DAROLLES Serge

This paper analyses the purchase and redemption behaviour of mutual fund investors and its implications on fund liquidity risk. We collect a novel set of proprietary data which contains a large number of French investors holding funds with various degrees of asset liquidity. We build a Self-Exciting Poisson model capturing fund flows' clustering effects and over-dispersion. The model improves the forecast accuracy of future flows and provides a reliable risk indicator (Flow Value at Risk.) Accordingly, we introduce the notion of liability risk where investor's behaviour increases mutual fund liquidity risk. We further decompose fund flows into investor categories. We find that investors exhibit high heterogeneous behaviour, and a lead-lag relation exists between them. Finally, we control flow dynamics for various economic conditions. We show that although flows evolve with economic conditions, investor's behaviour stays the main significant determinant of flows' randomness. Our findings encourage fund manager to adopt an ALM approach.

In this work we propose a forecasting methodology suitable for large panels of liquidity measures based on exploiting the cross-sectional commonality structure of volume. We begin by providing a number of stylized facts for a panel comprising the CAC40 constituents. We document the presence of a strong common component that is correlated with market volatility. Moreover, after the common component is filtered out, we find evidence of dependence across a number of ticker pairs. These stylized facts motivate us to propose a hybrid forecasting model that is made up of a factor and sparse vector-autoregressive components. We estimate such a model by combining PCA (Principal Component Analysis) and LASSO (Least Absolute Shrinkage and Selection Operator) estimation. We apply our methodology to forecast the intra-daily liquidity of the CAC40 constituents across different intra-daily frequencies. Results show that our approach systematically improves forecasting accuracy over a number of univariate and multivariate benchmarks.

Trend-following strategies have met with strong interest from institutional investors in recent years, due in particular to their good performance during the economic and financial crisis of 2008. The years 2016 to 2018 have reshuffled the deck, with performance deemed poor by many clients. This thesis focuses on the different characteristics of trend-following strategies, namely performance, risk and execution costs, and proposes new ways to approach these topics. Chapter 1 explains the difference in performance between hedge fund styles by confirming the presence of trends within the CTA and Global Macro strategies. The insurance nature of this strategy is confirmed within all types of hedge funds. Chapter 2 proposes a new decomposition of the risk associated with trend-following strategies into a common component and a specific component. The extraction of a systematic risk factor and its addition to the standard factor models allows us to better explain the performance of hedge fund styles in a different way than in chapter 1. Finally, chapter 3 addresses the issue of the execution of a trend following strategy. The cost paid by the investor, i.e. the cost associated with managing the portfolio, is not only a function of the individual liquidity of the assets handled but also depends on the allocation decisions made by the manager to meet the fund's performance and risk objectives.

This thesis focuses on the new opportunities offered by the growth of Exchange-Traded Funds (ETFs). This research explores three of their impacts on collective bond management.The first step of this study observes the effects on underlyings of inclusion or exclusion in an ETF. Because of their hybrid structure, ETFs can affect the characteristics of underlyings and increase the proportion of uninformed investors. I exploit bond downgrades to establish a relationship between ETF ownership and the liquidity or price of the underlyings. The results show that the effects of ETF inclusion are long-lasting and partially persist after ETF exit. The next chapter proposes a methodology using the information contained in ETFs as a stress measure. Many investors take advantage of the speed of trading of ETFs to adjust their exposures very quickly. The proposed indicator provides a single methodology for all asset classes, which facilitates the analysis of its propagation. Finally, the last chapter deals with the integration of portfolio insurance strategies in an open-ended fund. The flexibility of ETFs across multiple asset classes creates new opportunities for portfolio insurance. This chapter proposes a model that tailors exposure to a risky asset based on downside and upside protections. The methodology allows for protection of all investors regardless of the subscription/redemption period. regardless of the subscription/redemption period.

This paper investigates empirically whether time-series momentum returns can explain the performance of hedge funds in the cross section. Relying on the trend-following literature, a volatility-adjusted time-series momentum signal is applied on a daily basis across a large set of futures, covering the major asset classes. We build a hierarchical set of trend factors: the full version TREND can be split in summable factors across two dimensions: the horizon of the signals and the traded asset class. We show that Managed Futures, Global Macro and Fund of Hedge Funds strategies can be partly explained by a TREND exposure. Moreover, a TREND exposure is a significant determinant of hedge funds returns at the fund level, for Managed Futures and Global Macro but also, and more surprisingly, for the other styles.

