This book is the culmination of the authors’ industry-academic collaboration in the past several years. The investigation is largely motivated by bank balance sheet management problems. The main difference between a bank balance sheet management problem and a typical portfolio optimization problem is that the former involves multiple risks. The related theoretical investigation leads to a significant extension of the scope of portfolio theories. The book combines practitioners’ perspectives and mathematical rigor. For example, to guide the bank managers to trade off different Pareto efficient points, the topological structure of the Pareto efficient set is carefully analyzed. Moreover, on top of computing solutions, the authors focus the investigation on the qualitative properties of those solutions and their financial meanings. These relations, such as the role of duality, are most useful in helping bank managers to communicate their decisions to the different stakeholders. Finally, bank balance sheet management problems of varying levels of complexity are discussed to illustrate how to apply the central mathematical results. Although the primary motivation and application examples in this book are focused in the area of bank balance sheet management problems, the range of applications of the general portfolio theory is much wider. As a matter of fact, most financial problems involve multiple types of risks. Thus, the book is a good reference for financial practitioners in general and students who are interested in financial applications. This book can also serve as a nice example of a case study for applied mathematicians who are interested in engaging in industry-academic collaboration.

Digital Human Modeling and Medicine: The Digital Twin explores the body of knowledge and state-of-the-art in Digital Human Modeling (DHM) and its applications in medicine. DHM is the science of representing humans with their physical properties, characteristics and behaviors in computerized, virtual models. These models can be used standalone or integrated with other computerized object design systems to both design or study designs of medical devices or medical device products and their relationship with humans. They serve as fast and cost-efficient computer-based tools for the assessment of human functional systems and human-system interaction. This book provides an industry first introductory and practitioner focused overview of human simulation tools, with detailed chapters describing body functional elements and organs, organ interactions and fields of application. Thus, DHM tools and a specific scientific/practical problem – functional study of the human body – are linked in a coherent framework. Eventually the book shows how DHM interfaces with common physical devices in medical practice, answering to a gap in literature and a common practitioner question. Case studies provide the applied knowledge for practitioners to make informed decisions. - A non-specialist level, up-to-date overview and introduction to all medically relevant DHM systems to inform trialing, procurement decisions and initial application - Includes user-level examples and case studies of DHM applications in various medical fields - Clearly structured and focused compendium that is easy to access, read and understand

Portfolio Theory and Management examines the foundations of portfolio management with the contributions of financial pioneers up to the latest trends. The book discusses portfolio theory and management both before and after the 2007-2008 financial crisis. It takes a global focus by highlighting cross-country differences and practices.

The book is an in-depth review of the theory and empirics of the demand for money and other financial assets. The different theoretical approaches to the portfolio choice problem are described, together with an up-to-date survey of the results obtained from empirical studies of asset choice behaviour. Both single-equation studies and the more complete multi-asset portfolio models, are analysed.

A modern introduction to risk and portfolio management for advanced undergraduate and beginning graduate students who will become practitioners in the field of quantitative finance, including extensive live data and Python code as online supplements which allow the application of theory to real-world situations.

Embracing finance, economics, operations research, and computers, this book applies modern techniques of analysis and computation to find combinations of securities that best meet the needs of private or institutional investors.

Academic finance has had a remarkable impact on many financial services. Yet long-term investors have received curiously little guidance from academic financial economists. Mean-variance analysis, developed almost fifty years ago, has provided a basic paradigm for portfolio choice. This approach usefully emphasizes the ability of diversification to reduce risk, but it ignores several critically important factors. Most notably, the analysis is static; it assumes that investors care only about risks to wealth one period ahead. However, many investors—-both individuals and institutions such as charitable foundations or universities—-seek to finance a stream of consumption over a long lifetime. In addition, mean-variance analysis treats financial wealth in isolation from income. Long-term investors typically receive a stream of income and use it, along with financial wealth, to support their consumption. At the theoretical level, it is well understood that the solution to a long-term portfolio choice problem can be very different from the solution to a short-term problem. Long-term investors care about intertemporal shocks to investment opportunities and labor income as well as shocks to wealth itself, and they may use financial assets to hedge their intertemporal risks. This should be important in practice because there is a great deal of empirical evidence that investment opportunities—-both interest rates and risk premia on bonds and stocks—-vary through time. Yet this insight has had little influence on investment practice because it is hard to solve for optimal portfolios in intertemporal models. This book seeks to develop the intertemporal approach into an empirical paradigm that can compete with the standard mean-variance analysis. The book shows that long-term inflation-indexed bonds are the riskless asset for long-term investors, it explains the conditions under which stocks are safer assets for long-term than for short-term investors, and it shows how labor income influences portfolio choice. These results shed new light on the rules of thumb used by financial planners. The book explains recent advances in both analytical and numerical methods, and shows how they can be used to understand the portfolio choice problems of long-term investors.

This 2001 handbook surveys the state of practice, method and understanding in the field of mathematical finance. Every chapter has been written by leading researchers and each starts by briefly surveying the existing results for a given topic, then discusses more recent results and, finally, points out open problems with an indication of what needs to be done in order to solve them. The primary audiences for the book are doctoral students, researchers and practitioners who already have some basic knowledge of mathematical finance. In sum, this is a comprehensive reference work for mathematical finance and will be indispensable to readers who need to find a quick introduction or reference to a specific topic, leading all the way to cutting edge material.

This book presents mathematical, programming and statistical tools used in the real world analysis and modeling of financial data. The tools are used to model asset returns, measure risk, and construct optimized portfolios using the open source R programming language and Microsoft Excel. The author explains how to build probability models for asset returns, to apply statistical techniques to evaluate if asset returns are normally distributed, to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and to use optimization methods to construct efficient portfolios.

An innovative approach to trading by an expert in the field. In The Leverage Space Trading Model, quantitative portfolio analysis expert Ralph Vince takes the Leverage Space Model he presented in The Handbook of Portfolio Mathematics and brings it into entirely new territory. As Vince shows here, even if a trader doesn't use margin, he or she is still using leverage. Leverage refers to the schedule upon which an asset position is increased or decreased over time as an equity account fluctuates. Traditional models do not reflect real-world actualities of cash versus the position and the schedul.