This thesis investigates the application of principal component analysis to the Australian stock market using ASX200 index and its constituents from April 2000 to February 2014. The first ten principal components were retained to present the major risk sources in the stock market. We constructed portfolio based on each of the ten principal components and named these “principal portfolios

We propose a statistical model for weighted temporal networks capable of measuring the level of heterogeneity in a financial system. Our model focuses on the level of diversification of financial institutions; that is, whether they are more inclined to distribute their assets equally among partners, or if they rather concentrate their commitment towards a limited number of institutions. Crucially, a Markov property is introduced to capture time dependencies and to make our measures comparable across time. We apply the model on an original dataset of Austrian interbank exposures. The temporal span encompasses the onset and development of the financial crisis in 2008 as well as the beginnings of European sovereign debt crisis in 2011. Our analysis highlights an overall increasing trend for network homogeneity, whereby core banks have a tendency to distribute their market exposures more equally across their partners.

Submitted by Jamil Civitarese (jamil@rankings.watch) on 2015-09-08T17:16:54Z No. of bitstreams: 1 ebape_v2_completa.pdf: 1545221 bytes, checksum: 26ed0880a075cf3930258d1d3b4b769f (MD5) / Approved for entry into archive by ÁUREA CORRÊA DA FONSECA CORRÊA DA FONSECA (aurea.fonseca@fgv.br) on 2016-01-25T14:30:06Z (GMT) No. of bitstreams: 1 ebape_v2_completa.pdf: 1545221 bytes, checksum: 26ed0880a075cf3930258d1d3b4b769f (MD5) / Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2016-01-26T19:19:59Z (GMT) No. of bitstreams: 1 ebape_v2_completa.pdf: 1545221 bytes, checksum: 26ed0880a075cf3930258d1d3b4b769f (MD5) / Made available in DSpace on 2016-01-26T19:20:11Z (GMT). No. of bitstreams: 1 ebape_v2_completa.pdf: 1545221 bytes, checksum: 26ed0880a075cf3930258d1d3b4b769f (MD5) Previous issue date: 2015-08-08 / This dissertation presents two papers on how to deal with simple systemic risk measures to assess portfolio risk characteristics. The first paper deals with the Granger-causation of systemic risk indicators based in correlation matrices in stock returns. Special focus is devoted to the Eigenvalue Entropy as some previous literature indicated strong re- sults, but not considering different macroeconomic scenarios; the Index Cohesion Force and the Absorption Ratio are also considered. Considering the S&P500, there is not ev- idence of Granger-causation from Eigenvalue Entropies and the Index Cohesion Force. The Absorption Ratio Granger-caused both the S&P500 and the VIX index, being the only simple measure that passed this test. The second paper develops this measure to capture the regimes underlying the American stock market. New indicators are built using filtering and random matrix theory. The returns of the S&P500 is modelled as a mixture of normal distributions. The activation of each normal distribution is governed by a Markov chain with the transition probabilities being a function of the indicators. The model shows that using a Herfindahl-Hirschman Index of the normalized eigenval- ues exhibits best fit to the returns from 1998-2013.

nÃo hà / Este estudo teve como objetivo investigar a existÃncia de uma quebra estrutural na relaÃÃo entre o setor bancÃrio e o IBOVESPA durante o perÃodo de 1 de janeiro de 2007 a 29 de julho de 2011, em consequÃncia da crise financeira ocorrida em 2008. Foram empregadas tÃcnicas tradicionais em FinanÃas e Econometria para analisar os impactos da referida crise sobre o setor bancÃrio brasileiro, tomando por base as cotaÃÃes diÃrias de fechamento das aÃÃes dos principais bancos brasileiros: Banco do Brasil, Bradesco, Itaà e do IBOVESPA. Na metodologia utilizou-se o modelo de apreÃamento de ativos, CAPM, na mensuraÃÃo do risco sistÃmico. Observou-se que as evidÃncias estatÃsticas, obtidas com os testes de Chow e teste t para diferenÃa de mÃdias, indicam fundamentalmente, que foi possÃvel captar um efeito diferenciado durante a crise de 2008 entre os bancos privados e o banco pÃblico em relaÃÃo ao risco sistÃmico, alÃm de captar uma mudanÃa estrutural em 24 de outubro de 2008, mudanÃa essa detectada a partir do teste de Chow. / This research aimed to investigate the existence of a structural break in the relationship between the banking sector and IBOVESPA during the period of January 1st 2007 to July 29th 2011, in consequence of the financial crisis occurred in 2008. Traditional techniques were employed in Finance and Econometrics knowledge to analyze the impacts of this crisis on the Brazilian banking sector, based on the daily closing prices of the shares of major Brazilian banks, includes Banco do Brasil, Bradesco, Itaà and IBOVESPA. The methodology used was based on the model of asset pricing, CAPM, in the measurement of systemic risk. It was observed that the statistical evidence, gained with the Chow test and t test for averages differences, basically indicate that it was possible to capture a different effect during the 2008âs crisis between public bank and private banks in relation to systemic risk, and capture a structural change in October 24, 2008, a shift detected from the Chow test.

