[pt] O Bitcoin surgiu no fim da década passada. Desde então, emergiu uma nova classe de ativos: as criptomoedas. O ecossistema das moedas digitais vem avançando a passos largos, seja pelo surgimento de novas moedas, pelo nível de capitalização, pela escalada de investidores ou pelo expressivo desempenho em 2017. Dado o quadro, as criptomoedas se consolidam a cada dia como uma alternativa de investimento, tornando-se de vez uma rota do mercado financeiro. Por consequência, surge a necessidade de avaliar e estimar medidas de risco para esses ativos. Este estudo estimou os coeficientes Beta das quatorze maiores criptomoedas da economia – de acordo com o nível de capitalização – em relação à índices teóricos, com o fito de auxiliar os gestores de portfólios no apreçamento e na formatação de estratégias. Através de uma regressão de retornos passados destas moedas sobre os retornos dos índices de criptoativos, de ações e de uma cesta de moedas contra o dólar americano, estimou-se o Beta dos ativos. A partir das análises, concluiu-se que o Bitcoin possui elevada sensibilidade aos índices de criptomoedas, mesma condicionante para o Ethereum, porém com correlação mais branda aos referenciais, bem como ao próprio Bitcoin. Quantos às demais moedas, estas não exprimiram fator de risco associado aos índices de criptomoedas, visto os baixos coeficientes. Quando analisados os criptoativos em relação aos índices acionários e de moedas contra o dólar, constatou-se que os coeficientes foram iguais a zero. Portanto, o desempenho das criptomoedas, na janela de tempo estudada, possui relação involuntária às oscilações destes índices. / [en] Bitcoin has risen at the end of the last decade. Since then, a new class of assets emerged: the cryptocurrencies. The cryptocurrencies scenario has been advancing rapidly, by the emergence of new currencies, by the level of capitalization, either by the increase of investors or by its significant performance in 2017. Given the situation, cryptocurrencies keep consolidating itself every day as an investment alternative, becoming a permanent route for the financial market. Consequently, it becomes necessary to estimate risk measures for these assets. This study estimated the Beta coefficients of the largest cryptocurrencies – according to its capitalization level – in relation to theoretical indexes, in order to assist portfolios managers in pricing. Through a regression of past returns of virtual currencies on the returns of the cryptocurrencies indexes, stocks and a portfolio of currencies against the US dollar, the digital assets beta was estimated. From these analyses, it was possible to conclude that Bitcoin is significantly sensitive to cryptocurrencies indexes, the same condition for Ethereum, but with a softer correlation to the references, as well as Bitcoin itself. With respect to the other currencies, they did not express a relevant risk factor associated with cryptocurrencies indexes, due to low coefficient values. When analyzing the cryptocurrency in relation to the stock and currency indexes against the US dollar, it was noted that the coefficients were zero. Therefore, the digital currencies performance of this study, in the given timeframe, has an involuntary relation to the fluctuations of those indexes.

