitrage Pricing Theory (APT) model. The groups are composed of emerging and developed markets. Emerging markets have become important players in the world economy, especially as capital receptors, but they were not included in the majority of previous related works. Two strategies are used to choose two set of risk factors. The first one is to use macroeconomic variables, as prescribed by most of the literature, such as world excess return, exchange rates, variation in the spread between Eurodollar deposit tax and U.S. Treasury bill (TED spread) and change in the oil price. The second strategy is to extract factors by using a principal component analysis, designated as statistical factors. The first important result is a great resemblance between the first statistical factor and the world excess return. We estimate the APT model using two statistical methodologies: Iterated Nonlinear Seemingly Unrelated Regression (ITNLSUR) by McElroy and Burmeister (1988) and the Generalized Method Moments (GMM) by Hansen (1982). The results from both methods are very similar. With macroeconomic variables, only the world excess of return is priced in the three groups with a premium varying from 4.4% to 6.3% per year and, in the model with statistical variables, only the first statistical factor is priced in all groups with a premium varying from 6.2% to 8.5% per year. / Essa dissertação estuda o impacto de múltiplas fontes de riscos pré-especificados nos retornos de três grupos de países não sobrepostos, através de um modelo de Teoria de Precificação por Arbitragem (APT). Os grupos são compostos por mercados emergentes e desenvolvidos. Mercados emergentes tornaram-se importantes na economia mundial, especialmente como receptores de capital, mas não foram inclusos na maioria dos trabalhos correlatos anteriores. Duas estratégias foram adotadas para a escolha de dois conjuntos de fatores de risco. A primeira foi utilizar variáveis macroeconômicas, descritas na maior parte da literatura, como e excesso de retorno da carteira mundial, taxas de câmbio, variação da diferença entre a taxa de depósito em Eurodólar e a U.S. Treasury Bill (TED Spread) e mudanças no preço do petróleo. A segunda estratégia foi extrair fatores de risco através de uma análise de componentes principais, denominados fatores estatísticos. O primeiro resultado importante é a grande semelhança entre o primeiro fator estatístico e o retorno da carteira mundial. Nós estimamos o modelo APT usando duas metodologias estatísticas: Regressões Aparentemente não Correlacionadas Iteradas (ITNLSUR) de McElroy e Burmeister (1988) e o Método dos Momentos Generalizados (GMM) de Hansen (1982). Os resultados de ambas as metodologias são muito similares. Utilizando variáveis macroeconômicas, apenas o excesso de retorno da carteira mundial é precificado nos três grupos com prêmios variando de 4,4% a 6.3% ao ano e, no modelo com variáveis estatísticas, apenas o primeiro fator estatístico é precificado em todos os grupos com prêmios que variam entre 6,2% a 8,5% ao ano.

In this paper, I examine the top ten historical upward and downward daily shocks in the Dow Jones Industrial Average, and test whether industry specific abnormal returns can be explained by industry specific leverage ratios on those days. I use modified versions of the Capital Asset Pricing Model and the Fama French 3 Factor regression to examine within-industry abnormal returns. I then proceed to rank the industry abnormal returns and industry leverage ratios, from high to low, on days corresponding to these large shocks. Finally, I examine the correlation between these ranks on the days corresponding to the large moves. The results show that on upward moving days, there is no relationship between industry abnormal returns and industry leverage. However, on downward moving days, there is moderate negative correlation between industry abnormal returns and leverage, suggesting that higher leverage leads to lower abnormal returns. This paper explains these results in further detail, and discusses the implications to the greater field of financial economics.

The first paper revisits the link between interest rates and corporate bond credit spreads by applying Rigobon’s (2003) heteroskedasticity identification methodology. The second paper investigates the assumption that financial asset prices including stocks and bonds, reflect intrinsic value. The third paper decomposes the stock price into fundamental permanent, fundamental transitory, and non-fundamental shocks in order to explore the determinants of stock price fluctuations.

