Cryptocurrencies are known as unpredictable due to their highly volatility. In time series, the forecasting accuracy is strongly affected by the methodologies that are used in identifying the pattern of a nonstationary stochastic realization. The purpose of the present study is to develop an algorithm that is capable of efficiently identifying the pattern of cryptocurrencies. A brief summary of the algorithm is given. To illustrate the quality of our proposed algorithm, we study the pattern of ten different reputable cryptocurrencies and use their daily closing prices to constitute a time series. The comparison between our proposed forecasting algorithm versus the autoregressive integrated moving average (ARIMA) process will be demonstrated.
The paper discusses cryptocurrencies both in terms of their applicability to everyday financial transactions as well as in terms of criminogenic factors. The Authors will present practical ways to settle in virtual currency, institutions in which bitcoin payments can be made and the possibility of converting them into traditional currencies. Also factors conducive to combine cryptocurrencies with the criminal world, such as the anonymity of both the Internet and cryptocurrencies will be analysed. In addition, the paper presents the real impact of cryptocurrencies on the increase in crime worldwide, both related to financing of terrorism and in connection with taxation of income. The data and conclusions contained in this study are a contribution to the discussion on the sense of investing in cryptocurrencies, and above all on the opportunities and threats that entail the widespread introduction of this method of payment.
The CPS system senses and recognizes the physical world through numerous node devices and processes the collected data accordingly, then it can achieve the interconnection of the physical world and the virtual network world. However, under the existing conditions, the CPS system has not yet realized the interconnection between nodes and nodes and the physical world. Block chain technology is a distributed ledger in which nodes can freely enter and exit. This article proposes to combine the block chain with the CPS system to improve the efficiency and security of the CPS system. Through theoretical analysis and experiments, it is proved that the combination of block chain and CPS system can effectively improve the scalability of CPS system and improve the ability of CPS system to communicate with the physical world. Through the combination of the public chain and private chain of the block chain, the production speed of the block is improved, and the actual demand of the CPS system is better satisfied.
Michael D. Santonino, Constantine M. Koursaris, Michael Williams
Radio Frequency Identification, or RFID, has been gaining momentum within the aviation industry for improving efficiencies in the supply chain. RFID technology is not new, with many manufacturers outside of aviation being more responsive as early adopters to the technology. Currently, many of the full-scale implementation organizations from late adopters, have strategically integrated RFID technology into the manufacturing supply chain to tag parts and for airports/airlines to track baggage and passengers throughout their airport journey. Literature remains rather sparse in the implementation and success factors within the aviation supply chain as a number of businesses have kept much of the details discreet to differentiate themselves from the competitors. In this case study, we have examined the state of the early adopters in aviation to implement RFID technology into their supply chain for tracking parts, identifying information, logistics media, and other process improvements in component maintenance management. Airbus, who was the first in the aviation industry to adopt RFID will be examined. The paper examines the increasing numbers of airports/airlines use of RFID to track baggage and passengers with technology. Using information from published secondary data, we review the early adopters of RFID in aircraft manufacturing who are employing RFID to the improve supply chain and how airports/airlines usage of RFID has transcend to passenger tracking to improve airport operational efficiency and to increase passenger satisfaction. By identifying key trends in the aviation supply chain and the value-added in manufacturing and passenger experiences, this paper presents areas in need of further empirical research in order to understand the key success factors with RFID implementation in aviation.
The thesis consists of three chapters and studies the role of corporate bond dealers as liquidity providers in decentralized over-the-counter markets. The first two empirical chapters explore the impact of dealers' inventory financing constraints on their ability to act as middlemen in corporate bond markets. Specifically, the first chapter provides empirical evidence that dealers' financing constraints are a crucial determinant of the costs of their liquidity provision. The second chapter demonstrates that bonds handled by dealers with higher financing constraints are associated with substantially larger and abrupt price declines and slower price reversals in case of a rating downgrade from investment to non-investment grades. The third theoretical chapter studies the effects of post-trade disclosure on a dealer's dynamic trading strategy in a two-period dealership market and shows that in terms of customer welfare neither a regime with full nor one without post-trade transparency is universally dominating.
