LE FOL Gaelle

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.

The year 2018 started and ended with significant jumps in volatility.Is this return of volatility an epiphenomenon, a regime change or a return to "normal"? Is this market situation a harbinger of the next crisis? What is the impact of economic fundamentals and political decisions on volatility? Finally, what are the consequences for investors?

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 studies the behavior of investors in open-end mutual funds and its implications for liquidity risk. The objective of this research is to help fund managers avoid the "fund run" scenario where they suddenly lose their clients. The first step of this study is to collect a new database that records investors' "micro-transactions". This allows us to analyze their behavior at the individual level and to conduct three research papers on this topic. In the first paper, we develop a self-exciting counting model that captures stylized facts of the fund flow series. From this, we show a fund liability risk that is different from the asset risk already documented in the previous literature. We also identify a contagion of liquidity shocks across different clients in the same fund. In the next chapter, we study the investment horizons of individual clients. These horizons are strongly related to investor characteristics and economic conditions. We also show that fund managers face a premature exit risk related to the shortening of their clients' investment horizons. We then observe heterogeneity among investors: long-term investors behave differently than short-term investors. Finally, in the last chapter, we focus on rebalancing activities. We find that many investors hold a portfolio containing several funds and rebalance it to keep the same asset allocation.

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.

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After an overview of the Big Data market in finance and insurance, the speakers discussed the various impacts of Big Data on the organization of companies, but also on the future jobs of the students present, particularly the future of traders. The various experiences of the speakers allowed to bring to the round table very concrete examples of transformation, but also a look at the European regulation.

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.

This thesis attempts to qualitatively and quantitatively analyze the sometimes destabilizing impacts of excess global liquidity on emerging market asset prices. This excess of global liquidity has notably materialized in the form of a boom in portfolio investments in emerging markets, the study of which has become a central theme for both policy makers and the asset management industry. In this respect, we propose to circumvent the weaknesses of the Balance of Payments data by constructing a non-delayed, high-frequency indicator of portfolio flows, using EPFR data. The yield-seeking dynamics induced by the implementation of unconventional monetary policies by the main central banks of the developed markets has led to a strong inflation of asset prices, first and foremost in the emerging equity markets, where potential bubbles could appear in the so-called "New Normal" period.

The liquidity of financial markets has become a major concern that has long been ignored by both classical financial theory and practice. Thus, investing in the markets meant managing the famous risk/return trade-off.

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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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.

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.

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.

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.

No summary available.

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 thesis is articulated around three financial risks: liquidity, contagion and systemic risk. The latter have been the focus of attention since the 2007-08 crisis and will continue to be relevant in light of the events that the financial markets are experiencing. The first chapter of this thesis presents a funding liquidity factor obtained by interpreting a contagion phenomenon in terms of market liquidity risk. In the second chapter, we propose a meta-measurement of this market liquidity. The latter takes into account all the dimensions present in the definition of liquidity by looking at the dynamics of several liquidity measures simultaneously. The objective of the third chapter is to present a model of market returns that allows for the inclusion of funding liquidity in the estimation of the DCoVaR. Thus, this work proposes a new measure of systemic risk with countercyclical behavior. Finally, we focus on the non-linearity assumption of the dependence structure between market returns and financial institutions' returns. At the heart of systemic risk measurement, this assumption appears to be restrictive since it has little impact on the identification of the riskiest firms but can considerably complicate the estimation of these measures.

This thesis concerns the modeling of the dynamics of international yield curves with the consideration of several dependency channels. Using a new database of international sovereign rates, we observe that the criterion of explained variability, proposed in the literature, is not able to select a better combination of factors describing the joint dynamics of the yield curves. We propose a new method of factor sectioning based on the likelihood maximization of a linear Gaussian state-space model with common and local factors. The associated identification problem is solved in a novel way. By estimating different combinations of countries, we select two global and three local factors with predictive power for the macroeconomic variables (economic activity and inflation rate) in each economy considered. Our method also allows us to detect hidden factors in bond yields. They are not visible through a classical principal component analysis of bond yields and they contribute to the prediction of the inflation rate and the growth rate of industrial production.

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.

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.

Given the empirical failure of observable risk factors in explaining financial returns, the aim of this thesis is to use latent factor models and recent econometric developments to improve the understanding of asset risk. First, we describe latent factor models applied to finance and the main estimation methods. We also present how the use of financial and econometric theories allows us to link statistical factors with economic and financial variables in order to facilitate their interpretation. Second, we use latent factor models in cross-sections to estimate and interpret the risk profile of hedge funds and stocks. The methodology is consistent with the statistical properties of large samples and the dynamic nature of systematic risk. In a third step, we model a market where prices and volumes are sensitive to intraday liquidity shocks. We propose a structural mixture model of latent two-factor distributions to capture the impact of information shocks and liquidity frictions. This model allows us to construct a static liquidity measure specific to each stock. Second, we extend our structural model to account for the dynamic properties of liquidity risk. In particular, we distinguish two liquidity problems: liquidity frictions occurring at an intraday frequency and illiquidity events that persistently deteriorate market quality. Finally, we use statistical time series modeling to construct dynamic liquidity measures.

The end of the 19th century was marked by important changes in the financial markets through their development, liberalization and finally concentration in a cross-border dynamic. A salient fact of these changes is the progressive acceleration of asset price movements, faith between market segments, but also between countries. If the opening of capital markets favors an optimal allocation of resources and risks, this same opening also raises a systemic risk, on a global scale, due to the intensification of these movements observed in times of crisis. The first objective of this thesis is to use several innovative techniques of financial econometrics (in particular multiffractal models) that allow to take into account both the long term dynamics between different assets (linked for instance to the advent of the Euro zone) without neglecting the very short term dynamics. A second objective is to highlight the contribution for policy makers of the use of high frequency data. We show that the use of such data sheds new light on the evaluation of the European Central Bank's operational framework and its interaction with the financial markets.

Market microstructure is the study of the behavior of market participants and the characteristics of the resulting exchange under specific and explicit transaction rules. This is why any microstructure study requires a perfect knowledge of the market studied. The aim of this thesis is first to describe and analyze the French equity market. Then we compare, using usual criteria, the particular functioning of this market to other trading systems and propose alternative systems that would allow to correct the possible problems they may present. Based on high frequency data from the Paris Stock Exchange, we propose statistical analysis tools and new measures of market performance by studying the dynamics of the order book at regular time intervals and by using duration and time formation model approaches applied to transactions. Then, on the basis of the results obtained, we propose a model that is compatible with its functioning and, more particularly, that takes into account its main characteristic: it is a market that operates continuously and on which orders are stored in a book before being matched.