The goal of this thesis is to study the two key aspects of complexity of the financial system: interconnectedness and nonlinear relationships. In Chapter 1, I contribute to the literature that focuses on modelling the nonlinear relationship between variables at the extremes of their distribution. In particular, I study the nonlinear relationship between stock prices and short selling. Whereas most of the academic literature has focused on measuring the relationship between short selling and asset returns on average, in Chapter 1, I focus on studying the relationship that arises in the extremes of the two variables. I show that the association between financial stock prices and short selling can become extremely strong under exceptional circumstances, while at the same time being weak in normal times. The tail relationship is stronger for small cap firms, a result that is intuitively in line with the empirical findings that stocks with lower liquidity are more price-sensitive to short selling. Finally, results show that the adverse tail correlation between increases in short selling and declines in stock prices was not always lower during the ban periods, but had declined markedly towards the end of the analysis window. Such results cast doubts about the effectiveness of bans as a way to prevent self-reinforcing downward price spirals during the crisis. In Chapter 2, I propose a measure of interconnectedness that takes into account the time-varying nature of connections between financial institutions. Here, the parameters underlying comovement are allowed to evolve continually over time through permanent shifts at every period. The result is an extremely flexible measure of interconnectedness, which uncovers new dynamics of the US financial system and can be used to monitor financial stability for regulatory purposes. Various studies have combined statistical measures of association (e.g. correlation, Granger causality, tail dependence) with network techniques, in order to infer financial interconnectedness (Billio et al. 2012; Barigozzi and Brownlees, 2016; Hautsch et al. 2015). However, these standard statistical measures presuppose that the inferred relationships are time-invariant over the sample used for the estimation. To retrieve a dynamic measure of interconnectedness, the usual approach has been to divide the original sample period into multiple subsamples and calculate these statistical measures over rolling windows of data. I argue that this is potentially unsuitable if the system studied is time-varying. By relying on short subsamples, rolling windows lower the power of inference and induce dimensionality problems. Moreover, the rolling window approach is known to be susceptible to outliers because, in small subsamples, these have a larger impact on estimates (Zivot and Wang, 2006). On the other hand, choosing longer windows will lead to estimates that are less reactive to change, biasing results towards time-invariant connections. Thus, the rolling window approach requires the researcher to choose the window size, which involves a trade-off between precision and flexibility (Clark and McCracken, 2009). The choice of window size is critical and can lead to different results regarding interconnectedness. The major novelty of the framework is that I recover a network of financial spillovers that is entirely dynamic. To do so, I make the modelling assumption that the connection between any two institutions evolves smoothly through time. I consider this assumption reasonable for three main reasons. First, since connections are the result of many financial contracts, it seems natural that they evolve smoothly rather than abruptly. Second, the assumption implies that the best forecast of a connection in the future is the state of that connection today. This is consistent with the notion of forward-looking prices. Third, the assumption allows for high flexibility and for the data to speak for itself. The empirical results show that financial interconnectedness peaked around two main events: the Long-Term Capital Management crisis of 1998 and the great financial crisis of 2008. During these two events, I found that large banks and broker/dealers were among the most interconnected sectors and that real estate companies were the most vulnerable to financial spillovers. At the individual financial institution level, I found that Bear Stearns was the most vulnerable financial institution, however, it was not a major propagator, and this might explain why its default did not trigger a systemic crisis. Finally, I ranked financial institutions according to their interconnectedness and I found that rankings based on the time-varying approach were more stable than rankings based on other market-based measures (e.g. marginal expected short fall by Acharya et al. (2012) and Brownlees and Engle (2016)). This aspect is significant for policy makers because highly unstable rankings are unlikely to be useful to motivate policy action (Danielsson et al. 2015; Dungey et al. 2013). In Chapter 3, rather than assuming interconnectedness as an exogenous process that has to be inferred, as is done in Chapter 2, I model interconnectedness as an endogenous function of market dynamics. Here, I take interconnectedness as the realized correlation of asset returns. I seek to understand how short selling can induce higher interconnectedness by increasing the negative price pressure on pairs of stocks. It is well known that realized correlation varies continually through time and becomes higher during market events, such as the liquidation of large funds. Most studies model correlation as an exogenous stochastic process, as is done, for example, in Chapter 2. However, recent studies have proposed to interpret correlation as an endogenous function of the supply and demand of assets (Brunnermeier and Pedersen, 2005; Brunnermeier and Oehmke, 2014; Cont and Wagalath, 2013; Yang and Satchell, 2007). Following these studies, I analyse the relationship between short selling and correlation between assets. First, thanks to new data on public short selling disclosures for the United Kingdom, I connect stocks based on the number of common short sellers actively shorting them. I then analyse the relationship between common short selling and excess correlation of those stocks. To this end, I measure excess correlation as the monthly realized correlation of four-factor Fama and French (1993) and Carhart (1997) daily returns. I show that common short selling can predict one-month ahead excess correlation, controlling for similarities in size, book-to-market, momentum, and several other common characteristics. I verify the confirm the predictive ability of common short selling out-of-sample, which could prove useful for risk and portfolio managers attempting to forecast the future correlation of assets. Moreover, I