Within Central Counterparty Clearing, the Clearing House collects Initial Margin from its Clearing Members. The Initial Margin can be calculated in many ways, one of which is by applying the commonly used risk measure Value-at-Risk. However, Value-at-Risk has one major flaw, namely its inability to encapsulate Tail Risk. Due to this, there has for long been a desire to replace Value-at-Risk with Expected Shortfall, another risk measure that has shown to be much better suited to encapsulate Tail Risk. That said, Value-at-Risk is still used over Expected Shortfall, something which is mainly due to the fact that there is no consensus regarding how one should backtest Expected Shortfall. The goal of this thesis is to evaluate some of the most commonly proposed methods for backtesting Expected Shortfall. In doing this, several non-parametric backtests of Expected Shortfall are investigated using simulated data as well as market data from different types of securities. Moreover, this thesis aims to shed some light on the differences between Value-at-Risk and Expected Shortfall, highlighting why a change of risk measure is not as straightforward as one might believe. From the investigations of the thesis, several backtests are found to be sufficient for backtesting the Initial Margin with Expected Shortfall as the risk measure, the so called Minimally Biased Relative backtest showing the overall best performance of the looked at backtests. Further, the thesis visualizes how Value-at-Risk and Expected Shortfall are two risk measures that are inherently different in a real-world setting, emphasizing how one should be careful making conversions between the two based upon parametric assumptions.

Machine learning is a rapidly growing field within artificial intelligence that an increasing number of individuals and corporations are beginning to utilize. In recent times, the financial sector has also started to recognize the potential of these techniques and methods. Nasdaq Clearing is responsible for managing the clearing business for the clearinghouse's members, and the objective of this thesis has been to explore the possibilities of using machine learning to replicate a subpart of the SPAN model's margin call calculations, known as initial margin, in the commodities market. The purpose of replicating SPAN's initial margin calculations is to open up for possibilities to create transparency and understanding in how the input variables affect the output. In the long run, we hope to broaden the insights on how one can use machine learning within the margin call processes. Various machine learning algorithms, primarily focused on regression tasks but also a few classification ones, have been employed to replicate the initial margin size. The primary objective of the methodology was to determine the algorithm that demonstrated the best performance in obtaining values that were as close as possible to the actual initial margin values. The findings revealed that a model composed of a combination of classification and regression, with non-parametric algorithms such as Random Forest and KNN, performed the best in both cases. Our conclusion is that the developed model possesses the ability to effectively compute the size of the initial margin and thus accomplishes its objective. / Maskininlärning är ett snabbt växande område inom artificiell intelligens som allt fler individer och företag börjar använda. Finanssektorn har nu också börjat undersöka hur dessa tekniker och metoder kan skapa värde. Nasdaq Clearing hanterar clearingverksamheten för clearinghusets medlemmar och syftet med denna uppsats har varit att undersöka möjligheterna att använda maskininlärning för att replikera en del av SPAN-modellens beräkningar av marginkravet som kallas Initial Marginal. Syftet med att replikera SPANs initiala marginberäkningar är att öppna upp för möjligheter att skapa transparens och förståelse för hur inputvariablernapåverkar outputen. På sikt hoppas vi kunna bredda insikterna hur maskininlärningslösningar skulle kunna användas inom "Margin Call"- processen. De metoder som användes för att replikera storleken på Initial Margin var olika maskininlärningsalgoritmer, främst fokuserade på regressionsuppgifter men några klassificeringsalgoritmer användes också. Fokus i metoden var att hitta vilken algoritm som presterade bäst, det vill säga den algoritm som predikterade närmst de faktiska värdena för Initial Margin. Resultatet visade sig vara en modell som kombinerade klassificering och regression, där icke-parametriska algoritmer såsom Random Forest och KNN var de som presterade bäst i båda fallen. Vår slutsats är att den utvecklade modellen har en god förmåga att beräkna storleken på Initial Margin och därmed uppfyller den sitt syfte.