We propose a new family of bivariate nonnegative integer-autoregressive (BINAR) models for count process data. We first generalize the existing BINAR(1) model by allowing for dependent thinning operators and arbitrary innovation distribution. The extended family allows for intuitive interpretation, as well as tractable aggregation and stationarity properties. We then introduce higher order BINAR(p) and BINAR(∞) dynamics to accommodate more flexible serial dependence patterns. So far, the literature has regarded such models as computationally intractable. We show that the extended BINAR family allows for closed-form predictive distributions at any horizons and for any values of , which significantly facilitates non-linear forecasting and likelihood based estimation. Finally, a BINAR model with memory persistence is applied to open-ended mutual fund purchase and redemption order counts.

Hedge funds have experienced rapid growth in their assets under management. However, their poor performance during the 2008 financial crisis and in recent years has called into question the absolute nature of their returns. The first chapter demonstrates a positive link between the degree of self-correlation in hedge fund returns and their overperformance. Consistent with the assumption of bias in the estimators, the most auto-correlated funds also have the lowest measured risk factor exposures. Chapter two shows that the auto-correlation in hedge fund returns is only partially due to liquidity problems, and thus that smoothing of returns by fund managers is very prevalent.Finally, chapter three highlights that capital risk on financial intermediaries is a new risk factor that strongly explains the cross-section of hedge fund returns. Part of the alpha comes in fact from a risk premium for exposure to this factor.

Recent years have witnessed a lack of dynamism in the stock markets, which has led a growing number of investors to turn to private markets, particularly private equity. This manuscript addresses issues that characterize the changes in private equity today: a search for relative liquidity, a quest for higher returns in new markets, and the stability of human resources, which remain an important vector of communication when raising funds from investors.The first essay of this thesis examines the impact of the IPO of private equity vehicles on their performance and shows that this search for liquidity leads to a significant decrease in realized performance. The second essay discusses the performance of private equity in emerging markets and shows that the success of these investments is related to the geographical distance and cultural proximity between private equity firms and the companies financed.Finally, the last essay of this thesis examines the importance of human capital for private equity firms and shows that managerial mobility deteriorates realized performance.

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This paper proposes a new model with time-varying slope coefficients. Our model, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition of the conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinal data. We derive stationarity and invertibility conditions and prove consistency and asymptotic normality of the Full and equation-by-equation QML estimators of this model. We then show that this class of models is useful to estimate conditional betas and compare it to the approach proposed by Engle (2016). Finally, we use real data in a portfolio and risk management exercise. We find that the CHAR model outperforms a model with constant betas as well as the dynamic conditional beta model of Engle (2016).

This paper proposes a new model with time-varying slope coefficients. Our model, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition of the conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinal data. We derive stationarity and invertibility conditions and prove consistency and asymptotic normality of the Full and equation-by-equation QML estimators of this model. We then show that this class of models is useful to estimate conditional betas and compare it to the approach proposed by Engle (2016). Finally, we use real data in a portfolio and risk management exercise. We find that the CHAR model outperforms a model with constant betas as well as the dynamic conditional beta model of Engle (2016).

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The mixture of distribution hypothesis (MDH) model offers an appealing explanation for the positive relation between trading volume and volatility of returns. In this specification, the information flows constitute the only mixing variable responsible for all changes. However, this single static latent mixing variable cannot account for the observed short-run dynamics of volume and volatility. In this paper, we propose a dynamic extension of the MDH that specifies the impact of information arrival on market characteristics in the context of liquidity frictions. We distinguish between short-term and long-term liquidity frictions. Our results highlight the economic value and statistical accuracy of our specification. First, based on some goodness of fit tests, we show that our dynamic two-latent factor model outperforms all competing specifications. Second, the information flows latent variable can be used to propose a new momentum strategy. We show that this signal improves once we allow for a second signal – the liquidity frictions latent variable – as the momentum strategies based on our model present better performance than the strategies based on competing models.