Since the creation of Bitcoin, the virtual currency has attracted the attention of many people and is now a household name synonymous with cryptocurrencies. Today, many thousands of different variants of cryptocurrencies exist, and more are being launched each day. The increase in popularity over the recent years has made them grow exponentially in value but at the same time also created a significant increase in energy consumption. Many of the cryptocurrencies we know today are based on Proof of Work, which is very energy intensive. There are also new and upcoming currencies based on alternative algorithms, such as Proof of Stake, which can considerably reduce the energy consumption of cryptocurrencies. However, Proof of Stake has not been proven to be as resilient and secure as Proof of Work. This study explores the future energy consumption and carbon emission of cryptocurrencies and reflects on their sustainability via exploratory scenario analysis. It includes three scenarios. Scenario 1 – Business as usual is the reference scenario. Scenario 2 – Change in the market, is based on the possibility of the market naturally switching over to PoS. Scenario 3 – under regulation is based on the possibility of a ban of PoW within the EU.  The results of this study indicate that the current emissions might be much lower than previously considered and that they might only be 30 percent of what had previously been reported. The fact that the emission is lower today does not mean that they will be sustainable in the future. Suppose Bitcoin and Ethereum energy consumption continues to grow as it has been doing for the last two years. In that case, the combined electricity consumption of the two currencies will have the possibility to surpass 650 TWh, which is an increase of over 300 percent from today's estimates. Banning Proof of Work within the EU would not yield the desired outcome of reducing carbon emissions but would instead increase carbon emissions. A continually growing Proof of Work based network, as it is used today, cannot be seen as sustainable. The recommendations to both industry and policymakers are to find and facilitate areas where Proof of Work would have the possibility to provide added value to society. / Sedan Bitcoin först lanserades har den virtuella valutan väckt mycket intresse. Många känner till Bitcoin men det finns flera tusen olika kryptovalutor och flera skapas varje dag. Det växande intresset har fått flertalet kryptovalutor att öka enormt i värde, men också i dess energiåtgång. Många av dagens kryptovalutor drivs av en algoritm som kallas Proof of Work, vilket är väldigt energikrävande. Det finns även nya och växande kryptovalutor baserade på alternativa algoritmer så som Proof of Stake, vilket har stora möjligheter att minska energiåtgången avsevärt. Dock har inte Proof of Stake bevisats vara lika motståndskraftigt vid attacker så som Proof of Work.  Denna studie utforskar den framtida energikonsumtionen och koldioxidutsläppen från kryptovalutor och har som avsikt att reflektera på hållbarheten via en utforskande scenarioanalys. Där tre scenarios utforskats. Scenario 1 – Business as usual är referensscenariot. Scenario 2 – Change in the market, är baserat på att marknaden själv glider över till PoS och Scenario 3 – under regulation vilket är baserat på ett förbud av PoW inom EU. Resultatet från studien visar att de nuvarande utsläppen kanske är mycket lägre än vad som tidigare trotts och kanske bara är 30 procent av det som tidigare rapporterats. Faktumet att kryptovalutor kanske släpper ut mindre koldioxid idag betyder inte att de kan anses hållbara i framtiden. Anta att Bicoin och Ethereum fortsätter växa som de har gjort de senaste två åren, då kommer de två valutorna ha en möjlighet att förbruka mer än 650 TWh per år vid 2025. Detta är en ökning med mer än 300 procent från dagens energikonsumtion. Att införa ett förbud på Ptoof of Work inom EU kommer dock inte ge de önskade förhoppningarna om att minska koldioxidutsläppen, utan skulle snarare kunna öka dem. Med det sagt så kan ett ständigt växande Proof of Work nätverk inte anses vara hållbart. Därför är rekommendationerna till industrin och beslutsfattarna att identifiera och främja områden där Proof of Work kan implementeras för att skapa ett mervärde till samhället.

Syfte: Studiens syfte är att analysera huruvida korruption i biståndsprocesser bättre kan hanteras via de blockkedjebaserade lösningarna kryptovalutor, smarta kontrakt och tokens, jämfört med traditionellt biståndsgivande. Vidare ämnar studien applicera resultaten från fallstudien på generella avtalsprocesser exponerade mot opportunism, för att analysera huruvida de blockkedjebaserade lösningarna bättre kan hantera transaktionskostnader jämfört med traditionella avtalslösningar. Bakgrund: Informationsasymmetrier, begränsad rationalitet och strategiskt beteende resulterar i tillitsproblematik vid avtalsprocesser, vilket ökar transaktionskostnaderna (Williamson, 1974). Biståndsprocesser är extra exponerade mot opportunistiskt beteende i form av korruption, vilket leder till att en stor del av biståndet försvinner på vägen (Transparency International, 2017). Blockkedjebaserad teknik ger möjligheten att ingå avtal utan tillit till motparten, men  lösningen är inte optimal för alla typer av avtal. Det motiverar en analys om huruvida tekniken kan hantera transaktionskostnader i biståndsprocesser bättre än traditionellt biståndsgivande. Genomförande: Studiens primärdata inhämtades via semi-strukturerade intervjuer med två svenska biståndsorganisationer och två experter inom blockkedjetekniken. Utöver intervjuerna genomfördes även en litteraturstudie och den insamlade empirin analyserades utifrån ett transaktionskostnadsperspektiv. Slutsats: Blockkedjebaserade lösningar kan hantera transaktionskostnader bättre än traditionellt biståndsgivande när Greenspans (2015) fem kriterium är uppfyllda. Kontexten, avtalets karaktär och avtalsparternas preferenser är de avgörande faktorerna huruvida kriterierna uppfylls i såväl biståndsgivande som generella avtalsprocesser. En ökad transparens, öppenhet och censurresistens måste värderas högre vid implementering än de medföljande säkerhetsriskerna. / Purpose: The purpose of this study is to analyze whether corruption in aid processes better can be managed through the blockchain-based solutions cryptocurrencies, smart contracts and tokens, in comparison to traditional donation of aid. The study also aims to apply the results from the case study to general contractual processes exposed to opportunistic behavior, to analyze whether the blockchain-based solutions better can manage the transaction costs in comparison to traditional contractual solutions. Background: Asymmetric information, bounded rationality and strategic behavior result in trust issues in contractual processes, which increases transaction costs (Williamson, 1974). Aid processes are particularly exposed to opportunistic behavior in form of corruption, which leads to aid disappearing on the way (Transparency International, 2017). Blockchain-based technology enables contractual relationships without trusting the counterpart, but is not an optimal solution for all types of contracts. This provides incentives for a further analysis whether the technology can manage transaction costs in aid processes better than traditional donation of aid. Completion: The study´s primary data was obtained through semi-structed interviews with two Swedish aid organizations and two experts in blockchain technology. A literature review was made and the empirical data was analyzed from a transaction cost perspective. Conclusion: Blockchain-based solutions can manage transaction costs better than the traditional donation of aid when Greenspan´s (2015) five criteria is met. The context, the characteristics of the contract and the preferences of the contracting parties are the decisive factors whether the criteria are met in the aid donation process and in general contractual processes. Increased transparency, openness and censor resistance must be valued higher when implementing the technology than the following security risks. / <p>Bilagor är inkluderade</p>

