In March 2022, OECD published a public consultation document entitled Crypto-Asset Reporting Framework and Amendments to the Common Repoting Standard (CARF). This doucment proposed new and amended requirements covering reporting and exchange of information of crypto-assets as well as containing broader revisions to the existing Common Reporting Standard (CRS) for the automatic exhange of informaiton (AEOI) between countries. In recent years, there has been a mass adoption of crypt-assets for a range of invesment and financial activities. OECD believes that the use of crypto-assets threatens the Common Reporting Standard (CRS) since crypto-assets can be easily transferred without a central administrator and held inaccessbile crypto "wallets." In reponse, OECD drafted CARF in an attempt to retrofit regulations made for traditional financial institutions, a regulatory "choke point model," onto the nascent and quickly developing crypto-asset space. The thesis argues that CARF is flawed in several ways. First, the CARF's requirements deviate from CRS for unexplained reasons created extra costs and administrative burden for cryto-asset service providers (CASPs). Second, as crypto-assets are more in the nature of moveable assets, CARF's inartful attempt to retrofit CRS is onto the crypto-asset space is likely stifle innovation and technological development, especially critical for the developing world and shifting power away from banks and other large financial institutions back to individual consumers and merchants. Finally, CARF may not even materially meet its goal of increasing tax revenues and ensuring tax compliance.

Nichtstationarität ist eines der häufigsten, jedoch nach wie vor ungelösten Probleme in der Zeitreihenanalyse und ein immer wiederkehrendes Phänomen, sowohl in theoretischen als auch in angewandten Arbeiten. Die jüngsten Fortschritte in der ökonometrischen Theorie und in Methoden des maschinellen Lernens haben es Forschern ermöglicht, neue Ansätze für empirische Analysen zu entwickeln, von denen einige in dieser Arbeit erörtert werden sollen. Kapitel 3 befasst sich mit der Vorhersage von Mergers & Acquisitions (M&A). Obwohl es keinen Zweifel daran gibt, dass M&A-Aktivitäten im Unternehmenssektor wellenartigen Mustern folgen, gibt es keine einheitlich akzeptierte Definition einer solchen "Mergerwelle" im Zeitreihenkontext. Zur Messung der Fusions- und Übernahmetätigkeit werden häufig Zeitreihenmodelle mit Zähldaten verwendet und Mergerwellen werden dann als Cluster von Zeiträumen mit einer ungewöhnlich hohen Anzahl von solchen Mergers & Acqusitions im Nachhinein definiert. Die Verteilung der Abschlüsse ist jedoch in der Regel nicht normal (von Gaußscher Natur). In jüngster Zeit wurden verschiedene Ansätze vorgeschlagen, die den zeitlich variablen Charakter der M&A-Aktivitäten berücksichtigen, aber immer noch eine a-priori-Auswahl der Parameter erfordern. Wir schlagen vor, die Kombination aus einem lokalem parametrischem Ansatz und Multiplikator-Bootstrap an einen Zähldatenkontext anzupassen, um lokal homogene Intervalle in den Zeitreihen der M&A-Aktivität zu identifizieren. Dies macht eine manuelle Parameterauswahl überflüssig und ermöglicht die Erstellung genauer Prognosen ohne manuelle Eingaben. Kapitel 4 ist eine empirische Studie über Sprünge in Hochfrequenzmärkten für Kryptowährungen. Während Aufmerksamkeit ein Prädiktor für die Preise von Kryptowährungenn ist und Sprünge in Bitcoin-Preisen bekannt sind, wissen wir wenig über ihre Alternativen. Die Untersuchung von hochfrequenten Krypto-Ticks gibt uns die einzigartige Möglichkeit zu bestätigen, dass marktübergreifende Renditen von Kryptowährungenn durch Sprünge in Hochfrequenzdaten getrieben werden, die sich um Black-Swan-Ereignisse gruppieren und den saisonalen Schwankungen von Volatilität und Handelsvolumen ähneln. Regressionen zeigen, dass Sprünge innerhalb des Tages die Renditen am Ende des Tages in Größe und Richtung erheblich beeinflussen. Dies liefert grundlegende Forschungsergebnisse für Krypto-Optionspreismodelle und eröffnet Möglichkeiten, die ökonometrische Theorie weiterzuentwickeln, um die spezifische Marktmikrostruktur von Kryptowährungen besser zu berücksichtigen. In Kapitel 5 wird die zunehmende Verbreitung von Kryptowährungen (Digital Assets / DAs) wie Bitcoin (BTC) erörtert, die den Bedarf an genauen Optionspreismodellen erhöht. Bestehende Methoden werden jedoch der Volatilität der aufkommenden DAs nicht gerecht. Es wurden viele Modelle vorgeschlagen, um der unorthodoxen Marktdynamik und den häufigen Störungen in der Mikrostruktur zu begegnen, die durch die Nicht-Stationarität und die besonderen Statistiken der DA-Märkte verursacht werden. Sie sind jedoch entweder anfällig für den Fluch der Dimensionalität, da zusätzliche Komplexität erforderlich ist, um traditionelle Theorien anzuwenden, oder sie passen sich zu sehr an historische Muster an, die sich möglicherweise nie wiederholen. Stattdessen nutzen wir die jüngsten Fortschritte beim Clustering von Marktregimen (MR) mit dem Implied Stochastic Volatility Model (ISVM) auf einem sehr aktuellen Datensatz, der BTC-Optionen auf der beliebten Handelsplattform Deribit abdeckt. Time-Regime Clustering ist eine temporale Clustering-Methode, die die historische Entwicklung eines Marktes in verschiedene Volatilitätsperioden unter Berücksichtigung der Nicht-Stationarität gruppiert. ISVM kann die Erwartungen der Anleger in jeder der stimmungsgesteuerten Perioden berücksichtigen, indem es implizite Volatilitätsdaten (IV) verwendet. In diesem Kapitel wenden wir diese integrierte Zeitregime-Clustering- und ISVM-Methode (MR-ISVM) auf Hochfrequenzdaten für BTC-Optionen an. Wir zeigen, dass MR-ISVM dazu beiträgt, die Schwierigkeiten durch die komplexe Anpassung an Sprünge in den Merkmalen höherer Ordnung von Optionspreismodellen zu überwinden. Dies ermöglicht es uns, den Markt auf der Grundlage der Erwartungen seiner Teilnehmer auf adaptive Weise zu bewerten und das Verfahren auf einen neuen Datensatz anzuwenden, der bisher unerforschte DA-Dynamiken umfasst. / Nonstationarity is one of the most prevalent, yet unsolved problems in time series analysis and a reoccuring phenomenon both in theoretical, and applied works. Recent advances in econometric theory and machine learning methods have allowed researchers to adpot and develop new approaches for empirical analyses, some of which will be discussed in this thesis. Chapter 3 is about predicting merger & acquisition (M&A) events. While there is no doubt that M&A activity in the corporate sector follows wave-like patterns, there is no uniquely accepted definition of such a "merger wave" in a time series context. Count-data time series models are often employed to measure M&A activity and merger waves are then defined as clusters of periods with an unusually high number of M&A deals retrospectively. However, the distribution of deals is usually not normal (Gaussian). More recently, different approaches that take into account the time-varying nature of M&A activity have been proposed, but still require the a-priori selection of parameters. We propose adapating the combination of the Local Parametric Approach and Multiplier Bootstrap to a count data setup in order to identify locally homogeneous intervals in the time series of M&A activity. This eliminates the need for manual parameter selection and allows for the generation of accurate forecasts without any manual input. Chapter 4 is an empirical study on jumps in high frequency digital asset markets. While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto ticks gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models and opens up possibilities to evolve econometric theory to better address the specific market microstructure of cryptos. Chapter 5 discusses the increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), which raises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM) on a very recent dataset covering BTC options on the popular trading platform Deribit. Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we apply this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion and put the procedure to action on a new dataset covering previously unexplored DA dynamics.

