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Multi-Asset Risk Modeling describes, in a single volume, the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. Beginning with the fundamentals of risk mathematics and quantitative risk analysis, the book moves on to discuss the laws in standard models that contributed to the 2008 financial crisis and talks about current and future banking regulation. Importantly, it also explores algorithmic trading, which
Written by two experts in quantitative finance and with considerable professional market experience, this book brings the necessary tools for modelling multi-asset equity derivatives to the practitioner and student interested in the topic.
In today’s financial market, portfolio and risk management are facing an array of challenges. This is due to increasing levels of knowledge and data that are being made available that have caused a multitude of different investment models to be explored and implemented. Professionals and researchers in this field are in need of up-to-date research that analyzes these contemporary models of practice and keeps pace with the advancements being made within financial risk modelling and portfolio control. Recent Applications
Factor models are used to describe the fundamental drivers of financial asset returns. There are 3 types: time-series factor, statistical factor and fundamental factor models. While factor models have existed for almost 60 years, industry-wide adoption with factor-based investing has surged in the last decade. This dissertation is centered on factorAnalytics, an open source R package co-developed with other UW students and faculty members, that demystifies the industry black-box models, making model fitting tools readily available for any interested academic or practitioner.
A feasible asset allocation framework for the post 2008 financial world Asset allocation has long been a cornerstone of prudent investment management; however, traditional allocation plans failed investors miserably in 2008. Asset allocation still remains an essential part of the investment arena, and through a new approach, you'll discover how to make it work. In The New Science of Asset Allocation, authors Thomas Schneeweis, Garry Crowder, and Hossein Kazemi first explore the myths that plague this field then quickly move on to
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from
This encyclopedic, detailed resource covers all the steps of one-period allocation from the foundations to the most advanced developments. It includes a large number of figures and examples as well as real trading and asset management case studies.
Valuable insights on the major methods used in today's asset andrisk management arena Risk management has moved to the forefront of asset managementsince the credit crisis. However, most coverage of this subject isoverly complicated, misunderstood, and extremely hard to apply.That's why Steven Greiner—a financial professional with overtwenty years of quantitative and modeling experience—haswritten Investment Risk and Uncertainty. With this book, heskillfully reduces the complexity of risk management methodologiesapplied across many asset classes through practical examples ofwhen to
This book is a compilation of recent articles written by leading academics and practitioners in the area of risk-based and factor investing (RBFI). The articles are intended to introduce readers to some of the latest, cutting edge research encountered by academics and professionals dealing with RBFI solutions. Together the authors detail both alternative non-return based portfolio construction techniques and investing style risk premia strategies. Each chapter deals with new methods of building strategic and tactical risk-based portfolios, constructing and combining
A practitioner's account of how investment risk affects the decisions of professional investment managers. Jargon-free, with a broad coverage of investment types and asset classes, the non-investment professional will find this book readable and accessible.
The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book
The book explains that instead of asset allocation being set in an isolated and arbitrary fashion, it is in fact the way in which specific hurdle investment returns can be targeted, and that this approach is already in use in the US (and has been for many years). It involves extended and detailed financial analysis of various asset class returns and proposes a five-asset class approach for future use. Opening with a study of the historic asset allocation practice of
Contains Nearly 100 Pages of New MaterialThe recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modelin
Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques,