Essay One Under the principal-agent framework, we study and compare different compensation schemes commonly adopted by hedge fund and mutual fund managers. We find that the option-like performance fee structure prevalent among hedge funds is suboptimal to the symmetric performance fee structure. However, the use of high water mark (HWM) mitigates the suboptimality, though to a very limited extent. Bothour theoretical models and simulation results show that HWM will induce more managerial efforts only when a fund is slightly under the water but it will unfavorably dampen incentives when a fund is too deep under the water and when the manager's skill is poor. Allowing managers to invest personal wealth in their own funds, however, helps align interests and provides positive managerial incentives. Essay Two Existing literature has detected a "tournament behavior" among mutual fund managers that mid-year underperformers tend to take relatively higher risk than peers in the second half-year. We reexamine this issue and provide empirical evidence that such behavior does not exist among hedge fund managers, either at fund level or risk style level. Instead, hedge fund managers shift risk at mid-year in response to the moneyness of their incentive contracts. Also, risk shifting decisions are more driven by underperformance than by outperformance. HighWater Mark can strongly rein in excess risk-taking and therefore better aligns interests. Last, risk shifting on average does not improve either performance, moneyness of incentive contracts, or cash inflows. Essay Three We use factor models and optimal change point regression models to capture the intra-year risk dynamics of hedge fund managers. Those risk shifting managers are further divided into 'Informed', 'Uninformed' and 'Misinformed' groups, according to their post-shifting risk adjusted performance. We find evidence that supports the existence of an Adverse Selection' problem of managers compensation schemes. Namely, incentive contracts, designed to share risks and align interests, induce the strongest risk taking from the least informed or skilled hedge fund managers, whose risk-shifting decisions result in undesired or even deteriorated risk-adjusted returns for investors. We also find that the High Water Mark has only limited influence on mitigating excessive risk shifting.

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Regulators have long disagreed whether regulation would reduce hedge funds’ financial misreporting. On the one hand, critics have stated that hedge funds are unlikely to misreport because their investors are highly sophisticated financial players who can detect and deter financial misconduct. On the other hand, recent changes in the composition of hedge funds’ investors have led many to question this argument. In this paper, I test whether hedge fund regulation reduces misreporting by using a quasi-natural experiment in which a subset of hedge funds was regulated, deregulated, and then regulated again. Unique features of the setting permit me to study not only whether hedge fund regulation reduces financial misreporting—but, if so, why the regulation reduces misreporting. The results show that regulation reduces misreporting at hedge funds and that the imposition of disclosure requirements, even without other concurrent changes in regulation, can reduce hedge funds’ misreporting. The result seems surprising, because hedge funds’ investors are commonly thought to have access to far more information than is required by disclosure rules. Further inquiries suggest that disclosure requirements led funds to make changes in their internal governance, and that these changes in governance induced funds to report their financial performance more honestly and accurately.

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Dans cette thèse nous combinons les processus à mémoire longue ainsi que les modèles à changement de régime markovien afin d’étudier la dynamique non linéaire des rentabilités des hedge funds et leur exposition au risque de marché. L’attractivité des hedge funds réside dans leur capacité à générer des rentabilités décorrélées avec celles des actifs traditionnels tout en permettant d’améliorer les rentabilités et/ou de réduire le risque, indépendamment des conditions de marché. Cependant, certaines spécificités des rentabilités des hedge funds (non linéarité, asymétrie et présence d’une forte autocorrélation émanant des problèmes d’illiquidités) remettent en cause cet aspect qui n’est valable que dans un univers gaussien. Nous adoptons de ce fait une approche économétrique permettant de réconcilier la notion de mémoire longue et celle de la persistance pure des performances. Nous mettons l’accent sur le risque de confusion entre vraie mémoire longue et mémoire longue fallacieuse dans la mesure où certains processus peuvent générer des caractéristiques similaires à celles des processus à mémoire longue. Il ressort de cette étude non seulement l’insuffisance des modèles standards à prendre en compte les caractéristiques des séries des rentabilités financières mais aussi la pertinence du recours aux modèles mixtes pour mieux cerner l’ensemble de ces spécificités dans un cadre unifié. Le modèle Beta Switching ARFIMA-FIGARCH que nous proposons révèle la complexité de la dynamique des rentabilités des hedge funds. Il est donc nécessaire de mieux appréhender cette dynamique afin d'expliquer convenablement les interactions qui existent entre les hedge funds eux-mêmes et entre les hedge funds et les marchés standards. La composante mémoire longue est prise en compte à la fois au niveau de la moyenne conditionnelle à travers le processus ARFIMA ainsi qu’au niveau de la variance conditionnelle à travers plusieurs spécifications des processus hétéroscédastiques fractionnaires notamment les processus FIGARCH, FIAPARCH et HYGARCH. Cette modélisation mieux adaptée aux spécificités des hedge funds met en évidence le risque caché de ces derniers et représente une nouvelle perspective vers laquelle les gérants et les responsables d’agence pourraient s’orienter. / In this thesis we combine long memory processes and regime switching models to study the nonlinear dynamics of hedge funds returns and their exposure to market risk. The attractiveness of hedge funds lies in their ability to generate returns uncorrelated to those of traditional assets while allowing to improve returns and/or reduce the risk, regardless of market conditions. However, some specificity of returns of hedge funds as their nonlinear and asymmetric nature as well as the presence of a strong autocorrelation in related to illiquidity problems make this aspect only valid in a Gaussian framework. In this study, we adopt an econometric approach that reconciles the notion of long memory and that of pure performance persistence. In this regard, we focus on the risk of confusion between real and spurious long memory long memory since certain processes can generate similar characteristics to that of long memory processes. It appears from this study not only the inadequacy of standard models to take into account the characteristics of the series of financial returns but also the relevance of using mixed models to better understand all of these features within a unified framework. The Beta Switching ARFIMA-FIGARCH mode we suggest reveals the complexity of hedge fund return dynamics and proves the need to better understand the dynamics of returns of hedge funds in order to explain the interactions between hedge funds themselves and between hedge funds and standard markets. The long memory component is taken into account both at the conditional mean through the ARFIMA process and at the conditional variance through several specifications heteroscedatic fractional processes including FIGARCH, FIAPARCH and HYGARCH models. This model take into account several features of hedge fund returns, highlights their hidden risks and represents a new perspective to which managers could move.