While SWFs are not new, their number and financial power have grown steadily since the early 2000s, raising many concerns, particularly in developed countries. Are SWFs driven by the same motivations as institutional investors? Does their financial power risk destabilizing markets? These questions have made SWFs a research topic in its own right, and this research paper is part of it. The first essay of this thesis contributes to identify the main factors that may lead a country to create a SWF. Furthermore, the investment strategies of SWFs raise many questions: do they pursue a financial return objective or do they have more strategic objectives? The second essay highlights the complexity of SWFs' decision-making process by testing whether they prefer to invest in countries they are familiar with and/or in countries they have invested in before. Following this analysis, the third essay focuses more specifically on the determinants of SWFs' majority shareholdings by focusing on a particularly active group of funds: the funds of the Gulf States. More precisely, this analysis aims to identify the factors that influence the decision to take control in a given company.

ETFs and index funds have grown at very rapid rates in recent years. Originally launched totrack some large liquid indices in developed markets, they now also concern less liquid assetclasses such as emerging market bonds. Illiquidity certainly affects the quality of the replication,and in particular, liquidity might increase the tracking error of any index fund, i.e., thedifference between the fund and the benchmark returns. The tracking error is then the firstcharacteristic that investors consider when they select index funds. In this paper, we beginfrom the CDS-bond basis to simulate the tracking error (TE) of a hypothetical well-diversifiedfund investing in the emerging market bond universe. We compute the CDS-bond basis andthe tracking error for 9 emerging market sovereign entities: Brazil, Chile, Hungary, Mexico,Poland, Russia, South Africa, Thailand and Turkey. All of these countries are included inthe MSCI Emerging Market Debt in Local Currency index. Our sample period ranges fromJanuary 1, 2007 to March 26, 2012. Using a Regime Switching for Dynamic Correlations(RSDC) model, we show that the country-by-country tracking error is reduced by the diversificationat the fund level. Moreover, we show that this diversification effect is less effectiveduring crisis periods. This loss of diversification benefits is the main risk of index funds when they are designed to create a liquid exposure to illiquid asset classes.

Until recently the liquidity of financial assets has typically beenviewed as a second-order consideration. Liquidity was frequently associatedwith simple transaction costs that impose - temporary if any- effect on assetprices, and whose shocks could be easily diversified away. Yet the evidenceespeciallythe recent liquidity crisis- suggests that liquidity is now a primaryconcern. This paper aims at disentangling market risk and liquidity riskin the context of conditional volatility models. Our approach allows theisolation of the intrisic liquidity of any asset, and thus makes it possible todeduce a liquidity risk even when volumes are not observed.

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This thesis explores theoretically and empirically the implications of the joint stock/option dynamics on various issues related to options trading. First, we study the joint dynamics between an option on a stock and an option on the market index. The CAPM model provides an adequate mathematical framework for this study because it allows to model the joint dynamics of a stock and its market index. Moving to option prices, we show that beta and idiosyncratic volatility, parameters of the model, allow us to characterize the relationship between the implied volatility surfaces of the stock and the index. We then turn to the estimation of the beta parameter under the risk-neutral probability using option prices. This measure, called implied beta, represents the information contained in the option prices about the realization of the beta parameter in the future.For this reason, we try to see, if implied beta has any predictive power of the future beta.By conducting an empirical study, we conclude that implied beta does not improve the predictive ability compared to the historical beta which is computed through the linear regression of the stock returns on the index returns. Better yet, we note that the oscillation of the implied beta around the future beta can lead to arbitrage opportunities, and we propose an arbitrage strategy that allows to monetize this gap. On the other hand, we show that the implied beta estimator could be used to hedge options on the stock using index instruments, this hedging concerns notably the volatility risk and also the delta risk. In the second part of our work, we are interested in the problem of market making on options. In this study, we assume that the model of the underlying's dynamics under the risk-neutral probability could be misspecified, which reflects a mismatch between the implied distribution of the underlying and its historical distribution.First, we consider the case of a risk-neutral market maker who aims to maximize the expectation of his future wealth. Using a stochastic optimal control approach, we determine the optimal call and put prices on the option and interpret the effect of price inefficiency on the optimal strategy. In a second step, we consider that the market maker is risk averse and therefore tries to reduce the uncertainty associated with his inventory. By solving an optimization problem based on a mean-variance criterion, we obtain analytical approximations of the optimal buying and selling prices. We also show the effects of inventory and price inefficiency on the optimal strategy. We then turn to the market making of options in a higher dimension. Thus, following the same reasoning, we present a framework for the market making of two options with different underlyings with the constraint of variance reduction related to the inventory risk held by the market maker. In the last part of our work, we study the joint dynamics between the implied volatility at the currency and the underlying, and we try to establish the link between these joint dynamics and the implied skew. We are interested in an indicator called "Skew Stickiness Ratio" which has been introduced in the recent literature. This indicator measures the sensitivity of the implied volatility of the currency to the movements of the underlying. We propose a method that allows us to estimate the value of this indicator under the risk-neutral probability without the need to admit assumptions on the dynamics of the underlying. [.].