Cryptocurrencies as an investment have received increasing attention by media and international governments over the last years.However, little is known yet about the dynamics that drive these highly volatile alternative assets.This thesis studies the dynamic interdependencies between the volatility of Bitcoin, Litecoin, Ripple, Dogecoin and Feathercoin via the Dynamic Conditional Correlation model by Engle (2002) with the multivariate Student-t distribution.The main question is whether a multivariate approach improves the Value at Risk forecasting accuracy for the conditional heteroscedasticity in comparison to univariate GARCH-type models.Results show that there is a high interconnectedness between the volatility of the currencies.However, the Dynamic Conditional Correlation model can not deliver better forecasting results than the univariate GARCH-type models for the individual cryptocurrency return series. Contents List of Figures iv List of Tables vList of Tables 1 Summary Statistics for daily log returns 100 of cryptocurrencies.Log returns are calculated using: r t = 100ln(P t /P t-1 ).Returns are observed until 14 th of March 2018.Market cap is captured at 14 th of March 2018.Jarque-Bera-Test checks for deviation from normality (skewness S different from zero and kurtosis K different from 3): JB = T (S/6 + (k -3) 2 /24), is distributed as X 2 (2) with 2 degrees of freedom.Its critical value at the five-percent level is 5.99 and at the one-percent it is 9.21. . . . . . . . . . . . . . . . . . . . . . . . . 2 AIC and BIC for the estimated GARCH-type models. t is modelled via an ARMA-(1,1) process. t is modelled via a GARCH-type process of order (1,1).T=1544.Lowest AICs and BICs per group are written in bold letters. . . . . . . . . . . . . . . . . . . . . . . . . . 3 1%-and 5%-Value at Risk results for the univariate GARCH-type models.1-day-ahead rolling forecast with recursive window, model parameters refitted every 300 observations.Model is built on a training data set of 800 observations, which leaves 744 out-of-sample forecasts.% Viol: Percentage of VaR violations at = 1% and = 5%.L uc : p-value for test of unconditional coverage; L cc : p-value for test of conditional coverage.Values printed bold if p < 0.05. . . . . . . 4 Model parameters of the selected GARCH models. t is modelled via an ARMA-(1,1) process.T=1544.*** p-value < 0.001; ** pvalue < 0.01; * p-value < 0.05.Q(10): p-value of Ljung-Box test on squared standardized residuals for lag = 10; ARCH(5): p-value for weighted ARCH LM test for lag = 5. . . . . . . . . . . . . . . . . 5 Lag = 0 sample correlation matrix 0 (Pearson) of the five crypto currency log return series.T = 1554. . . . . . . . . . . . . . . . . .6 Model parameters for the estimated DCC models.T=1544, k=5, *** p-value < 0.001; ** p-value < 0.01; * p-value < 0.05.Model parameters for univariate volatility series are listed in table (4). . .7 Mean and (standard deviation) of the lag = 0 correlations in the multivariate volatility of the currencies estimated by the DCC model in equation (57).T=1544. . . . . . . . . . . .
Hallvard Kristoffer Boland Haugen, Andreas Fougner Engebretsen
The topic of revenue streams in the music industry has been frequently discussed since the transition from sales to streaming started when Spotify launched in 2008. Even though revenues in the industry have reached new heights, musicians express dissatisfaction with lower royalty payouts. Moreover, it has become increasingly more difficult to understand the royalty calculations. With today's complicated licensing agreements, money flows through a complex chain of third parties before it reaches the musicians. The industry struggles with transparency and efficiency, and the musicians are paying the price. Meanwhile, blockchain technology has developed since its first implementation with Bitcoin in 2008. Today, more advanced blockchains can run decentralized transparent applications that utilize the technology's efficient transaction system. With the industry issues and the promises of blockchain in mind, we investigate how blockchain technology can be applied to solve value chain problems within the music space.\n\nIn this thesis, we identify core issues in the music industry, propose a decentralized application (dApp) that attempts to solve these issues and implement the proposed solution. We develop the business logic using smart contracts on the Ethereum blockchain and make an associated web application using a JavaScript framework. The dApp works as a global copyrights database where musicians can register and license musical works. We exploit Ethereum's efficient transactional system to manage license purchases. Furthermore, we discuss the advantages and disadvantages of blockchain based solutions.
Cel – Celem artykułu jest prezentacja koncepcji systemu informatycznego umożliwiającego prognozowanie kursu kryptowaluty bitcoin (BTC) w odniesieniu do waluty euro. Metodologia badania – Na potrzeby realizacji tak sformułowanego celu opracowano model sztucznej sieci neuronowej – perceptronu wielowarstwowego. W ramach badań dobrano zmienne wejściowe, od których uzależniono kurs BTC. Pozyskano także odpowiednie dane, pochodzące z dziennych notowań kursów wybranych walut i metali. Dane poddano stosownej obróbce matematycznej w celu ich dostosowania do wykorzystania podczas uczenia, walidacji i testowania sztucznej sieci neuronowej. Oryginalność/wartość – Oryginalny był dobór wektora zmiennych wejściowych, umożliwiających prognozowanie kursu BTC. Wyniki przeprowadzonych eksperymentów potwierdziły wysoką skuteczność prognozowania w perspektywie jedno- i dwudniowej. Wysokie wartości współczynnika regresji (R) i mały błąd średniokwadratowy (MSE) świadczą o tym, że opracowany system predykcyjny prawidłowo przewiduje kursy analizowanej kryptowaluty nie tylko w odniesieniu do danych historycznych, lecz także dla wartości bieżących i przyszłych.