The goal of this Master's Thesis is to analyze the specific characteristics of the hedge funds with a primary focus on profitability and risk of the industry, which has experienced significant growth of the value of assets under management since the beginning of the new millennium. I want to provide a realistic point of view at the whole industry, which is considered as very risky in the eyes of the public. I want to support this point of view with an analysis of the risks and profitability of hedge funds. The single types of hedge funds are compared to each other and also to other asset classes. The first chapter discusses the specifics of hedge funds, so we can easily understand what differs them from the other collective investment funds. I have briefly described the development of the whole industry, which I have supported with graphical and numerical expression. The second chapter is focused on the development of regulation of hedge funds on the US market and on the European continent. The last chapter is dedicated to the analysis of the profitability and risk of hedge funds. The output of this part is the ideal hedge fund for the potential investor.

La recherche menée propose une contribution à l’analyse de la performance des hedge funds dansun contexte d’instabilité des marchés financiers. Ce travail de thèse tourne autour de deux axes deréflexions. Le premier axe se propose d’examiner la structure de dépendance entre les mesures alternativesde performance absolue, en mettant en évidence l’impact du changement de base de donnéeset de période d’analyse sur le classement des indices. Dans ce cadre, nous proposons de comparer laperformance de cinq indices de stratégies issus de trois différentes bases de données. L’analyse esteffectuée sur trois périodes : une période avant crise, une période de crise et une période globale.Nous avons pu montrer que l’évaluation de la performance et la dépendance entre les indicateurssont des éléments fortement sensibles à la période d’analyse. De plus, cette analyse confirme qu’iln’existe pas d’indice "universel" pouvant représenter l’univers des hedge funds. Le deuxième axede réflexion est lié à l’analyse de la stabilité des performances des hedge funds sur trois horizons :le court terme, le moyen terme et le long terme. Il ressort de notre étude que la persistance desperformances des hedge funds n’est pas un phénomène de long terme et que le niveau de persistanceest fortement dépendant de la stratégie d’investissement et de l’indicateur de performance utilisé. / This dissertation provides a contribution to hedge funds performance analysis in a financialmarket instability context. The objective of this thesis is twofold. The first aim is to examine thedependence structure between alternative measures of absolute performance, highlighting the impactof the database switch and analysis period changing on the leaderboard of hedge funds indexes.In this framework, we propose to compare the performance of five hedge funds indexes extractedfrom three different databases. The analysis is conducted over three periods : a pre-crisis period, acrisis period and an overall period. Our findings show that the performance evaluation and dependancebetween performance indicators are highly sensitive to the analysis period. In addition, wefind that there is no "universal" index that can represent hedge funds universe. The second thesis’spurpose is related to the hedge funds performance stability over three horizons : the short term,the medium term and the long term. Firstly, it appears that hedge funds performance persistence isnot a long term phenomenon. Secondly, the persistence level is highly dependent to the investmentstrategy and to the performance indicator used.