This thesis presents several extensions to positive affine interest rate models. A first chapter introduces the concepts related to the models used in the following chapters. It details the definition of so-called affine processes, and the construction of asset price models obtained by non-arbitrage. Chapter 2 proposes a new estimation and filtering method for linear-quadratic state-space models. The next chapter applies this estimation method to the modeling of interbank spreads in the Eurozone, in order to decompose the fluctuations related to default and liquidity risk. Chapter 4 develops a new technique to create multivariate affine processes from their univariate counterparts, without imposing conditional independence between their components. The last chapter applies this method and derives a multivariate affine process in which some components can remain at zero for extended periods. Incorporated into an interest rate model, this process can efficiently account for zero-bottom rates.

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In a new environment where liquidity providers as well as liquidity consumers act strategically, understanding how liquidity flows and dries-up is key. We propose a model that specifies the impact of information arrival on market characteristics, in the context of liquidity frictions. We distinguish short-lasting liquidity frictions, which impact intraday prices, from long-lasting liquidity frictions, when information is not fully incorporated into prices within the day. We link the first frictions to the strategic behavior of intraday liquidity providers and the second to the strategic behavior of liquidity consumers, i.e. long-term investors who split up their orders not to be detected. Our results show that amongst 61% of the stocks facing liquidity problems, 57% of them point up liquidity providers as the sole strategic market investor. Another 27% feature long-term investors as the single strategic player, while both liquidity providers and liquidity consumers act strategically in the remaining 16%. This means that 43% of these stocks are actually facing a slow-down in the information propagation in prices, which thus results in a significant decrease of (daily) price efficiency due to long-term investors’ strategic behavior.

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Based on the concept that the presence of liquidity frictions can increase the daily traded volume, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Tauchen and Pitts (1983) to measure the liquidity portion of volume. Our approach relies on a structural definition of liquidity frictions arising from the theoretical framework of Grossman and Miller (1988), which explains how liquidity shocks affect the way in which information is incorporated into daily trading characteristics. In addition, we propose an econometric setup exploiting the volatility–volume relationship to filter the liquidity portion of volume and infer the presence of liquidity frictions using daily data. Finally, based on FTSE 100 stocks, we show that the extended MDH model proposed here outperforms that of Andersen (1996) and that the liquidity frictions are priced in the cross-section of stock returns.

The objective of this thesis is to evaluate and compare the performance of portfolio insurance strategies in an attempt to define which strategies should be preferred by investors. We compare a number of insurance strategies (OBPI, CPPI, synthetic put and stop-loss) with each other and with a few other benchmark strategies. We use different criteria for comparison which include: 1. pay-off distributions, level of protection, stochastic dominance and cost of insurance under different market conditions identified by Markovian regime-switching models. 2. Risk-adjusted performance measures that can reflect investors' preferences for risk and return. 3. Investor preferences by incorporating cumulative prospect theory (CPT). Our results seem to show a dominance of CPPI strategies in the majority of cases and for the majority of the comparison criteria.

A characteristic of hedge funds is not only an active portfolio management, but also the allocation of portfolio performance between different accounts, which are the accounts for the external investors and an account for the management firm, respectively. Despite lack of transparency in hedge fund market, the strategy of performance allocation is publicly available. This paper shows that, for the High-Water Mark Scheme, these complex performance allocation strategies might explain empirical facts observed in hedge fund returns, such as return persistence, skewed return distribution, bias ratio, or implied increasing risk appetite.

Much research into financial contagion and systematic risks has been motivated by the finding that cross-market correlations (resp. coexceedances) between asset returns increase significantly during crisis periods. Is this increase due to an exogenous shock common to all markets (interdependence) or due to certain types of transmission of shocks between markets (contagion)?Darolles and Gourieroux explain that an attempt to convey contagion and causality in a static framework can be flawed due to identification problems. they provide a more precise definition of the notion of shock to strengthen the solution within a dynamic framework.This book covers the standard practice for defining shocks in SVAR models, impulse response functions, identitification issues, static and dynamic models, leading to the challenges of measurement of systematic risk and contagion, with interpretations of hedge fund survival and market liquidity risks.