showed that this predictive ability can be used to establish a trading strategy that yields positive cumulative returns over 12 months. In the second part of the chapter I concentrate on possible mechanisms that could give rise to this effect. I focus on three, non-exclusive, mechanisms. First, short selling can induce higher correlation in asset prices through the price-impact mechanism (Brunnermeier and Oehmke, 2014; Cont and Wagalath, 2013). According to this mechanism, short sellers can contribute to price declines by creating sell-order imbalances i.e. by increasing excess supply of an asset. Thus, short selling across several stocks should increase the realized correlation of those stocks. Second, common short selling can be associated with higher correlation if short sellers are acting as voluntary liquidity providers. According to this mechanisms, short sellers might act as liquidity providers in times of high buy-order imbalances (Diether et al. 2009b). In this cases, the low returns observed after short sales might be compensations to short sellers for providing liquidity. In a multi-asset setting, this mechanism would result in short selling being associated with higher correlation mechanism. Both above-mentioned mechanisms deliver a testable hypothesis that I verify. In particular, both mechanisms posit that the association between short selling and correlation should be stronger for stocks which are low on liquidity. For the first mechanism, the price impact effect should be stronger for illiquid stocks and stocks with low market depth. For the liquidity provision mechanism, the compensation for providing liquidity should be higher for illiquid stocks. The empirical results cannot confirm that uncovered association between short selling and correlation is stronger for illiquid stocks, thus not supporting the price-impact and liquidity provision hypothesis. I thus examine a third possible mechanism that could explain the uncovered association between short selling and correlation i.e. the informative trading mechanism. Short sellers have been found to be sophisticated market agents which can predict future returns (Dechow et al. 2001). If this is indeed the case, then short selling should be associated with higher future correlation. I found that informed common short selling i.e. common short selling that is linked to informative trading, was strongly associated to future excess correlation. This evidence supports the informative trading mechanism as an explanation for the association between short selling and correlation. In order to further verify this mechanism, I checked if informed short selling takes place in the data, whilst controlling for several of the determinants of short selling, including short selling costs. The results show evidence of both informed and momentum-based non-informed short selling taking place. Overall, the results have several policy implications for regulators. The results suggest that the relationship between short selling and future excess correlation is driven by informative short selling, thus confirming the sophistication of short sellers and their proven importance for market efficiency and price informativeness (Boehmer and Wu, 2013). On the other hand, I could not dismiss that also non-informative momentum-based short selling is taking place in the sample. The good news is that I did not find evidence of a potentially detrimental price-impact effect of common short selling for illiquid stock, which is the sort of predatory effect that regulators often fear. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Magister Commercii - MCom / A distinctive phenomenon on the JSE Securities Exchange (JSE) is the market segmentation between the resource sector and the financial and industrial sectors. Criticisms also arise from employing a capitalization-weighted (cap-weighted) index such as the ALSI index when the market is less than perfectly efficient. A study conducted by Vardharah and Fabozzi (2007) also suggests that a correlation exists between sector allocation decisions and the investment styles inherent in portfolios. The uniqueness of the South African stock market is that it is dominated by three major sectors, namely, the financial sector, the industrial sector and the resources sector. The goal of this research is to examine the application of sector influences on the JSE over the examination period 1 January 2003 to 31 December 2013. It is the contention that the cap-weighted ALSI index is price-sensitive and potentially mean-variance inefficient. The study therefore attempts to evaluate the relative meanvariance efficiency of alternative sector allocation strategies versus the cap-weighted ALSI as the optimal risky portfolio on the JSE. Two optimal long-only portfolios that maximises the Sharpe ratio are constructed and compared to the market proxy on the JSE over the examination period from 1 January 2003 to 31 December 2013. A longonly portfolio that comprises the JSE tradable sector indices and includes a cash allocation (risk-free proxy) and a long-only portfolio exclusive of the cash allocation are constructed. The research extends to cross-examine the inter-relationship between sector returns and the investment styles on the JSE using the Carhart (1997) four-factor model. The research further reexamines and updates the market segmentation phenomenon over the extended examination period from 1 January 2003 to 31 December 2013. The practicality of two sector-based multifactor APT models are examined and compared to the single-factor CAPM to determine which of the asset pricing models better explain JSE equity returns. A sector-based two-factor APT model proposed by Van Rensburg (2002) using the JSE sector indices FNDI and RESI as the sector proxies is reexamined and a sector-based three-factor APT model using the JSE tradable sector indices FINI, INDI and RESI as the sector proxies is explored. The optimal long-only portfolio with the cash allocation is found to offer the best meanvariance efficient allocation and the ALSI index represents the most mean-variance inefficient portfolio. The resource sector is found to be the worst performing sector and significantly influences the performance of ALSI. In terms of the style risk influences, the financial sector has a strong value bias and the industrial sector has a moderate value bias, small cap bias and a momentum bias. The resource sector, for the most part, is influenced by growth stocks and has a contrarian tilt. It is also found that the market segmentation phenomenon continues to exist on the JSE. Although the explanatory power of the three-factor APT model and the two-factor APT model is similar, the distinct advantage of the three-factor APT model is that systematic risks could be observed more closely by separating FINI and INDI in the asset pricing model.