The recent financial crisis has brought to the fore the need to better understand systemic risk, that is, the risk of collapse of a large part of the financial system and its potential effects on the real economy. In this thesis, we argue that a proper assessment of systemic risk must include an analysis of the network of interdependencies that exists between the different financial institutions. In fact, today's level of financial interconnectedness between and among markets has proven to have ambiguous effects. On the one hand, a highly connected system allows to diversify risk at the micro level. On the other hand, too much interdependencies provide various paths for contagion to take place and propagate at the macro level. In what follows, we analyze financial markets as networks of interactions and dependencies between financial agents. Through this lens, we investigate three major aspects: (i) how the structure of financial networks can amplify or mitigate the propagation of financial distress, (ii) what are the implications for macro-prudential regulation and (iii) which patterns of interactions characterize real financial networks.We start out by delivering a stability analysis of a network model of interbank contagion that accounts for panics and bank runs. We identify the effects of market architecture, banks' capital ratios, market liquidity and shocks. Our results show that no single network architecture is always superior to others. In particular, highly concentrated networks can both be the most robust and the most fragile depending on other market characteristics, mainly, liquidity.We then move on to tackle issues related to the building of regulatory frameworks that adequately account for the effects of financial interdependencies. We propose a new methodology to compute individual and systemic probabilities of default and show that certain network characteristics give rise to uncertainty. More precisely, we find that network cycles are responsible for the emergence of multiple equilibria even in the presence of complete knowledge. In turn, multiple equilibria give rise to uncertainty for the regulator in the determination of default probabilities. We also quantify the effects of network structures, leverage, volatility and correlations.Having introduced a way to overcome multiplicity, we deliver a method that quantifies the price of complexity in financial markets based on the above mentioned model. This method consists of determining the scope of possible levels of systemic risk that can be obtained when some parameters are subject to small deviations from their true value. The results show a price to the interconnected nature of credit markets even when the equilibrium is unique: small errors can lead to large mistakes in measuring the likelihood of systemic default. Extending the model to account for derivative contracts, we show that error effects increase dramatically as more types of contracts are present in the system. While there is an intuition for such phenomenon, our framework formalizes the idea and quantifies its determinants.In the last part of this thesis, we contribute to the quantitative analysis of real financial networks. We start with a temporal network analysis of one of the major national interbank markets, that is, the German interbank market. We report on the structural evolution of two of the most important over-the-counter markets for liquidity: the interbank market for credit and for derivatives. We find that the majority of interactions is concentrated onto a set of few market participants. There also exists an important correlation between the borrowing and lending activities for each bank in terms of numbers of counterparties. In contrast with other works, we find little impact of the 2008 crisis on the structure of the credit market. The derivative market however exhibits a peak of concentration in the run up to the crisis. Globally, both markets exhibit large levels of stability for most of the network metrics and high correlation amongst them.Finally, we analyze how banks interact with the real economy by investigating the network of loans from banks to industries in Japan. We find evidence of a particular structure of interactions resulting from the coexistence of specific strategies both on the lending side and the borrowing side: generalist agents and specialist agents. Generalist banks have a diversified portfolio (i.e. they provide liquidity to almost all industries) while specialist banks focus their activity on a narrow set of industries. Similarly, generalists industries obtain credit from all banks while specialist industries have a restricted number of creditors. Moreover, the arrangement of interactions is such that specialists tend to only interact with generalists from the other side. Our model allows to structurally characterize highly persistent, and economically meaningful, sets of generalists and specialists. We further provide an analysis of the factors that predict whether a given bank or industry is a generalist. We show that size is an important determinant, both for banks and industries, but we also highlight additional relevant factors. Finally, we find that generalist banks tend to be less vulnerable. Hence, how banks position themselves in the network has important implications for their risk profile. Overall the results presented in this thesis highlight the complex role played by financial interlinkages. Therefore, they demonstrate the need to embed the network dimension in the regulatory framework to properly assess the stability profile of financial systems. Such findings are relevant for both theoretical modeling and empirical investigations. We believe that they also shed light on crucial aspects of systemic risk relevant for policy making and regulation of today's complex financial systems. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