Fund managers face liquidity problems but they have to distinguish the market liquidity risk implied by their assets and the funding liquidity risk. This latter is due to both the liquidity mismatch between assets and liabilities and the redemption risk due to the possible outflows from clients. The main contribution of this paper is the analysis of contagion looking at common market liquidity problems to detect funding liquidity problems. Using the CDS Bond Spread basis as a liquidity indicator and a state space model with time-varying volatility specification, we show that during the 2007-2008 financial crisis, there exist pure contagion effects both in terms of price and liquidity on the emerging sovereign debt market. This result has strong implication since the main risk for an asset manager is to get stuck with an unwanted position due to a dry-up of market liquidity.

A characteristic of hedge funds is not only an active portfolio management, but also the allocation of portfolio performance between different accounts, which are the accounts for the external investors and an account for the management firm, respectively. Despite a lack of transparency in hedge fund market, the strategy of performance allocation is publicly available. This paper shows that, for the High WaterMark Scheme, these complex performance allocation strategiesmight explain empirical facts observed in hedge fund returns, such as return persistence, skewed return distribution, bias ratio, or implied increasing risk appetite.

The ratings of fund managers based on past performances of the funds and the rating dynamics are crucial information for investors. This paper proposes a stochastic migration model to investigate the dynamics of performance-based ratings of funds, for a given risk-adjusted measure of performance. We distinguish the absolute and relative ratings and explain how to identify their idiosyncratic and systematically persistent (resp. amplifying cycles) components. The methodology is illustrated by the analysis of hedge fund returns extracted from the TASS database for the period 1994–2008.

Decomposing returns into market and stock specific components is common practice and forms the basis of popular asset pricing models. What about volume? Can volume be decomposed in the same way as returns? Lo and Wang (2000) suggest such a decomposition. Our paper contributes to this literature in two different ways. First, we provide a model to explain why volumes deviate from the benchmark. Our interpretation is in terms of arbitrage strategies and liquidity. Second, we propose a new efficient screening tool that allows practitioners to extract specific information from volume time series. We provide an empirical illustration of the relevance and the possible uses of our approach on daily data from the FTSE index from 2000 to 2002.

This paper analyses how an external adverse shock will impact the financial situations of banks and insurance companies and how it will diffuse among these companies. In particular we explain how to disentangle the direct and indirect (contagion) effects of such a shock, how to exhibit the contagion network and how to detect the ”superspreaders”, i.e. the most important firms involved in the contagion process. This method is applied to a network of 8 large European banks in order to analyze whether the revealed interconnections within these banks differ depending on the underlying measure of banks’ financial positions, namely their market capitalization, the price of the CDS contract written on their default and their book value.

Decomposing returns into market and stock specific components is common practice and forms the basis of popular asset pricing models. What about volume? Can volume be decomposed in the same way as returns? Lo and Wang (2000) suggest such a decomposition. Our paper contributes to this literature in two different ways. First, we provide a model to explain why volumes deviate from the benchmark. Our interpretation is in terms of arbitrage strategies and liquidity. Second, we propose a new efficient screening tool that allows practitioners to extract specific information from volume time series. We provide an empirical illustration of the relevance and the possible uses of our approach on daily data from the FTSE index from 2000 to 2002.

This paper analyses how an external adverse shock will impact the financial situations of banks and insurance companies and how it will diffuse among these companies. In particular we explain how to disentangle the direct and indirect (contagion) effects of such a shock, how to exhibit the contagion network and how to detect the ”superspreaders”, i.e. the most important firms involved in the contagion process. This method is applied to a network of 8 large European banks in order to analyze whether the revealed interconnections within these banks differ depending on the underlying measure of banks’ financial positions, namely their market capitalization, the price of the CDS contract written on their default and their book value.

In addition to active portfolio management, hedge funds are characterized by the allocation of portfolio performance between the external investors and the management firm accounts. This allocation can take different forms, such as the Loss Carry Forward scheme, and some of them can be coupled with performance smoothing techniques. This paper shows that this additional smoothing component might explain some empirical facts observed on the distribution and the dynamics of hedge fund returns.

