This dissertation implements an optimal trading strategy based on the machine learning method and extreme value theory (EVT) to obtain an excess return on investments in the capital market. The trading strategy outperforms the benchmark S&P 500 index with higher returns and lower volatility through effective market timing. In addition, this dissertation starts by modeling the market tail risk using the EVT and reinforcement learning methods, distinguishing from the traditional value at risk method. In this dissertation, I used EVT to extract the characteristics of the tail risk, which are inputs for reinforcement learning. This process is proved to be effective in market timing, and the trading strategy could avoid market crash and achieve a long-term excess return. In sum, this study has several contributions. First, this study takes a new method to analyze stock price (in this dissertation, I use the S&P 500 index as a stock). I combined the EVT and reinforcement learning to study the price tail risk and predict stock crash efficiently, which is a new method for tail risk research. Thus, I can predict the stock crash or provide the probability of risk, and then, the trading strategy can be built. The second contribution is that this dissertation provides a dynamic market timing trading strategy, which can significantly outperform the market index with a lower volatility and a higher Sharpe ratio. Moreover, the dynamic trading process can provide investors an intuitive sense on the stock market and help in decision-making. Third, the success of the strategy shows that the combination of EVT and reinforcement learning can predict the stock crash very well, which is a great improvement on the extreme event study and deserves further study. / Business Administration/Finance

This thesis investigates different aspects of the impact of extreme downside risk on stock returns. We first investigate the impact at market level, where the return of the stock market index is expected to be positively correlated to its tail risk. More specifically, we incorporate Markov switching mechanism into the framework of Bali et al. (2009) to analyse the relationship between risk and returns under different market regimes. Interestingly, although highly significant in calm periods, the tail risk-return relationship cannot be captured during turbulent times. This is puzzling since this is the time when the distress risk is most prominent. We show that this pattern persists under different modifications of the framework, including expanding the set of state variables and accounting for the non-iid feature of return process. We suggest that this result is due to the leverage and volatility feedback effects. To better filter out these effects, we propose a simple but effective modification to the risk measures which reinstates the positive extreme risk-return relationship under any state of market volatility. The success of our method provides insights into how extreme downside risk is factored into expected returns. In the second investigation, this thesis explores the impact of extreme downside risk on returns in a security level analysis. We demonstrate that a stock with higher tail risk exposure tends to experience higher average returns. Motivated by the limitations of systematic extreme downside risk measures in the literature, we propose two groups of new ‘co-tail-risk’ measures constructed from two different approaches. The first group is the natural development of canonical downside beta and comoment measures, while the second group is based on the sensitivity of stock returns on innovations in market systematic crash risk. We utilise our new measures to investigate the asset pricing implication of extreme downside risk and show that they can capture a significant positive relationship between this risk and expected stock return. Moreover, our second group of ‘co-tail-risk’ measures show a highly consistent performance even in extreme settings such as low tail threshold and monthly sample estimation. The ability of this measure to generate a number of observations given limited return data solves one of the most challenging problems in tail risk literature. In the last investigation, this thesis examines the influence of extreme downside risk on portfolio optimisation. It is motivated by the evidence in Chapter 4 regarding the size pattern of the extreme downside risk impact on stock returns where the impact is larger for small stocks. Accordingly, portfolio optimisation practice that focuses on tail risk should be more effective when applied to small stocks. In comparing the performance of mean-Expected Tail Loss against that of mean-variance across size groups of Fama and French’s (1993) sorted portfolios, we confirm this conjecture. Moreover, we further investigate the performance of different switching approaches between mean-variance and mean-Expected Tail Loss to utilise the suitability of these optimisation methods for specific market conditions. However, our results reject the use of any switching method. We demonstrate the reason switching could not enhance performance is due to the invalidity of the argument regarding the suitability of any optimisation method for a specific market regime.

