Regime Changes In Bitcoin Garch Volatility Dynamics

We test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log-returns using Markov-switching GARCH (MSGARCH) models. We also compare MSGARCH to traditional single-regime GARCH specifications in predicting one-day ahead Value-at-Risk (VaR). The Bayesian approach is used to estimate the model parameters and to compute the VaR forecasts. We find strong evidence of regime changes in the GARCH process and show that MSGARCH models outperform single-regime specifications when predicting the VaR.

The cryptocurrency market has attracted significant attention from all around the world, especially on Bitcoin (BTC), due to its intractable behaviour and unpredicted price movements. The unpredictability and high volatility of BTC are always the central issues of many research studies (Williams, 2014; Dwyer, 2015). Particularly, Chaim and Laurini (2018) had recorded two high volatility periods of BTC which are from late 2013 to early 2014 as well as in December 2017. More specifically, Corbet et al. (2019) and Panagiotidis et al. (2019) offered a more comprehensive and systematic review of the cryptocurrency market on major academic research.It has been found that the large volatility of BTC was mainly due to the presence of great swings observed in the returns which also displays characteristic of regime changes (Bariviera et al., 2017). In the similar context, structural breaks have been detected in BTC return series showing the change in the structure of return series over the periods (Mensi et al., 2018; Thies and Mólnar, 2018; Bouri et al., 2019a; Tan et al., 2019). With that in mind, instead of focusing on the single- regime generalised autoregressive conditional heteroscedasticity (GARCH)-type model in the study of BTC volatility dynamics (see e.g., Dyhrberg, 2016; Chu et al., 2017; Katsiampa, 2017 and Lahmiri et al., 2018), we consider the Markov-switching GARCH (MSGARCH) framework in this study.

This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.

This book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.

This book is a collection of selected papers presented at the Third Congress on Intelligent Systems (CIS 2022), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 5–6, 2022. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as the Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber-physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision-making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.

Alternative assets such as fine art, wine, or diamonds have become popular investment vehicles in the aftermath of the global financial crisis. Correlation with classical financial markets is typically low, such that diversification benefits arise for portfolio allocation and risk management. Cryptocurrencies share many alternative asset features, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This collection of papers addresses alternative assets and cryptocurrencies from economic, financial, statistical, and technical points of view. It gives an overview of their current state and explores their properties and prospects using innovative approaches and methodologies.

The digital revolution is impacting not only organizations but all areas of society. No sector of activity has been spared, and the survival of many organizations depends on their ability to transform and reinvent themselves in a new digital paradigm that is still very uncertain and relatively anxiety-provoking. Before tackling technological issues, it is important to ask the right questions and to acquire a minimum of digital culture that will enable the implementation of a transformation strategy and the use of appropriate technological tools. COVID-19’s Impact on the Cryptocurrency Market and the Digital Economy proposes a dive into the digital ecosystem through a historical, sociological, political, and economic approach that supplies readers with a foundation they can build their future digital skills on. Covering topics such as cryptocurrency and economic resiliency, it is ideal for industry professionals, researchers, practitioners, scholars, academicians, and students.

This book analyzes the emerging asset class of digital assets. When a new asset class originates, researchers try to understand some basic questions: Can digital assets, with the flagship asset bitcoin, really be considered a serious asset class? Since it is possible to trade digital assets, does it make sense to trade or to invest in these assets? How do digital assets compare to traditional asset classes like equities or bonds? After describing basic financial theory and breaking down the digital asset universe, this book provides fundamental knowledge with respect to this young and rising asset class. It focuses on special issues like the application of technical indicators, investment styles, asset pricing and portfolio construction. Furthermore, it offers remarks and links to other traditional asset classes and describes and warns of data issues in digital asset data.

This book constitutes revised papers from the nine workshops and one accompanying event which took place at the 22nd International Conference on Business Information Systems, BIS 2019, held in Seville, Spain, in June 2019. There was a total of 139 submissions to all workshops of which 57 papers were accepted for publication. The workshops included in this volume are: AKTB 2019: 11th Workshop on Applications of Knowledge-Based Technologies in Business BITA 2019: 10th Workshop on Business and IT Alignment BSCT 2019: Second Workshop on Blockchain and Smart Contract Technologies DigEX 2019: First International Workshop on transforming the Digital Customer Experience iCRM 2019: 4th International Workshop on Intelligent Data Analysis in Integrated Social CRM iDEATE 2019: 4th Workshop on Big Data and Business Analytics Ecosystems ISMAD 2019: Workshop on Information Systems and Applications in Maritime Domain QOD 2019: Second Workshop on Quality of Open Data SciBOWater 2019: Second Workshop on Scientific Challenges and Business Opportunities in Water Management

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