The Swedish central bank has in 2018 launched an investigation into what a digital e-currency in Sweden would look like. Tendermint is being investigated for a potential implementation. Tendermint is a blockchain building environment which has its own consensus-algorithm, and its own solution to the Byzantine general's problem. The most relevant part is the scalability and reliability of Tendermint. To do this we tested out the software by sending transactions between our computers, and recorded its performance in the case of one node and two nodes. After a series of simulations, we then come to the conclusion that indeed Tendermint is a suitable software for a potential e-krona.

This thesis describes basis of blockchain technology implementation for SAP Cloud platform with emphasis to security and safety of critical data which are stored in blockchain. This diploma thesis implements letter of credit to see and control business process administration. It also compares all the possible technology modification. Thesis describes all elementary parts of software which are necessary to implement while storing data and secure integrity. This thesis also leverages ideal configuration of each programable block in implementation. Alternative configurations of possible solutions are described with pros and cons as well. Another part of diploma thesis is actual working implementation as a proof of concept to cover letter of credit. All parts of code are design to be stand alone to provide working concept for possible implementation and can source as a help to write productive code. User using this concept will be able to see whole process and create new statutes for whole letter of credit business process.

<p>\textbf{Chapter 1}\\</p> <p>This paper explores the implications of the mismatch hypothesis in the context of the labor market using a survey on newly licensed US lawyers called the After the JD Study. Using a triple difference approach, I measure the impact of diversity quotas on marginal minority workers’ future salaries, promotion rates, and leaving rates for occupation and job. With middling statistical power, my findings are in line with the mismatch hypothesis in that beneficiaries of the diversity quota policy are made ex-ante worse off. My findings are also in line with recent literature on diminishing racial outcome gaps by skill.</p> <p><br></p> <p>\textbf{Chapter 2}\\</p> <p>In this paper, we provide theoretical framework for three models of Digital Media Firm behavior called \textit{Premium}, \textit{Free-to-Play}, and \textit{Play-to-Earn} as well as suggest an empirical measure of firm ponzi-likeness. First, we study a baseline model optimal price and quality of a digital product, the premium model. Second, we extend the baseline model where some customers, called minnows, receive the product for free and other customers, called whales, pay a price for a better version of the product, this is the free-to-play model. Finally, we explore a model where customers receive a security-like asset from the firm and this asset acts like a negative price while also subsidizing the firm's revenue. This final model provides an environment for much research. We show that, even when firms are ponzi-schemes in this final model, quality of the product need not be at a minimum. We also briefly discuss how one would measure the ponzi-likeness of a digital media firm in the third model setting. </p> <p><br></p> <p>\textbf{Chapter 3}\\</p> <p>In this paper, I explore unique measures of racial prejudice and their impact on black wages in the labor market using the General Social Survey, Current Population Survey and the NLSY79. I generate two variables to proxy for racial prejudice which are extracted from the GSS and the NLSY79. The first variable, drawn from the GSS, measures prejudice sentiment towards blacks and the second, drawn from the NLSY79, measures individual experience with racial discrimination. I use these measurements to proxy for racial prejudice and its impact on the black-white wage gap. I find that these variables are two distinctly different measures of racial discrimination in the labor market, providing a powerful instrument for measuring racial discrimination in the labor market. They also provide the insight that, while racial prejudice may be high in certain occupations and regions, this sentiment does not directly impact black outcomes. Specifically, wages are more sensitive to racial prejudice in WC jobs than in BC jobs. </p>