<p>This dissertation explores issues regarding the short-lived temporal variation of the equity risk premium. In the past decade, the equity risk premium puzzle is resolved by many competing consumption-based asset pricing models. However, before \cite{btz:vrp:rfs}, the return predictability as an outcome of such models has limited empirical support in the short-run. Nowadays, there has been a consensus of the literature that the short-run equity return's predictability is intimately linked with the variance risk premium---the difference between options-implied and actual realized variation measures.</p><p>In this work, I continue to argue the importance of the short-lived components in the equity risk premium. Specifically, I first provide simulation evidence of the strong return predictability based on the variance risk premium in the U.S. aggregate market, and document new empirical findings in the international setting. Then I attempt to use a structural macro-finance model to guide through the predictability estimation with much more efficiency gain. Finally I decompose the equity risk premium into two short-lived parts --- tail risk and diffusive risk --- and propose a semi-parametric estimation method for each part. The results are arranged in the following order.</p><p>Chapter 1 of the dissertation is co-authored with Tim Bollerslev, James Marrone and Hao Zhou. In this chapter, we demonstrate that statistical finite sample biases cannot ``explain'' this apparent predictability in U.S. market based on variance risk premium. Further corroborating the existing evidence of the U.S., we show that country specific regressions for France, Germany, Japan, Switzerland, the Netherlands, Belgium and the U.K. result in quite similar patterns. Defining a ``global'' variance risk premium, we uncover even stronger predictability and almost identical cross-country patterns through the use of panel regressions. </p><p>Chapter 2 of the dissertation is co-authored with Tim Bollerslev and Hao Zhou. In this chapter, we examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidences that the expected return variation and the variance risk premium positively forecast both short-horizon returns \textit{and} dividend growth rates. We also confirm that dividend yield positively forecasts long-horizon returns, but that it does not help in forecasting dividend growth rates. Our equilibrium-based ``structural'' factor GARCH model permits much more accurate inference than %the reduced form VAR and</p><p>univariate regression procedures traditionally employed in the literature. The model also allows for the direct estimation of the underlying economic mechanisms, including a new volatility leverage effect, the persistence of the latent long-run growth component and the two latent volatility factors, as well as the contemporaneous impacts of the underlying ``structural'' shocks.</p><p>In Chapter 3 of the dissertation, I develop a new semi-parametric estimation method based on an extended ICAPM dynamic model incorporating jump tails. The model allows for time-varying, asymmetric jump size distributions and a self-exciting jump intensity process while avoiding commonly used but restrictive affine assumptions on the relationship between jump intensity and volatility. The estimated model implies that the average annual jump risk premium is 6.75\%. The model-implied jump risk premium also has strong explanatory power for short-to-medium run aggregate market returns. Empirically, I present new estimates of the model based equity risk premia of so-called "Small-Big", "Value-Growth" and "Winners-Losers" portfolios. Further, I find that they are all time-varying and all crashed in the 2008 financial crisis. Additionally, both the jump and volatility components of equity risk premia are especially important for the "Winners-Losers" portfolio.</p> / Dissertation