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

Abstract This doctoral thesis aims to contribute to the literature on hedge fund performance and risk by conducting four interrelated essays. The first two essays measure and predict hedge fund performance using novel methodologies based on recent development in portfolio choice techniques. This new way to evaluate fund performance relies on economic theory and robust econometric principles. The first essay exploits hedge fund characteristics in order to pick right funds into a portfolio, whereas the second essay predicts hedge fund performance using conditional information that is contained in macroeconomic variables. The empirical analysis shows that the proposed conditional real-time portfolio strategies deliver significant outperformance over the unconditional benchmark strategy which does not utilize conditional information. The third essay investigates whether a particular hedge fund with specific fund characteristics contributes to systemic risk and how hedge funds with a high systemic risk contribution perform during the times of financial distress. The findings suggest that the fund’s capital structure is related to its systemic risk contribution, and, furthermore, that hedge funds with a high systemic risk contribution tend to deliver extremely poor performance during the times of financial distress. The fourth essay examines the impact of share restrictions on hedge fund performance and risk-taking. The essay finds that hedge funds with a lockup period tend to take excess risk that is not compensated when performance is measured as a unit of risk taken by the hedge fund. In addition, the length of notice periods increases along with the illiquidity level of fund investments. Finally, hedge funds with a long notice period seem to be able to earn an illiquidity premium.

Le déclenchement de la dette des Subprime en 2007, suivie de la crise de la dette Européenne en 2011, a attiré l'attention vers le risque de crédit, ses causes et ses implications. Depuis, Les décideurs des politiques économiques cherchent à trouver un moyen pour réguler les mouvements sur le marché des obligations et des dettes. Ainsi, sous l'impulsion du Conseil de stabilité financière, les accords de Bâle III sont apparus. Ce sont des réformes visant à renforcer le système financier afin d'affirmer la solidité financière des banques en imposant des conditions d'emprunt dont un niveau minimum de capitaux propres. Les travaux étudiant les risque de crédit avec ses différentes façades ont connu un boom. Il y a notamment la modélisation du risque de crédit qui s'est énormément développée. Cette thèse s'inscrit dans une branche macroéconomique à l'échelle Européenne. Nous essayons d'identifier les déterminants du risque de crédit aux niveaux souverains et bancaires et d'étudier les connections entre les deux. Dans une première partie, nous utilisons une modélisation Autoregressive à retards échelonnés1(ARDL) an de définir les déterminants macroéconomiques d'un échantillon de pays européens. Nous suggérons que le risque de crédit de ces derniers dépend largement des fondamentaux macroéconomiques avec des élasticités différentes selon la santé économique du pays. Plus précisément, nous trouvons que l'endettement a des effets opposés suivant le niveau du risque de crédit du pays. Dans la deuxième partie, nous nous intéressons aux déterminants du risque de crédit d'un échantillon de groupes bancaires Européens. Depuis 2007, Ces derniers ont été affectés par les deux crises (Subprime et la crise de la dette Européenne). Les relations de long terme dénies selon les tests de cointégration via l'approche des Bounds Tests 2, montrent qu'une dévaluation de l'Euro baisse le risque de crédit des banques étudiées en rendant leurs dettes libellées en Euro moins coûteuses. Aussi, notre analyse indique que la valeur de marché de l'entité ainsi que l'indice boursier dans lequel la banque est inscrite sont inconsistant dans l'explication du risque de crédit de cette dernière. Dans la dernière partie nous étudions les relations de causalité entre les risques de crédit des entités souveraines et bancaires étudiées dans les chapitres précédents. Les tests de causalité au sens de Granger révèlent que les relations trouvées sont asymétriques et dynamiques. Ces liens varient considérablement en fonction de l'état de l'économie de la région. L'analyse montre aussi que juste avant les période de grandes turbulences financières, notamment les crises financières, le transfert du risque de crédit est très important augmentant ainsi les contagions et par la suite le risque systémique. Cependant, la propagation de la méfiance et de la prudence pendant les périodes d'incertitude et des crises, fait baisser significativement le transfert du risque de crédit. Ceci s'accentue dans une zone monétaire comme la zone Euro puisque les pays adoptent forcément les mêmes politiques monétaires voire fiscales malgré leur hétérogénéité. / The outbreak of the Subprime debt in 2007, followed by the European debt crisis in 2011, drew attention to credit risk, its causes and implications. Since then, the economic policy makers are seeking to and a way to regulate the movementson the bond and debt market. Thus, the Basel III appeared under the guidance of the Financial Stability Board. These are reforms aiming at strengthening the financial system in order to assert the financial soundness of banks by imposingloan conditions and requirements including a minimum level of Capital. Works studying the credit risk with its diferent fronts boomed. Credit risk modeling has expanded tremendously. This thesis fits into a macroeconomic branchon a European scale. We try to identify the determinants of credit risk on the sovereign and banking levels and to study the connections between both. In the first section, Using Autoregressive Distributed Lag Modeling (ARDL), weempirically investigate the link between the macroeconomic fundamentals and sovereign credit risk for particular countries in the Euro zone. The studied sample was affected by disadvantageous economic conditions. We did not retain the same macroeconomic factors to explain the risk of default for the selected countries. The results, indicate that the reditworthiness of the studied entities depends largely on macroeconomic fundamentals with various elasticities which require a different economic policy for each country. The assessment of the results shows that the unemployment rate is the most influential variable especially for countries with disadvantageous economic conditions. The estimated relationships are globally stable in the long run (for 7 out of 9 countries), while the short run links are rare (except the unemployment rate). In the second section, we investigate the long-run relationships between European Banks' Credit default swap spreads and contextual factors using Bounds testing approach to cointegration (ARDL-ECM). The results reveal that in the long run, an increase of the inflation and/or the home countries' credit risk rise the European banks' credit risk as measured by credit default swap spreads. The estimatessuggest that the devaluation of the Euro, makes Euro-denominated debt less costly which lowers the credit risk of the European entities. Yet, unlike what is expected, our analysis shows that the market value of an entity as well as the stock index in which the firm is registered are becoming insignificant in explaining its credit risk. In this last section, we investigate the evolution and the expansion of the CDS network among the studied entities over the 2008 - 2013 period by splitting it intothree sub-periods. We highlight the variation of the connectedness according to the financial and economic characteristics of each studied sub-period. We found that the resulting relationships are not symmetrical and that they vary considerablydepending on the state of the region economy. We also show that just before huge financial turmoil phases, the risk transfer is very important increasing contagion and the systemic risk, while it drops significantly during uncertainty times marked by mistrust spread. This is particularly important in the European Union as countries adopt the same monetary policies while being heterogeneous.