Under de senaste åren har kryptovalutor som Bitcoin blivit alltmer populära och genererat stor uppmärksamhet inom finansiella och tekniska kretsar. Trots den ökande populariteten och intresset för kryptovalutor som investeringsobjekt och spekulativt instrument, är deras användning som betalningsmedel fortfarande begränsad. Det finns flera faktorer som kan påverka användares acceptans av kryptovalutor för transaktioner, inklusive osäkerhet kring säkerhet och integritet, brist på reglering och stabilitet, och begränsad acceptans hos handlare och serviceleverantörer. Studiens syfte avser att undersöka varför kryptovaluta som betalningsmedel inte fått genomslag på den svenska marknaden, och de attityder som ligger till grund för detta. Till val av metod har triangulering använts för att samla in data genom vetenskapliga artiklar, enkäter och intervjuer. Fokus har legat på att få en förståelse för användare av kryptovalutor. Genom att engagera användarna direkt har vi haft möjlighet att lyssna på deras åsikter, erfarenheter och attityder på en djupare nivå. Genom att undersöka användares uppfattningar om att använda kryptovalutor som betalningsmedel, kan vi belysa viktiga insikter som kan forma framtida utveckling och acceptans av kryptovalutor. Samtliga data analyseras med hjälp av innehållsanalys. Resultatet som framställdes var att informationen kring kryptovalutor framstår i sin helhet som bristfällig. Genom att nå ökad förståelse kan man öka intresset och användningen av teknologin. Genom att undersöka och analysera olika aspekter av kryptovalutors acceptans och användning som betalningsmedel har vi identifierat flera viktiga faktorer som påverkar människors beslut att använda denna teknologi. Bland de främsta faktorerna är kunskap om kryptovalutor, uppfattningar om säkerhet och integritet, acceptans hos företag och serviceleverantörer, samt fördelarna med snabba och enkla transaktioner. Samtidigt har vi noterat att bristande kunskap och osäkerhet kring kryptovalutor kan vara en bromsande faktor för deras acceptans. För att öka användningen av kryptovalutor som betalningsmedel bör det finnas en kombination av utbildning, reglering, acceptans och incitament för användare. Detta kommer att underlätta för människor att fatta informerade beslut om sina ekonomiska transaktioner och främja en hållbar och säker användning av kryptovalutor. / In recent years, cryptocurrencies such as Bitcoin have become increasingly popular and generated a lot of attention in financial and technology circles. Despite the increasing popularity and interest in cryptocurrencies as an investment and speculative instrument, their use as a means of payment is still limited. There are several factors that can affect user acceptance of cryptocurrencies for transactions, including uncertainty about security and privacy, lack of regulation and stability, and limited acceptance by merchants and service providers. The purpose of the study is to investigate why cryptocurrencies haven’t break through as a payment method on the Swedish market, and what attitudes that contributes to this. We have used triangulation as a method to collect data through scientific articles, surveys, and interviews. The focus has been on gaining an understanding of cryptocurrency users. By engaging the users directly, we have had the opportunity to listen to their opinions, experiences, and attitudes on a deeper level. By examining users' perceptions of using cryptocurrencies as a means of payment, we can emphasize important insights that can shape the future development and acceptance of cryptocurrencies. All data is analyzed using content analysis. The information about cryptocurrencies appears to be deficient. By achieving increased understanding, interest and use of the technology can be increased. By researching and analyzing various aspects of cryptocurrency acceptance and use as a means of payment, we have identified several important factors that influence people's decisions to use this technology. Among the main factors are knowledge of cryptocurrencies, perceptions of security and privacy, acceptance by businesses and service providers, and the benefits of fast and easy transactions. At the same time, we have noted that lack of knowledge and uncertainty surrounding cryptocurrencies can be a slowing factor for their acceptance. To increase the use of cryptocurrencies as a means of payment, there should be a combination of education, regulation, acceptance, and incentives for users. This will facilitate people to make informed decisions about their financial transactions and promote the sustainable and safe use of cryptocurrencies.

No description available.

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.