Face à un constat de déconnexion entre la valorisation de trois types d’actifs (deux actifs financiers et un actif immobilier) et les fondamentaux économiques dans la période récente, marquée par l’occurrence de crises économiques et financières sévères, cette thèse vise à mettre en évidence le rôle des anticipations des agents économiques.Premièrement, cette thèse souligne que le rôle des anticipations dans la récente crise de dette en zone euro est lié à l’existence de complémentarités stratégiques dans les décisions des agents économiques. Dans cette perspective, l’apport de cette thèse est de s’intéresser à un fait central et pourtant passé relativement inaperçu : la hausse de la part de dette détenue par les résidents dans les économies les plus fragiles de la zone euro à partir de la fin 2008. Nous montrons que si les chocs d’endettement public ont bien un effet positifs ur le biais domestique dans la détention de dette publique, les chocs d’anticipations pessimistes peuvent également jouer un rôle significatif pour expliquer les variations du biais domestique. Deuxièmement, cette thèse montre que la volatilité excessive du prix de certains actifs relativement aux fondamentaux peut être expliquée dans le cadre de modèles standards dès lors que l’on relâche l’hypothèse d’anticipations rationnelles et que l’on suppose que les agents estiment les paramètres des lois gouvernant la dynamique des fondamentaux, à la façon d’économètres. Sous cette hypothèse, un modèle de prix d’actifs standard permet d’expliquer l’épisode de forte valorisation du prix des actions américaines au début des années 2000, suivi par un fort effondrement des cours à partir de 2008 et jusqu’à mi-2009.Enfin, nous montrons que modéliser un processus d’apprentissage bayésien sur le prix des actifs immobiliers dans le cadre d’un modèle DSGE avec des frictions de crédit permet d’expliquer simultanément la forte volatilité des prix immobiliers - qui a joué un rôle central dans la crise des subprimes - et celle de variables caractérisant le cycle des affaires aux Etats-Unis sur la période 1985-2015. / In view of the disconnect between the pricing of three types of assets (two types of financial assets and real-estate assets) and economic fundamentals in the recent period ofsevere economic and financial crises, this thesis aims at highlighting the role of economicagents’ expectations.First, this thesis emphasizes that the role of expectations in the recent Eurozonesovereign debt crisis relates to strategic complementarities in agents’ decisions. In thisrespect, this thesis focuses on one major but mostly unnoticed fact: the share of governmentdebt held by the resident sector increased beginning at the end of 2008 in the mostfragile economies of the zone. We show that – whereas public debt shocks positively affectthe home bias in sovereign debt – pessimistic expectation shocks can also significantlyexplain variations in home bias.Second, this thesis shows that excess volatility in stock and in house prices relativeto fundamentals can be accounted for by standard models when the rational expectationshypothesis is relaxed and when agents are assumed to estimate the parameters of the lawsof motion driving the dynamics of economic fundamentals – that is, as econometricians.Under this assumption, a standard asset pricing model can explain the persistently highvaluation in US stock prices in the early 2000s followed by their dramatic bust by 2009.Finally, we show that modelling Bayesian learning regarding house prices in the contextof a DSGE model with credit frictions allows us to simultaneously replicate the dramaticvolatility in house prices - which played a crucial role in the subprime crisis - and in businesscycle variables over the 1985-2015 period.