Central Clearing Counterparties play a crucial role in financial markets, requiring robust risk management practices to ensure operational stability. A growing emphasis on risk analysis and stress testing from regulators has led to the need for sophisticated tools that can model extreme but plausible market scenarios. This thesis presents a method leveraging Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to construct an independent scenario generator capable of modeling and generating return distributions for financial markets. The developed method utilizes two primary components: the WGAN-GP model and a novel scenario selection strategy. The WGAN-GP model approximates the multivariate return distribution of stocks, generating plausible return scenarios. The scenario selection strategy employs lower and upper bounds on Euclidean distance calculated from the return vector to identify, and select, extreme scenarios suitable for stress testing clearing members' portfolios. This approach enables the extraction of extreme yet plausible returns. This method was evaluated using 25 years of historical stock return data from the S&P 500. Results demonstrate that the WGAN-GP model effectively approximates the multivariate return distribution of several stocks, facilitating the generation of new plausible returns. However, the model requires extensive training to fully capture the tails of the distribution. The Euclidean distance-based scenario selection strategy shows promise in identifying extreme scenarios, with the generated scenarios demonstrating comparable portfolio impact to historical scenarios. These results suggest that the proposed method offers valuable tools for Central Clearing Counterparties to enhance their risk management. / Centrala motparter spelar en avgörande roll i dagens finansmarknad, vilket innebär att robusta riskhanteringsrutiner är nödvändiga för att säkerställa operativ stabilitet. Ökande regulatoriskt tryck för riskanalys och stresstestning från tillsynsmyndigheter har lett till behovet av avancerade verktyg som kan modellera extrema men troliga marknadsscenarier. I denna uppsats presenteras en metod som använder Wasserstein Generative Adversarial Networks med Gradient Penalty (WGAN-GP) för att skapa en oberoende scenariogenerator som kan modellera och generera avkastningsfördelningar för finansmarknader. Den framtagna metoden består av två huvudkomponenter: WGAN-GP-modellen och en scenariourvalstrategi. WGAN-GP-modellen approximerar den multivariata avkastningsfördelningen för aktier och genererar möjliga avkastningsscenarier. Urvalsstrategin för scenarier använder nedre och övre gränser för euklidiskt avstånd, beräknat från avkastningsvektorn, för att identifiera och välja extrema scenarier som kan användas för att stresstesta clearingmedlemmars portföljer. Denna strategi gör det möjligt att erhålla nya extrema men troliga avkastningar. Metoden utvärderas med 25 års historisk aktieavkastningsdata från S&P 500. Resultaten visar att WGAN-GP-modellen effektivt kan approximera den multivariata avkastningsfördelningen för flera aktier och därmed generera nya möjliga avkastningar. Modellen kan dock kräva en omfattande mängd träningscykler (epochs) för att fullt ut fånga fördelningens svansar. Scenariurvalet baserat på euklidiskt avstånd visade lovande resultat som ett urvalskriterium för extrema scenarier. De genererade scenarierna visar en jämförbar påverkan på portföljer i förhållande till de historiska scenarierna. Dessa resultat tyder på att den föreslagna metoden kan erbjuda värdefulla verktyg för centrala motparter att förbättra sin riskhantering.

Suite à la crise financière de 2008 les pays du G20 se sont interrogés sur la transparence des marchés, la stabilité du système et une façon de réguler les risques posés par le nouvel environnement économique. Les produits dérivés de gré à gré ont été identifiés et des engagements ont été pris en faveur de nouvelles régulations des dérivés de gré à gré et la gestion des risques sous-jacents. Les régulateurs ont donc adopté chacun à leur tour un cadre législatif régulant les dérivés de gré à gré tout en déployant un effort international d'harmonisation et de reconnaissance des contreparties assujetties à des régimes équivalents. Les autorités canadiennes en valeurs mobilières ont publié des projets de règlements. Nous nous interrogerons sur ce nouveau cadre réglementaire des dérivés de gré à gré élaboré par les autorités canadiennes en valeurs mobilières, prenant en considération les spécificités canadiennes et les acteurs actifs sur leur territoire. Notre étude traite de ces projets de règlements et de la difficulté d'encadrer les marchés des dérivés de gré à gré qui par définition ne comportent pas de plateformes de négociation ou de lieu géographique et de frontières mais se caractérisent surtout par le lien contractuel entre les parties et l'identification de ces parties. L'élaboration d'un nouveau cadre pour les dérivés de gré à gré qui régule les transactions transfrontières semble très délicat à traiter et les possibles conflits et chevauchements de lois seront inévitables. Confrontés à des définitions divergentes de contreparties locales, les parties à une opération seront condamnées à un risque de qualification en vertu des règlements nationaux sur les dérivés de gré à gré. Une concertation pourrait être renforcée et la détermination de l'autorité compétente ainsi que les concepts de contreparties locales, succursales ou filiales pourraient être harmonisés. / Having in mind the 2008 financial crisis the G20 countries have questioned the transparency of the markets, the stability of the system and explored new solutions in order to control the risks triggered by a new economic environment. OTC derivatives have been identified and commitments were made in favor of new regulations of OTC derivatives and the management of the underlying risks. Regulators have therefore adopted in turn a legislative framework regulating OTC derivatives while deploying an incentive for international harmonization and recognition of counterparties subject to equivalent schemes. Canadian Securities Administrators have issued proposed regulations. We wonder how the Canadian Securities Administrators have developed this new regulatory framework for OTC derivatives, taking into consideration the Canadian specificities and the local activities of the Canadian actors? Our study addresses these draft regulations and the difficulty to supervise OTC derivatives which in the past, were not traded via platforms, recorded or related to a geographic location but mainly characterized by the contractual relationship between the parties. The development of a new framework for OTC derivatives which will regulate cross-border transactions seem very difficult to deal with and possible conflicts and overlapping laws are inevitable. Faced with different definitions of local counterparts, the parties to a transaction will be condemned to a risk of qualification under national regulations of OTC derivatives. A dialogue shall therefore be strengthened and the determination of the competent authority and clarification of the local counterparts concepts, branches or subsidiaries shall be harmonized.