Decentralized autonomous organizations, or DAOs, are spoken highly of in cryptocurrency spaces as a new way of organizing capital and labor. The basic concept is an organization with a shared vision or goal, where the participants and/or outside stakeholders own tokens that grant governing rights over resources through smart contracts. The smart contract usage allows the organization to govern resources without relying on any trusted third parties such as governments, banks, companies or other entities, at least in theory. It also allows the governing of resources without a legal entity. As the name suggests, DAOs have a large focus on decentralization, which raises the issue of how it moves in the right direction. Management control offers suggestions for how traditional organizations move in the right, or intended, direction, and this report tries to apply theory from that field to the DAO organization type. The purpose of this report is to investigate, describe and analyze how management control systems are used within DAOs. The report is an interpretive multiple case study, which gathers data from interviews, observations and a literature study. The data is analyzed by primarily using Malmi and Brown (2008), Olve and Nilsson (2018) and Simons (1994) to filter and identify management control systems. This report investigates three DAOs, DXdao, Index Coop and ENS DAO, that attempts to answer the question of how they use management control. DXdao develops products for the blockchain ecosystem, Index Coop creates index fund-like products that bundle together blockchain based assets and ENS DAO owns and furthers a product that lets users claim names on the Ethereum blockchain to be used as URLs, usernames or for other causes. The report identifies that many management control systems, such as budget, planing and values, are used in similar ways as in traditional companies. While some other systems such as rewards and compensation, and governance structure seem to have unique aspects to them though the use of tokens for compensation, and a governance process through blockchain based voting. Furthermore, the report identifies the different definition of Decentralization between the management control field and the blockchain industry. Where the blockchain industry focus on the distribution of decision-making and control, whereas in Management Controlthe focus is on the division of responsibility to managers. The report concludes that one can view decentralization as a position on a scale, between complete individual decision making to a completely collective decision-making, where most DAOs lay in between these two extremes.

This thesis evaluates and constructs a general, product-neutral legal concept and model of financial instruments, as opposed to the product-dependent definitions currently employed in contemporary capital markets law. Through a combination of law and economics perspectives, legal history, and comparative analysis, the study examines the various types of financial instruments currently and previously in use. The legal characteristics and features of these instruments are evaluated and compared, leading to the identification of commonalities that can be used to define a product-neutral concept. The thesis argues that such a concept is more beneficial to the function of the capital markets by removing obstacles for financial innovation while also providing a consistent way to ensure that new financial products are governed by the same regulatory framework as comparable instruments.The thesis also examines the historical evolution of financial instruments and how it has been driven by the evolution of international trade and the demand and surplus of available capital. The proposed concept is applied to current financial instruments, including equity and debt, as well as pre-modern markets, and evaluated in terms of regulation, practical use, and legal characteristics such as transferability and negotiability.The research of this thesis encountered several challenges and limitations. Firstly, the historical and comparative analysis proved difficult to carry out, due to limitation in available source material and language related restrictions, respectively. These limitations were overcome by limiting the scope and by employing contacts with law firms in the respective jurisdictions. Secondly, several key issues proved to require further research to be able to provide definitive conclusions. Such research would have been out of scope and as such, simplified explanations and models were employed. The thesis concludes with a discussion of the practical implications of the proposed concept, including its application to cryptocurrencies and similar assets, and identifies potential areas for future research.