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Handbook of digital currency : bitcoin, innovation, financial instruments, and big data
Incorporating currencies, payment methods, and protocols that computers use to talk to each other, digital currencies are poised to grow in use and importance. Taking a cross-country perspective, its comprehensive view of the field includes history, technicality, IT, finance, economics, legal, tax and regulatory environment. This book discusses all major strategies and tactics associated with digital currencies, their uses, and their regulations; presents future scenarios for the growth of digital currencies; is written for regulators, crime prevention units, tax authorities, entrepreneurs, micro-financiers, micro-payment businesses, cryptography experts, software developers, venture capitalists, hedge fund managers, hardware manufacturers, credit card providers, money changers, remittance service providers, exchanges, and academics. --
eBook
C# for Financial Markets
A practice-oriented guide to using C# to design and program pricing and trading models
In this step-by-step guide to software development for financial analysts, traders, developers and quants, the authors show both novice and experienced practitioners how to develop robust and accurate pricing models and employ them in real environments. Traders will learn how to design and implement applications for curve and surface modeling, fixed income products, hedging strategies, plain and exotic option modeling, interest rate options, structured bonds, unfunded structured products, and more. A unique mix of modern software technology and quantitative finance, this book is both timely and practical. The approach is thorough and comprehensive and the authors use a combination of C# language features, design patterns, mathematics and finance to produce efficient and maintainable software.
Designed for quant developers, traders and MSc/MFE students, each chapter has numerous exercises and the book is accompanied by a dedicated companion website, www.datasimfinancial.com/forum/viewforum.php?f=196&sid=f30022095850dee48c7db5ff62192b34, providing all source code, alongside audio, support and discussion forums for readers to comment on the code and obtain new versions of the software.
eBook
Advances in financial machine learning
Learn to understand and implement the latest machine learning innovations to improve your investment performance
Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.
In the book, readers will learn how to:
* Structure big data in a way that is amenable to ML algorithms
* Conduct research with ML algorithms on big data
* Use supercomputing methods and back test their discoveries while avoiding false positives
Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.
Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
eBook
Understanding cybersecurity management in decentralized finance : challenges, strategies, and trends
This book discusses understand cybersecurity management in decentralized finance (DeFi). It commences with introducing fundamentals of DeFi and cybersecurity to readers. It emphasizes on the importance of cybersecurity for decentralized finance by illustrating recent cyber breaches, attacks, and financial losses. The book delves into understanding cyber threats and adversaries who can exploit those threats. It advances with cybersecurity threat, vulnerability, and risk management in DeFi. The book helps readers understand cyber threat landscape comprising different threat categories for that can exploit different types of vulnerabilities identified in DeFi. It puts forward prominent threat modelling strategies by focusing on attackers, assets, and software. The book includes the popular blockchains that support DeFi include Ethereum, Binance Smart Chain, Solana, Cardano, Avalanche, Polygon, among others. With so much monetary value associated with all these technologies, the perpetrators are always lured to breach security by exploiting the vulnerabilities that exist in these technologies. For simplicity and clarity, all vulnerabilities are classified into different categories: arithmetic bugs, re-Entrancy attack, race conditions, exception handling, using a weak random generator, timestamp dependency, transaction-ordering dependence and front running, vulnerable libraries, wrong initial assumptions, denial of service, flash loan attacks, and vampire Since decentralized finance infrastructures are the worst affected by cyber-attacks, it is imperative to understand various security issues in different components of DeFi infrastructures and proposes measures to secure all components of DeFi infrastructures. It brings the detailed cybersecurity policies and strategies that can be used to secure financial institutions. Finally, the book provides recommendations to secure DeFi infrastructures from cyber-attacks.
Book
Computational Finance
Intro -- Contents -- Preface -- Contributors -- Introduction -- Risk Management and Portfolio Optimization -- Importance Sampling and StratiEcation for Value-at-Risk -- ConEdence Intervals and Hypothesis Testing for the -- Sharpe and Treynor Performance Measures: -- A Bootstrap Approach -- Conditional Value at Risk -- Advances in Importance Sampling -- Arbitrage and the APTZA Note -- Bayesian Network Models of Portfolio Risk and Return -- Volatility -- Change of Measure in Monte Carlo Integration -- via Gibbs Sampling with an Application to -- Stochastic VolatilityModels -- Comparing Models of Intra daySeasonal Volatility -- in the Foreign Exchange Market -- A Symbolic Dynamics Approach to Volatility Prediction -- Does Volatility Timing Matter? -- Time Series Methods -- Goodness of FitG Stability and Data Mining -- A Bayesian Approach to Estimating Mutual Fund Returns -- Independent Component Ordering in ICS Snalysis -- of Financial Data -- Curved Gaussian Models with Spplication to Modeling -- Foreign Exchange Rates -- Nonparametric EJciency Testing of Ssian -- Foreign Exchange Markets -- Term Structure of Interactions of Foreign Exchange Rates -- Exchange Rates and Fundamentals¸ Evidence from -- Out(of(Sample Forecasting Using Neural Networks -- Dynamic Trading Strategies -- Trading Models as Specimcation Tools -- Statistical Arbitrage Models of the FTSE JDD -- Implementing Trading Strategies for Forecasting Models -- Using Nonlinear Neurogenetic Models with Prokt Related -- Objective Functions to Trade the US THbond Future -- Parameter Tuning in Trading Algorithms Using ASTA -- Hedge Funds Styles -- Optimization ofTechnical Trading Strategy Using Split -- Search Genetic Algorithms -- Trading Mutual Funds with PieceMwise Constant Models -- Minimizing Downside Risk via Stochastic -- Dynamic Programming.
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