Modern insurance has been operating in the same business model since its inception while its recent IT improvement is mainly for specific functions without overall structural review. This paper is to incorporate the concept of a cryptocurrency, called Risk Coin, as the foundation of a new model to enhance the risk financing efficiency with capital market. Risk Coin is 1) a shareholding token of the premium fund with benefits from law-of-large-numbers, and 2) a cryptocurrency with benefits from seigniorage as fiat currency anchored at value of coin fund formed by collected premium and refund from loss payments. Applications in current insurance environment of pipeline model as well as in an innovative environment of platform model are simulated. Both results show a self-balancing mechanism that higher coin value from less coin preference, therefore, encouraging coin receivers to keep coins. Overall the new model will enhance the effectiveness of risk funding by keeping risk coins and also turn insurance deal to a win-win situation form zero-sum game through premium payment as gaining ownership of risk fund.
matthew baldree, paul widhalm, brandon hill, Matteo Ortisi
In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with a standard deviation of 0.54%. From our analysis, we conclude that Bitcoin is a unique asset with similarities to gold. As a young asset, it lacks economic fundamentals making it very difficult to predict. By leveraging technical momentum indicators to provide buy, sell, and hold markers or labels, a tool can be developed that performs as good or better than a buy and hold trading strategy in a bear market, bull market or both markets.
Open access
Advanced Steganography and Watermarking Techniques
Bitcoin is a phenomenon that is new and there is little information on how and why it behaves as volatile as it does. This thesis uses existing data on Bitcoin’s exchange rate to estimate a model that describes the pattern and use it in a financial risk analysis. We also aim to contribute as a foundation for further studies in this field.\nThe statistical properties of the log-return of the exchange rate are analysed and it is deemed to be iid. From the eleven distributional candidates we study is the fitted skew generalised t distribution proven to represent the data best after evaluation by criteria and statistics. The estimated VaR and ES show that the rate is volatile and that the risk from investments is still high.\nThe findings show that it is necessary to describe the exchange rate with complex and flexible distributions, and even if the data shows more stability today than earlier is it important to show caution in interpretations and evaluations on the topic.\nKeywords: Bitcoin, cryptocurrencies, statistical distributions, statistical analysis, exchange rate, modelling
Virtual machine (VM) measurements data in IaaS cloud play a crucial role in integrity evaluation and decision making. Hence, the secure storage for these data has attracted more attention recently. This paper proposes a novel approach, named Mchain, to enhance the integrity and controllability of the secure storage. Especially, to enhance the integrity, a two-layer blockchain network is introduced. In the first layer, after the production, the data packages are first verified by leveraging a correspondence between a package and a policy, and a one-to-one relation among a VM, a user, and a node. After that, we propose a consensus achievement algorithm to construct a semi-finished block on a candidate block arranged by data packages. Meanwhile, the semi-finished block is distributed to all nodes, which can provide a certain integrity. In the second-layer, tamper-resistant metadata is generated by performing PoW tasks on the semi-finished block, resulting in strong integrity. Further, to enhance the controllability, a revisable user-defined policy-based encryption method with KP-ABE is proposed. It helps to flexibly control the scope of authorized verifiers. The experimental results on six scenarios with simulated data set show that the proposed approach is appealing in integrity and controllability, and the time overhead of data storage.