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Decomposing returns into market and stock specific components is common practice and forms the basis of popular asset pricing models. What about volume? Can volume be decomposed in the same way as returns? Lo and Wang (2000) suggest such a decomposition. Our paper contributes to this literature in two different ways. First, we provide a model to explain why volumes deviate from the benchmark. Our interpretation is in terms of arbitrage strategies and liquidity. Second, we propose a new efficient screening tool that allows practitioners to extract specific information from volume time series. We provide an empirical illustration of the relevance and the possible uses of our approach on daily data from the FTSE index from 2000 to 2002.

The last crisis sheds light on the importance to consider liquidity risk in the financial industry. Indeed, liquidity had a predominant role in propagating the turmoil. In contrast, controlling for liquidity is a difficult task. The definition of liquidity links different dimensions that are impossible to fully capture together. As a consequence, there exist a lot of liquidity measures and we find in the literature some solutions to take into account more than one dimension of liquidity but also liquidity measures considering a long lasting liquidity problem. In this paper, we focus on drastic illiquidity events, i.e liquidity problems reported by several liquidity measures simultaneously. We propose a Meta-Measure of liquidity called MLiq and defined as the probability to be in a state of high liquidity risk. We use a multivariate model allowing to measure correlations between liquidity measures jointly with a state-space model that endogenously defines the illiquid periods.

With recent outbreaks of multiple large-scale financial crises, amplified by interconnected risk sources, a new paradigm of fund management has emerged. This new paradigm leverages "embedded" quantitative processes and methods to provide more transparent, adaptive, reliable and easily implemented "risk assessment-based" practices. This book surveys the most widely used factor models employed within the field of financial asset pricing. Through the concrete application of evaluating risks in the hedge fund industry, the authors demonstrate that signal processing techniques are an interesting alternative to the selection of factors (both fundamentals and statistical factors) and can provide more efficient estimation procedures, based on lq regularized Kalman filtering for instance. With numerous illustrative examples from stock markets, this book meets the needs of both finance practitioners and graduate students in science, econometrics and finance.

This chapter presents a new family of algorithms named regularized Kalman Filters (rgKFs) that have been derived to detect and estimate exogenous outliers that might occur in the observation equation of a standard Kalman filter (KF). Inspired from the robust Kalman filter (RKF) of Mattingley and Boyd, which makes use of a l1-regularization step, the authors introduce a simple but efficient detection step in the recursive equations of the RKF. This solution is one means by which to solve the problem of adapting the value of the l1-regularization parameter: when an outlier is detected in the innovation term of the KF, the value of the regularization parameter is set to a value that will let the l1-based optimization problem estimate the amplitude of the spike. The chapter deals with the application of algorithm to detect irregularities in hedge fund returns.

This chapter introduces, illustrates and derives both least squares estimation (LSE) and Kalman filter (KF) estimation of the alpha and betas of a return, for a given number of factors that have already been selected. It formalizes the “per return factor model” and the concept of recursive estimate of the alpha and betas. The chapter explains the setup, objective, criterion, interpretation, and derivations of LSE. The setup, main properties, objective, interpretation, practice, and geometrical derivation of KF are also discussed. The chapter also explains the working of LSE and KF. Numerous simulation results are displayed and commented throughout the chapter to illustrate the behaviors, performance and limitations of LSE and KF.

This chapter focuses on the empirical ad hoc approach and presents three reference models that are widely used in the literature. These models are all based on the factor representation, but highlight the nature of the factors to be used to explain specific asset class returns. In a section, the authors denote by eigenfactors the factors obtained from the observations using the eigenvector decomposition of the covariance matrix of the returns. The chapter describes some classical techniques, arising from the information theory. It provides complementary sections which provide some light on related problems to this approach such as the estimation of the covariance matrix of the data, the similarity of the approach with subspace methods and the extension of this approach to large panel data.

A tidal wave of regulation is hitting financial markets worldwide as a result of the credit crisis of 2008–2009 and this time around the hedge fund world will not be immune. We argue in this chapter that, unlike conventional wisdom, regulation could be an opportunity for the funds of hedge funds (FoHFs) industry. The only necessary condition is fair treatment of hedge fund investments. We take the Solvency II framework as an example and show how the implementation of the granularity adjustment, first introduced for implementation in the Basel framework, makes it possible to take into account the diversification potential of FoHFs and in turn reconcile the outcome of the standard formula with empirical evidence.