A Case Study on Long-tail Risks and Risk Mitigation in Risk Management.   How can Allianz Global Corporate and Specialty (AGCS) make best use of risk mitigation measures for drafting product liability policy wordings? A case study on Triclosan as a possible Endocrine Disruptor with the potential for Mass Litigation.   With external forces, the insurance industry has been facing issues since before  9/11 but the evolvement of risk managers and risk management programs in organizations has become a standard for all corporations due to the realization of the potential impact these external forces and risks possibly possess. These programs have emerged to reduce the risk and uncertainty factor that organizations are facing. The factors have been identified in previous literature, as the regulation through authorities (Carroll et al., 2016), the customer relationship that to a certain degree even embraces risk (Kerr, 2016), the agency risk in risk taking (Eling &amp; Marek, 2013). In terms to prepare for these risks, the corporations need to go through a rescaling of their business which was associated with the establishment of Risk Management Processes on all levels (Thislethwaite and Wood, 2018). As such, the rescaling in general can be seen as a Risk Management (RM) structure that would framework the communication of risk in a company.   The insurer AGCS is studied on its Risk Management (RM) processes, especially in the fourth phase of RM which is the phase of risk mitigation or reduction. Here it has previously been identified there being no other possible ethical actuarial mitigation methods for long-tail risks (Carroll et al., 2016). Therefore, a risk with such categories was studied with the study on Triclosan. Triclosan is a widely spread and commonly used chemical substance with certain and uncertain causations that can pose several risks with one of them being the possibility of mass litigation. The underwriter tool to mitigate such long-tail risks has been defined as the policy wording which can be used to create an optimal contract in the product liability insurance to reduce the risk of mass litigation.    To answer the above research question, this study has taken an interpretivist stance and the form of a quantitative study to follow the framework of Yin’s (2009) case study approach. With the goal to research the meaning behind a phenomenon, rather than to quantify a phenomenon, the use of semi structured interviews with experts of the insurance industry was conducted. These experts were found in the departments of Allianz Risk Consulting, Underwriting, and Claims.    The findings, similarly to the previous research that has been discussed in the introductory chapter, found that there are certain macro forces that shape the risk mitigation phase and here the influence on the policy wording within was touched upon. It was found that regulations do play a vital part and pose as leverage for the insurer and a pillar that would carry the weight of policy wording. It has further been identified that the costumer relationship and the costumer strength in the market are responsible for a functioning risk mitigation and also that certain demands stemming from the market, will shape the product liability insurance. While the more specific answer to the research question was, yes, the corporate insurer should cover triclosan related risks on a claims-made basis, with serial-loss clause and a retroactive date, there would be other factors that influence the policy wording. The grounded theory that has been established in this research is thus;    To manage liability insurance coverage for long-tail risks, product liability policy wording language needs to reflect main pillars as being used for comparable base materials. This includes but is not limited to claims made trigger, retro-active dates and other coverage elements. Macro forces and drivers of the policy wording, include but are not limited to, costumer strength, market demand, risk perception and market regulations. To ensure a successful risk management on an enterprise level for coverage of long-tail risks, the above factors have to be accounted for when offering product liability coverage.   Based on the aforementioned theory, Triclosan is a manageable risk from a corporate liability insurers perspective, hence insurance coverage can be given under product liability policy wordings.   Here it is proposed that further research be conducted on the identified macro forces and their impact on the product liability insurance and the more general RM in organizations. Also, it is proposed to research such a possible framework for including the costumer in the process of risk mitigation in terms of reducing the risks form where they start with the starting point being at the costumer. This is a future vision that as such would need further research to reach scientific saturation.

Submitted by Kym Marcel Martins Ardison (kymmarcel@gmail.com) on 2018-09-16T17:36:08Z No. of bitstreams: 1 TESE.pdf: 3325190 bytes, checksum: e1bbe47dabd1e2befe53e9ace94ab46b (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2018-10-25T19:43:48Z (GMT) No. of bitstreams: 1 TESE.pdf: 3325190 bytes, checksum: e1bbe47dabd1e2befe53e9ace94ab46b (MD5) / Made available in DSpace on 2018-10-29T20:07:02Z (GMT). No. of bitstreams: 1 TESE.pdf: 3325190 bytes, checksum: e1bbe47dabd1e2befe53e9ace94ab46b (MD5) Previous issue date: 2018-04-12 / We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identi able stochastic discount factors (SDFs). These SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments between HF and benchmark factors returns. We provide full asymptotic theory for our SDF estimators that allows us to test for the statistical signi cance of each fund's performance and for the relevance of individual benchmark factors in identifying each proposed measure. Empirically, we apply our methodology to a large panel of individual hedge fund returns, revealing sizable di erences across performance measures implied by di erent exposures to higher-order mixed moments. Moreover, when we compare SDF-based measures to the traditional linear regression approach (Jensen's alpha), our measures identify a signi cantly smaller fraction of funds in the cross-section of HFs with statistically signi cant performances

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

Submitted by Kym Marcel Martins Ardison (kymmarcel@gmail.com) on 2015-04-06T19:04:20Z No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) / Approved for entry into archive by BRUNA BARROS (bruna.barros@fgv.br) on 2015-04-28T12:21:10Z (GMT) No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-05-04T12:33:49Z (GMT) No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) / Made available in DSpace on 2015-05-04T12:37:02Z (GMT). No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) Previous issue date: 2015-02-12 / This paper proposes a new novel to calculate tail risks incorporating risk-neutral information without dependence on options data. Proceeding via a non parametric approach we derive a stochastic discount factor that correctly price a chosen panel of stocks returns. With the assumption that states probabilities are homogeneous we back out the risk neutral distribution and calculate five primitive tail risk measures, all extracted from this risk neutral probability. The final measure is than set as the first principal component of the preliminary measures. Using six Fama-French size and book to market portfolios to calculate our tail risk, we find that it has significant predictive power when forecasting market returns one month ahead, aggregate U.S. consumption and GDP one quarter ahead and also macroeconomic activity indexes. Conditional Fama-Macbeth two-pass cross-sectional regressions reveal that our factor present a positive risk premium when controlling for traditional factors.