This thesis aims to identify main driving market risk factors of different strategies implemented by hedge funds by looking at correlation coefficients, implementing Principal Component Analysis and analyzing "loadings" for first three principal components, which explain the largest portion of the variation of hedge funds' returns. In the next step, a stepwise regression through iteration process includes and excludes market risk factors for each strategy, searching for the combination of risk factors which will offer a model with the best "fit", based on The Akaike Information Criterion - AIC and Bayesian Information Criterion - BIC. Lastly, to avoid counterfeit results and overcome model uncertainty issues a Bayesian Model Average - BMA approach was taken. Key words: Hedge Funds, hedge funds' strategies, market risk, principal component analysis, stepwise regression, Akaike Information Criterion, Bayesian Information Criterion, Bayesian Model Averaging Author's e-mail: aleksaradosavcevic@gmail.com Supervisor's e-mail: mp.princ@seznam.cz

An objective of my graduation thesis is an analysis of hedge funds and hedge strategies while reflecting the distribution of the investments to right portfolio taking into account the risk. The main aim is to provide clear and consistent classification of whole variety of different hedge styles and strategies. There are plenty of different investment and trading strategies of hedge funds and their classification differs from analyst to analyst and from database to database. The work focuses on finding an alternative consistent classification of hedge funds which will lead to improvement of investment decisions of financial market participants, to effective distribution of the investment portfolio and therefore to elimination of undiversified risks. For the practical analysis I use real data of hedge fund returns of particular relevant time period. I focus on research and description of possible methods of hedge fund classification mentioning their pluses and minuses. After passionate evaluation of each method I have chosen two methods according to which I classify the hedge funds datasets and finally I compare the results of both. The theoretical part of work focuses on definition of hedge funds, hedge styles and strategies, pluses and minuses as well as risk accompanying particular strategy.

Thesis (PhD)--Stellenbosch University, 2013. / A persistent problem for hedge fund researchers presents itself in the form of inconsistent and diverse style classifications within and across database providers. For this paper, single-manager hedge funds from the Hedge Fund Research (HFR) and Hedgefund.Net (HFN) databases were classified on the basis of a common factor, extracted using the factor axis methodology. It was assumed that the returns of all sample hedge funds are attributable to a common factor that is shared across hedge funds within one classification, and a specific factor that is unique to a particular hedge fund. In contrast to earlier research and the application of principal component analysis, factor axis has sought to determine how much of the covariance in the dataset is due to common factors (communality). Factor axis largely ignores the diagonal elements of the covariance matrix and orthogonal factor rotation maximises the covariance between hedge fund return series. In an iterative framework, common factors were extracted until all return series were described by one common and one specific factor. Prior to factor extraction, the series was tested for autoregressive moving-average processes and the residuals of such models were used in further analysis to improve upon squared correlations as initial factor estimates. The methodology was applied to 120 ten-year rolling estimation windows in the July 1990 to June 2010 timeframe. The results indicate that the number of distinct style classifications is reduced in comparison to the arbitrary self-selected classifications of the databases. Single manager hedge funds were grouped in portfolios on the basis of the common factor they share. In contrast to other classification methodologies, these common factor portfolios (CFPs) assume that some unspecified individual component of the hedge fund constituents’ returns is diversified away and that single manager hedge funds should be classified according to their common return components. From the CFPs of single manager hedge funds, pure style indices were created to be entered in a multivariate autoregressive framework. For each style index, a Vector Error Correction model (VECM) was estimated to determine the short-term as well as co-integrating relationship of the hedge fund series with the index level series of a stock, bond and commodity proxy. It was postulated that a) in a well-diversified portfolio, the current level of the hedge fund index is independent of the lagged observations from the other asset indices; and b) if the assumptions of the Efficient Market Hypothesis (EMH) hold, it is expected that the predictive power of the model will be low. The analysis was conducted for the July 2000 - June 2010 period. Impulse response tests and variance decomposition revealed that changes in hedge fund index levels are partially induced by changes in the stock, bond and currency markets. Investors are therefore cautioned not to overemphasise the diversification benefits of hedge fund investments. Commodity trading advisors (CTAs) / managed futures, on the other hand, deliver diversification benefits when integrated with an existing portfolio. The results indicated that single manager hedge funds can be reliably classified using the principal factor axis methodology. Continuously re-balanced pure style index representations of these classifications could be used in further analysis. Extensive multivariate analysis revealed that CTAs and macro hedge funds offer superior diversification benefits in the context of existing portfolios. The empirical results are of interest not only to academic researchers, but also practitioners seeking to replicate the methodologies presented.