Financial markets are today so interconnected that they are fragile to contagion. Massive investment funds with very short horizons in -and out- flows can generate contagion effects between markets. Since 2010, investors are willing to get a liquid exposure to the EMsovereign debt. As a consequence, some asset management firms started to propose products to track the performance of this asset class. However in that case, the fund manager faces a mismatch of liquidity between assets and liabilities and needs some tools to manage the liquidity of his investments. The main contribution of this paper is the analysis of contagion looking at common market liquidity problems to detect funding liquidity problems. Using the CDS Bond Spread basis as a liquidity indicator and a state space model with time-varying volatility specification, we show that during the 2007-2008 financial crisis, there exist pure contagion effects both in terms of price and liquidity on the emergings overeign debt market.This result has strong implication since the main risk for an asset manager is to get stuck with an unwanted position due to a dry-up of market liquidity.

Emerging economies have passed an important stress test during the period 2008-09 and are now the key drivers for global growth of the world economy. Financial markets are today so interconnected that they are fragile to contagion. The issue of financial contagion was historically concerning Emerging Markets (EM). These latter attract foreign investors and massive investments funds in -and out- flows on very short horizons can be a source of contagion effects between markets. The analysis of the sovereign debt markets and particularly related CDS markets is of interest since it is at the very center of a new phenomenon: banks are not anymore the main source of systemic risk but sovereign economies are. As foreign investors represent the most of the volume traded, capital flows in these markets should also impact FX market. Their analysis is thus also central to this study. Indeed, the main risk for an asset manager is to get stuck with unwanted sovereign debt due to a dry up of market liquidity. The main contribution of this paper is the analysis of contagion looking at common markets liquidity problems to detect funding liquidity problems. We use the Credit Default Swap bond spread basis and the deviations from the Covered Interest Parity as liquidity measures respectively for sovereign debt and FX markets. Moreover, we distinguish interdependence and pure contagion using a state-space model with a time-varying volatility specification and we apply it to both returns and liquidity indicators.

In this paper we examine the dependence between the liquidation risks of individual hedge funds. This dependence can result either from common exogenous shocks (shared frailty), or from contagion phenomena, which occur when an endogenous behaviour of a fund manager impacts the Net Asset Values of other funds. We introduce dynamic models able to distinguish between frailty and contagion phenomena, and test for the presence of such dependence effects, according to the age and management style of the fund. We demonstrate the empirical relevance of our approach by measuring the magnitudes of contagion and exogenous frailty in liquidation risk dependence in the TASS database. The empirical analysis is completed by stress-tests on portfolios of hedge funds.

This chapter introduces the common version of linear factor models and also discusses its limits and developments. It introduces different notations and discusses the model and its structure. The chapter lists out the reasons why factor models are generally used in finance, and further explains the limits of this approach. It also deals with the different steps in the building of factor models, i.e. factor selection and parameter estimation. Finally, the chapter gives a historical perspective on the use of factor models such as capital asset pricing model (CAPM), Sharpe's market model and arbitrage pricing theory (APT) in finance.

This thesis consists of five chapters, and focuses on the statistical analysis of the spectral decomposition of the conditional expectation operator. The first chapter introduces the notion of Markov processes, then their construction from the semigroup of conditional expectation operators and the associated infinitesimal generator. In the second chapter, we consider the problem of the nonparametric estimation of the trend and volatility functions of a scalar diffusion process, from discrete time observed data. We introduce the notion of a truncated process and study its dynamic properties. The functions belonging to the generator domain of the truncated process verify explicit constraints that can then be used in the estimation phase. The third chapter deals with the extension of the spectral methods used in the framework of scalar diffusions to the case of non-reversible processes. This extension is a generalization of the usual linear canonical analysis. We consider a nonlinear canonical analysis based on a kernel estimator of the density, which allows us to obtain the convergence and the asymptotic normality of the estimators of the canonical correlations and of the canonical variables. The fourth chapter focuses on the intraday dynamics of transaction prices in financial markets operating with continuous matching. Transaction prices have two major characteristics: they are discrete in level and exist only at random trading dates. We propose a model that takes into account these two characteristics and then compare it to a structural model in which there is an underlying asset value in continuous time. The fifth chapter proposes an application of the spectral decomposition of the conditional expectation operator to the approach of the price of a derivative asset.