We extend sociodynamic modeling of the financial business cycle to the Euro Area and Japan. Using an opinion-formation model and machine learning techniques we find stable model estimation of the financial business cycle using central bank lending surveys and a few selected macroeconomic variables. We find that banks have asymmetric response to good and bad economic information, and that banks adapt to their peers' opinions when changing lending policies.

Cette thèse contribue en quatre chapitres à l’analyse et la mesure du risque systémique. Le premier chapitre discute la notion de risque systémique et détaille les enjeux méthodologiques de sa modélisation. Le deuxième chapitre propose un modèle structurel de contagion en solvabilité. Ce modèle d’équilibre permet de mesurer le risque de contagion en distinguant l’effet direct d’un choc de sa propagation. Dans le troisième chapitre, nous fournissons un cadre de valorisation de la dette d’une institution intégrant l’effet des interconnexions entre institutions. Nous calculons une prime de risque spécifiquement liée aux interconnexions. Dans le quatrième chapitre, nous modélisons les effets conjoints de chocs à l’actif et au passif d’une institution financière. Nous adaptons les mesures usuelles de risque pour identifier les risques de marché, de financement et de liquidité de marché. Enfin, nous expliquons comment déterminer la composition et le niveau des réserves réglementaires pour limiter le risque de défaut. / This thesis contributes to the analysis and measure of systemic risk through four chapters. In the first chapter, we discuss the notion of systemic risk and detail the methodological issues of modeling. The second chapter proposes a structural model of solvency contagion. Within an equilibrium model, we measure the contagion by identifying the direct effect of an external shock and its propagation. In the third chapter, we provide a pricing framework for financial institution’s debt encompassing the effect of interconnections between institutions. We compute a risk premium specific to interconnections. In the last chapter, we model the joint effects of the shocks on the asset side and on the liability side of a financial institution. We adapt the usual risk measures to pinpoint the funding liquidity risk and the market liquidity risk. Lastly, we explain how to set the level and the composition of regulatory reserves to control for default risk.