In this dissertation, I investigate three related topics on asset pricing: the consumption-based asset pricing under long-run risks and fat tails, the pricing of VIX (CBOE Volatility Index) options and the market price of risk embedded in stock returns and stock options. These three topics are fully explored in Chapter II through IV. Chapter V summarizes the main conclusions. In Chapter II, I explore the effects of fat tails on the equilibrium implications of the long run risks model of asset pricing by introducing innovations with dampened power law to consumption and dividends growth processes. I estimate the structural parameters of the proposed model by maximum likelihood. I find that the stochastic volatility model with fat tails can, without resorting to high risk aversion, generate implied risk premium, expected risk free rate and their volatilities comparable to the magnitudes observed in data. In Chapter III, I examine the pricing performance of VIX option models. The contention that simpler-is-better is supported by the empirical evidence using actual VIX option market data. I find that no model has small pricing errors over the entire range of strike prices and times to expiration. In general, Whaley’s Black-like option model produces the best overall results, supporting the simpler-is-better contention. However, the Whaley model does under/overprice out-of-the-money call/put VIX options, which is contrary to the behavior of stock index option pricing models. In Chapter IV, I explore risk pricing through a model of time-changed Lévy processes based on the joint evidence from individual stock options and underlying stocks. I specify a pricing kernel that prices idiosyncratic and systematic risks. This approach to examining risk premia on stocks deviates from existing studies. The empirical results show that the market pays positive premia for idiosyncratic and market jump-diffusion risk, and idiosyncratic volatility risk. However, there is no consensus on the premium for market volatility risk. It can be positive or negative. The positive premium on idiosyncratic risk runs contrary to the implications of traditional capital asset pricing theory.

This thesis provides a study on stochastic models of financial markets related to problems of asset pricing and hedging, optimal portfolio managing and statistical changepoint detection in trends of asset prices. Chapter 1 develops a general model of a system of interconnected stochastic markets associated with a directed acyclic graph. The main result of the chapter provides sufficient conditions of hedgeability of contracts in the model. These conditions are expressed in terms of consistent price systems, which generalise the notion of equivalent martingale measures. Using the general results obtained, a particular model of an asset market with transaction costs and portfolio constraints is studied. In the second chapter the problem of multi-period utility maximisation in the general market model is considered. The aim of the chapter is to establish the existence of systems of supporting prices, which play the role of Lagrange multipliers and allow to decompose a multi-period constrained utility maximisation problem into a family of single-period and unconstrained problems. Their existence is proved under conditions similar to those of Chapter 1.The last chapter is devoted to applications of statistical sequential methods for detecting trend changes in asset prices. A model where prices are driven by a geometric Gaussian random walk with changing mean and variance is proposed, and the problem of choosing the optimal moment of time to sell an asset is studied. The main theorem of the chapter describes the structure of the optimal selling moments in terms of the Shiryaev–Roberts statistic and the posterior probability process.

A plethora of recent literature on asset pricing provides plenty of empirical evidence on the importance of liquidity, governance and adverse selection of equity on pricing of assets together with more traditional factors such as market beta and the Fama-French factors. However, literature has usually stressed that these factors are priced individually. In this dissertation we argue that these factors may be related to each other, hence not only individual but also joint tests of their significance is called for. In the three related essays, we examine the liquidity premium in the context of the finer three-digit SIC industry classification, joint importance of liquidity and governance factors as well as governance and adverse selection. Recent studies by Core, Guay and Rusticus (2006) and Ben-Rephael, Kadan and Wohl (2010) find that governance and liquidity premiums are dwindling in the last few years. One reason could be that liquidity is very unevenly distributed across industries. This could affect the interpretation of prior liquidity studies. Thus, in the first chapter we analyze the relation of industry clustering and liquidity risk following a finer industry classification suggested by Johnson, Moorman and Sorescu (2009). In the second chapter, we examine the dwindling influence of the governance factor if taken simultaneously with liquidity. We argue that this happens since governance characteristics are potentially a proxy for information asymmetry that may be better captured by market liquidity of a company’s shares. Hence, we jointly examine both the factors, namely, governance and liquidity – in a series of standard asset pricing tests. Our results reconfirm the importance of governance and liquidity in explaining stock returns thus independently corroborating the findings of Amihud (2002) and Gompers, Ishii and Metrick (2003). Moreover, governance is not subsumed by liquidity. Lastly, we analyze the relation of governance and adverse selection, and again corroborate previous findings of a priced governance factor. Furthermore, we ascertain the importance of microstructure measures in asset pricing by employing Huang and Stoll’s (1997) method to extract an adverse selection variable and finding evidence for its explanatory power in four-factor regressions.

This paper analyzes U.S. factor ETF risk exposures and diversification benefits relative to the ETFs’ academic factor portfolios. The purpose of the paper is to observe whether the factor ETFs’ correlations and risk exposures reflect that of their academic factor portfolios, the long-short and long-only portfolios. The results exhibit the market factor as the fundamental agent of returns, although size, value, and momentum also provide exposure to the intended factors. When measuring the loadings of factor ETFs and their intended factor portfolios, the long-short investing approach provides the most optimal diversification strategy.