W. Brent Lindquist – författare

Cryptocurrencies have transformed finance by opening new avenues for investment and innovation, while exposing portfolios to extreme volatility, fat tails, liquidity shocks, and shifting regulation. Risk Management for Cryptocurrency Portfolios provides a rigorous, practice-oriented toolkit for this landscape. The book blends postmodern portfolio theory, heavy-tailed statistics, and empirically tested optimization methods into a coherent framework tailored to digital assets.Starting from the data, the authors assemble a consistent set of 40 major tokens and examine hourly performance, stylized facts, and benchmarks. They study stationarity, the non-normal nature of returns, and tail risk using Hill estimators and generalized Pareto modeling and quantify distances between return series to guide diversification. The portfolio core begins with mean-variance analysis, the capital market line, and coherent risk measures. Building on this foundation, the book develops mean-CVaR optimization and equivalent formulations, with MATLAB implementations and step-by-step case studies.Strategy chapters compare long-only and long-short constructions, including Jacobs et al. and Lo-Patel approaches, momentum variants, and portfolios under turnover constraints. Performance is evaluated with maximum drawdown and widely used ratios such as Sharpe, Sortino-Satchell, and the Rachev ratio.The dynamic optimization introduces ARMA(1,1)-GARCH(1,1) models with Student's t-innovations, multivariate t-distributions and t-copulas, and the simulation of return scenarios. Robust optimization addresses model misspecification by treating observed return distributions as uncertain; readers learn box and ellipsoidal uncertainty sets, Kantorovich distances between discrete distributions, and robust CVaR portfolios on historical data.Validation is integral. A backtesting suite consisting of value-at-risk tests, including binomial and traffic-light procedures, plus Kupiec, Christoffersen, and Haas tests, assesses model quality and contrasts historical, dynamic, and robust allocations. Written for practitioners, analysts, researchers, and graduate students, the text is selfcontained and comprehensive. Clear exposition, empirical examples, and ready to run MATLAB code make advanced methods usable in day-to-day portfolio construction. Risk Management for Cryptocurrency Portfolios equips readers with insight and tested techniques needed to build, stress-test and refine crypto portfolios with confidence.

This book provides an investor-friendly presentation of the premises and applications of the quantitative finance models governing investment in one asset class of publicly traded stocks, specifically real estate investment trusts (REITs). The models provide highly advanced analytics for REIT investment, including:portfolio optimization using both historic and predictive return estimation;model backtesting;a complete spectrum of risk assessment and management tools with an emphasis on early warning systems, risk budgeting, estimating tail risk, and factor analysis; derivative valuation;and incorporating ESG ratings into REIT investment.These quantitative finance models are presented in a unified framework consistent with dynamic asset pricing (rational finance). Given its scope and practical orientation, this book will appeal to investors interested in portfolio optimization and innovative tools for investment risk assessment.

This book provides an investor-friendly presentation of the premises and applications of the quantitative finance models governing investment in one asset class of publicly traded stocks, specifically real estate investment trusts (REITs). The models provide highly advanced analytics for REIT investment, including:

portfolio optimization using both historic and predictive return estimation; model backtesting; a complete spectrum of risk assessment and management tools with an emphasis on early warning systems, risk budgeting, estimating tail risk, and factor analysis; derivative valuation; and incorporating ESG ratings into REIT investment.

These quantitative finance models are presented in a unified framework consistent with dynamic asset pricing (rational finance). Given its scope and practical orientation, this book will appeal to investors interested in portfolio optimization and innovative tools for investment risk assessment.