Submitted by Laura Simonsen Leal (arula@fgvmail.br) on 2016-06-22T12:59:34Z No. of bitstreams: 1 laura_tese14(final).pdf: 1036208 bytes, checksum: eac8007047195b00593f30884e72a3e2 (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2016-06-22T13:18:16Z (GMT) No. of bitstreams: 1 laura_tese14(final).pdf: 1036208 bytes, checksum: eac8007047195b00593f30884e72a3e2 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2016-06-29T13:38:50Z (GMT) No. of bitstreams: 1 laura_tese14(final).pdf: 1036208 bytes, checksum: eac8007047195b00593f30884e72a3e2 (MD5) / Made available in DSpace on 2016-06-29T13:39:56Z (GMT). No. of bitstreams: 1 laura_tese14(final).pdf: 1036208 bytes, checksum: eac8007047195b00593f30884e72a3e2 (MD5) Previous issue date: 2016-03-21 / The main purpose of this paper is to propose a methodology to obtain a hedge fund tail risk measure. Our measure builds on the methodologies proposed by Almeida and Garcia (2015) and Almeida, Ardison, Garcia, and Vicente (2016), which rely in solving dual minimization problems of Cressie Read discrepancy functions in spaces of probability measures. Due to the recently documented robustness of the Hellinger estimator (Kitamura et al., 2013), we adopt within the Cressie Read family, this specific discrepancy as loss function. From this choice, we derive a minimum Hellinger risk-neutral measure that correctly prices an observed panel of hedge fund returns. The estimated risk-neutral measure is used to construct our tail risk measure by pricing synthetic out-of-the-money put options on hedge fund returns of ten specific categories. We provide a detailed description of our methodology, extract the aggregate Tail risk hedge fund factor for Brazilian funds, and as a by product, a set of individual Tail risk factors for each specific hedge fund category.

För investerare vars portföljer utgörs av internationella investeringar är det i synnerhet viktigt att begrunda beroendestrukturen mellan internationella investeringar och valutakurser. Detta på grund av den valutarisk som investeraren exponerar sig mot utöver de internationella tillgångarnas inneboende risk. I denna studie undersöks hur svenska investerare med investeringar i den amerikanska aktie- och företagsobligationsmarknaden påverkas av valutakursförändringar i USD:SEK. De amerikanska investeringarna är i denna studie denominerade i amerikanska dollar men portföljen och dess risk är denominerad i svenska kronor, portföljen påverkas därmed av valutaeffekten.Vidare undersöks samvariationen mellan dessa tillgångar och en optimal valutahedge upprättas för att reducera svansrisk i en sådan portfölj.  För att bestämma en optimal valutahedge optimeras CVaR för nio olika portföljer med olika viktning av S&amp;P 500, investment grade- företagsobligationer och high yield-företagsobligationer. Två metoder för att ta fram scenariopriser till optimeringen används: historisk simulering samt Monte Carlo-simulering från en vine-copula. Resultaten i denna studie antyder att svenska investerare bör hedga bort viss exponering mot USD. På den amerikanska aktiemarknaden bör större andel av valutarisken bibehållas än på den amerikanska high yield-obligationsmarknaden. Detta antyder att viss valutarisk bidrar med en hedgande effekt. På den amerikanska investment grade-obligationsmarknaden bör endast en mycket liten exponering mot USD bibehållas och ingen tydlig hedgande effekt kunde påvisas. Analys av samvariation mellan amerikansk aktiemarknad, företagsobligationsmarknad och valutakursen USD:SEK antyder att USD:SEK uppvisar förhöjt negativt beroende vid svansutfall i både den amerikanska aktiemarknaden och high yield-obligationsmarknaden. Detta antyder att USD uppvisar så kallade safe haven-egenskaper för svenska investerare i dessa marknader. / For investors whose portfolios consist of international investments, it is of particular importance to consider the dependence structure between international investments and foreign exchange rates. This is due to the currency risk that the investor is exposed to in addition to the inherent risk of the international assets. This study examines how Swedish investors with investments in the US equity and corporate bond market are affected by exchange rate fluctuations in the currency pair USD:SEK. In this study, US investments are denominated in US dollars, but the portfolio and its risk are denominated in Swedish Kronor, the portfolio is thus affected by the foreign currency effect. Furthermore, the covariation between these assets is examined and an optimal hedge is established in order to reduce tail risk in such a portfolio. To determine the optimal currency hedge, CVaR is optimized for nine different portfolios with different weightings of S&amp;P 500, investment grade corporate bonds and high yield corporate bonds. Two methods for producing scenario prices for the optimization are used: historical simulation and Monte Carlo simulation from a vine copula. The results of this study suggest that Swedish investors should hedge some of the exposure against USD. In the US stock market, a larger share of currency risk should be maintained than in the US high yield bond market. This suggests that some currency risk contributes to a hedging effect. In the US investment grade bond market little exposure against USD should be maintained and no clear hedging effect could be demonstrated. Analysis of covariation between the US stock market, corporate bond market and the exchange rate USD:SEK indicates that USD:SEK displays increased negative dependence in tail events in boththe US stock market and the high yield bond market. This indicates that USD displays so-called safe haven properties for Swedish investors in these markets.

