Quantitative Finance with R and Cryptocurrencies

Download or Read eBook Quantitative Finance with R and Cryptocurrencies PDF written by Dean Fantazzini and published by Independently Published. This book was released on 2019-05-20 with total page 588 pages. Available in PDF, EPUB and Kindle.
Quantitative Finance with R and Cryptocurrencies
Author :
Publisher : Independently Published
Total Pages : 588
Release :
ISBN-10 : 1090685319
ISBN-13 : 9781090685315
Rating : 4/5 (19 Downloads)

Book Synopsis Quantitative Finance with R and Cryptocurrencies by : Dean Fantazzini

Book excerpt: The main objective of this book is to provide the necessary background to analyze cryptocurrencies markets and prices. To this end, the book consists of three parts: the first one is devoted to cryptocurrencies markets and explains how to retrieve cryptocurrencies data, how to compute liquidity measures with these data, how to calculate bounds for Bitcoin (and cryptocurrencies) fundamental value and how competing exchanges contribute to the price discovery process in the Bitcoin market. The second part is devoted to time series analysis with cryptocurrencies and presents a large set of univariate and multivariate time series models, tests for financial bubbles and explosive price behavior, as well as univariate and multivariate volatility models. The third part focuses on risk and portfolio management with cryptocurrencies and shows how to measure and backtest market risk, how to build an optimal portfolio according to several approaches, how to compute the probability of closure/bankruptcy of a crypto-exchange, and how to compute the probability of death of crypto-assets.All the proposed methods are accompanied by worked-out examples in R using the packages bitcoinFinance and bubble.This book is intended for both undergraduate and graduate students in economics, finance and statistics, financial and IT professionals, researchers and anyone interested in cryptocurrencies financial modelling. Readers are assumed to have a background in statistics and financial econometrics, as well as a working knowledge of R software.


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