This dissertation develops a set of analytical tools and conceptual frameworks to explore the socio-technical implications of transitioning to a low carbon energy future. The chapters here investigate the energy challenges in Sub-Saharan Africa and analyze power expansion pathways in Nigeria and Kenya, outline the development of a novel electricity modeling tool, and conceptualize an energy sovereignty framework to enable people-centered energy planning approaches. Chapter 2 presents an overview of Africaâs energy systems and the role renewable energy can play in supporting sustainable development in Africa, with a main focus on the challenges in Sub-Saharan Africa. I synthesize the most prominent papers in the past five years. I review the literature concerning the scale of generation expansion needed to achieve universal access in the region, the challenges of power sector finance, and the need for people-centered planning paradigms. Through an extensive literature review, I assess the capacity expansion needs of the region and highlight the policy lessons that enable private power sector investment such as transparent regulatory and procurement policies. I also present a critique of the socio-political implications of increased foreign investment in the regionâs power sector. Finally, I present several studies that explore the need for people-centered planning approaches in order to achieve more equitable energy systems for all. I argue that renewable energy presents opportunities to achieve power systems expansion in an economically, environmentally and socially sustainable manner. To do this, Sub-Saharan Africa must adapt its planning strategies to holistically address the technical, economic and socio-political challenges it faces. Chapter 3 takes a deep-dive from an overview of Sub-Saharan Africa to a focus on Nigeria. I develop a first-order capacity expansion model to analyze power expansion scenarios in Nigeria. Nigeria serves as a case of countries with significant electricity demand growth that is constrained by under-developed grid infrastructure. I illustrate how the dependence on natural gas for generation has stifled the nationâs power supply, assess the role of renewable energy in meeting the nationâs electricity demand growth, and compare the cost of its current power generation expansion pathways to cost-optimized pathways. Using the capacity expansion model, I find that Nigeriaâs current energy policy, known as Vision 30:30:30, perpetuates this heavy reliance on natural gas and significantly underestimates the role of solar energy in the future electricity mix. I also identify and assess lower cost alternative pathways which do not require any coal and nuclear generation expansion unlike the Vision 30:30:30 pathway. The results show that Nigeria will have to install at least an additional 38 GW by 2030 to keep up with grid-based demand growth alone - about eight times the current operational capacity. This chapter reveals Nigeriaâs need for an energy policy reform that reduces its dependency on natural gas, eschews coal and nuclear expansion, and harnesses its abundant solar potential using centralized and distributed renewable energy technologies.Chapter 4 outlines my development of a novel open-access electricity modeling tool known as PROGRESS (Programmable Resource Optimization for Growth in Renewable Energy and Sustainable Systems). PROGRESS enables generation expansion modeling for countries with low availability and access to power systems data. The design of sustainable electricity systems needed to fuel development in regions with low electrification rates (such as Sub-Saharan Africa) requires context-specific power system modeling. Modeling data requirements for these regions, however, can be challenging for researchers and other stakeholders to access. This chapter presents a proof-of-concept description to show how PROGRESS works and then presents preliminary results for generation capacity expansion using the case of Kenya.Chapter 5 presents what is, for me, the most critical aspect of this dissertation. I explore how transitioning to low carbon energy systems and achieving universal electricity access will require not only an extensive redesign of the existing energy infrastructure but also a rethinking of energy planning approaches. I argue that innovation in decentralized and distributed energy technology transforms people from mere consumers to prosumers by empowering them to plan for their energy autonomously. I aim to connect the rise of prosumers with long-standing social movements that call for just, fair and sustainable energy systems. I draw from a rich literature of socio-energy concepts that aim to incorporate social and human dimensions into energy planning. I focus on energy justice, energy democracy, and I introduce energy sovereignty. I synthesize how these concepts together emphasize critical considerations for energy planning: âenergy for whom, for what, and at whose costs?â I also introduce an additional consideration: âenergy by whom?â and I conceptualize its framework in relation to electricity provision. I propose that âenergy by whom?â is an essential question for re-envisioning a new energy paradigm and designing a low-carbon energy future.Overall, this dissertation contributes analytical and conceptual tools for low carbon energy systems, which together provide novel socio-technical approaches for planning towards a low carbon energy future, and urge on the paradigm shift to just and sustainable energy for all.
The latency and throughput of blockchain-based cyrptocurrencies is a major concern for their suitability as mainstream currencies and as transaction processors in general. The prevalent proof-of-work scheme, exemplified by Bitcoin, is a deliberately laborious effort: the time and energy required to mine blocks makes the blockchain virtually immutable and assists in the consensus-reaching process. Coinspermia (coin=money + spermia=seed) is a different approach: transactions are concurrently seeded throughout a network of peer nodes to an extent sufficient to achieve a high reliability of essential currency operations, including the fast transfer of coins from an owner to a recipient, and the prevention of double spending. A number of Bitcoin features are retained in Coinspermia, including transaction input-outputs and cryptographic addresses and signing, but no special proof-of-work is required to commit transactions. Instead, a client can be assured of an operation completion when a quorum of network nodes acknowledge the operation, which can occur before a transaction operation finishes propagating through the network. Simulation substantiates improved latency and throughput.
Cryptocurrency is a new trend in business sphere all over the world.It gave us opportunities to earn, to produce, to sell.But the growth of popularity of it must be connected with understanding of its functioning, understanding of possibilities it gives us.According to statistics, there are hundreds Billion dollars invested in this sphere, which makes it worth our close attention, but misunderstanding, fake news and the lack of credible information arouses distrust and creates a gap between society and this budding sphere.The aim of this work is popularization and explanation of possibilities of earning, which are created by cryptocurrencies.