Cette thèse est composée de 3 essais. Le 1er essai traite de la problématique du risque de pertes extrêmes dans le secteur bancaire dans un contexte du problème d'agence entre les actionnaires et les top managers des banques. Pour pouvoir inciter les banques à ne pas prendre le risque de pertes extrêmes, il est proposé d'appliquer la régulation des fonds propres sous forme d'une politique de recapitalisations obligatoires, dont les paramètres sont choisis pour inciter les actionnaires à rémunérer leurs managers de la manière à les détourner des stratégies au risque de pertes extrêmes.Le 2ème essai développe le design de la supervision bancaire qui vise à éliminer le problème d'aléa moral au sein d'une banque, tout en assurant un coût minimum de supervisions. Les banques, dont la situation financière commence à se dégrader, doivent être soumises à des audits aléatoires. Les banques, dont la valeur de l'actif s'est dégradée considérablement, doivent être mises sous tutelle pour un redressement financier. Les auditeurs externes peuvent être impliqués dans le processus de supervision, mais ne doivent pas complètement remplacer les régulateurs. Le 3ème essai étudie comment la capacité d'emprunt de l'entreprise non-financière affecte sa politique d'investissement en présence des coûts d'émission de la dette. Il est montré que les entreprises, dont la capacité d'emprunt est moyenne, ont intérêt à réaliser un investissement plus important par rapport aux entreprises dont la capacité d'emprunt est relativement faible/forte. Cela est entièrement dû à l'effet des coûts fixes d'émission de la dette, qui émerge dans le contexte dynamique d'investissement. / This dissertation consists of 3 self-contained theoretical essays.Essay 1 brings into focus the problem of "manufacturing" tail risk in the banking sector. This work shows that, in order to prevent banks from engaging in tail risk, bank capital regulation should account for the internal agency problem between bank shareholders and bank top managers. It is proposed to design bank capital requirements in the form of incentive-based recapitalization mechanism which would induce bank shareholders to shape executive compensation in such a way as to prevent top managers from engaging in tail-risk.Essay 2 deals with the problem of moral hazard in bank asset management. It proposes the concept of incentive-based bank supervision aimed at preventing moral hazard at a minimum cost to the regulator. It is shown that the intensity of supervision efforts should be gradually adjusted to the bank's financial health: banks in the mild form of distress should be subject to random audits, whereas deeply distressed banks should be placed under temporary regulatory control. To prevent double moral hazard, external auditors involved in supervision should be offered the optimal incentive contract.Essay 3 examines the impact of credit rationing (debt capacity) on corporate investment in the setting with costly debt financing. It is shown that, when credit constraints are binding, the firms with intermediate levels of debt capacity will establish larger investment projects than the firms with relatively low or high debt capacity. This non-monotonicity of investment on debt capacity arises due to the effect of the lump-sum debt issuance costs in the dynamic context of investment.