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Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.

\"This book analyses and discusses bonds and bond portfolios. Different yields and duration measures are investigated. The transition from a single bond to a bond portfolio leads to the equation for the internal rate of return. Its solution is analyzed and compared to different approaches proposed in the financial industry. The impact of different yield scenarios on a model bond portfolio is illustrated. Market and credit risk are introduced as independent sources of risk. Different concepts for assessing credit markets are described. Lastly, an overview of the benchmark industry is offered and an introduction to convertible bonds is given. This book is a valuable resource not only for students and researchers but also for professionals in the financial industry\"-- Publisher's description.

New edition of book that demystifies quant and algo trading In this updated edition of his bestselling book, Rishi K Narang offers in a straightforward, nontechnical style-supplemented by real-world examples and informative anecdotes-a reliable resource takes you on a detailed tour through the black box. He skillfully sheds light upon the work that quants do, lifting the veil of mystery around quantitative trading and allowing anyone interested in doing so to understand quants and their strategies. This new edition includes information on High Frequency Trading. Offers an update on the bestselling book for explaining in non-mathematical terms what quant and algo trading are and how they work Provides key information for investors to evaluate the best hedge fund investments Explains how quant strategies fit into a portfolio, why they are valuable, and how to evaluate a quant manager This new edition of Inside the Black Box explains quant investing without the jargon and goes a long way toward educating investment professionals.

\"The book will be most helpful for readers who are interested in learning about the quantitative side of equity portfolio management, mainly portfolio optimization and risk analysis. Mean-variance portfolio optimization is covered in detail, leading to an extensive discussion on robust portfolio optimization. Nonetheless, readers without prior knowledge of portfolio management or mathematical modeling should be able to follow the presentation since basic concepts are covered in each chapter. Furthermore, the main quantitative approaches are presented with MATLAB examples, allowing readers to easily implement portfolio problems in MATLAB or similar modeling software. There is an online appendix that provides the MATLAB codes presented in the chapter boxes (www.wiley.com/go/robustequitypm)\"--

With about $450 billion in assets, funds of hedge funds are the most recent darling of investors. While hedge funds carry high risk for the promise of high returns they are designed for the very rich and for large institutional investors such as pension funds. A Fund of Hedge Funds (FOF) spreads investments among a number of hedge funds to reduce risk and provide diversification, while maintaining the potential for higher than average returns. Odds are that some pension fund of yours is invested heavily in these products, and more recently these FOFs have been opened to more and more individual investors in offshore jurisdictions with lower minimum entry levels. Since this is a new and extremely fast-moving financial phenomenon, academic research has just begun in earnest, and this is the first book to present rigorous academic research by some of the leading lights in academic finance, carefully analyzing the broad array of issues involved in FOFs. * With over $450 billion in assets, hedge funds of funds are the darling of investors* First book to present rigorous academic research about funds of funds * Leading lights in academic finance from around the world analyze the broad array of issues involved in funds of funds

This book provides a cutting edge introduction to market risk management for Hedge Funds, Hedge Funds of Funds, and the numerous new indices and clones launching coming to market on a near daily basis. It will present the fundamentals of quantitative risk measures by analysing the range of Value-at-Risk (VaR) models used today, addressing the robustness of each model, and looking at new risk measures available to more effectively manage risk in a hedge fund portfolio.The book begins by analysing the current state of the hedge fund industry - at the ongoing institutionalisation of the market, and at its latest developments. It then moves on to examine the range of risks, risk controls, and risk management strategies currently employed by practitioners, and focuses on particular risks embedded in the more classic investment strategies such as Long/Short, Convertible Arbitrage, Fixed Income Arbitrage, Short selling and risk arbitrage. Addressed along side these are other risks common to hedge funds, including liquidity risk, leverage risk and counterparty risk.The book then moves on to examine more closely two models which provide the underpinning for market risk management in investment today - Style Value-at-Risk and Implicit Value-at-Risk. As well as full quantitative analysis and backtesting of each methodology, the authors go on to propose a new style model for style and implicit Var, complete with analysis, real life examples and backtesting. The authors then go on to discuss annualisation issues and risk return before moving on to propose a new model based on the authors own Best Choice Implicit VaR approach, incorporating quantitative analysis, market results and backtesting and also its potential for new hedge fund clone products.This book is the only guide to VaR for Hedge Funds and will prove to be an invaluable resource as we embark into an era of increasing volatility and uncertainty.

In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail to meet practical investment goals of marketability, usability, and performance, prompting many investors to seek simpler alternatives. Financial experts Richard and Robert Michaud demonstrate that the limitations of MV optimization are not the result of conceptual flaws in Markowitz theory but unrealistic representation of investment information. What is missing is a realistic treatment of estimation error in the optimization and rebalancing process. The text provides a non-technical review of classical Markowitz optimization and traditional objections. The authors demonstrate that in practice the single most important limitation of MV optimization is oversensitivity to estimation error. Portfolio optimization requires a modern statistical perspective. Efficient Asset Management, Second Edition uses Monte Carlo resampling to address information uncertainty and define Resampled Efficiency(TM) (RE) technology. RE optimized portfolios represent a new definition of portfolio optimality that is more investment intuitive, robust, and provably investment effective. RE rebalancing provides the first rigorous portfolio trading, monitoring, and asset importance rules, avoiding widespread ad hoc methods in current practice. The Second Edition resolves several open issues and misunderstandings that have emerged since the original edition. The new edition includes new proofs of effectiveness, substantial revisions of statistical estimation, extensive discussion of long-short optimization, and new tools for dealing with estimation error in applications and enhancing computational efficiency. RE optimization is shown to be a Bayesian-based generalization and enhancement of Markowitz’s solution. RE technology corrects many current practices that may adversely impact the investment value of trillions of dollars under current asset management. RE optimization technology may also be useful in other financial optimizations and more generally in multivariate estimation contexts of information uncertainty with Bayesian linear constraints. Michaud and Michaud’s new book includes numerous additional proposals to enhance investment value including Stein and Bayesian methods for improved input estimation, the use of portfolio priors, and an economic perspective for asset-liability optimization. Applications include investment policy, asset allocation, and equity portfolio optimization. A final chapter includes practical advice for avoiding simple portfolio design errors. A simple global asset allocation problem illustrates portfolio optimization techniques. The presentation is intuitive, rigorous and informed with institutional management experience to appeal to investment management executives, consultants, fund trustees, brokers, academics, and anyone seeking to stay abreast of the future of investment technology. With its important implications for investment practice, Efficient Asset Management’s highly intuitive yet rigorous approach to defining optimal portfolios will appeal to investment management executives, consultants, brokers, and anyone seeking to stay abreast of current investment technology. Through practical examples and illustrations, Michaud and Michaud update the practice of optimization for modern investment management.

Turn insight into profit with guru guidance toward successful algorithmic trading A Guide to Creating a Successful Algorithmic Trading Strategy provides the latest strategies from an industry guru to show you how to build your own system from the ground up. If you're looking to develop a successful career in algorithmic trading, this book has you covered from idea to execution as you learn to develop a trader's insight and turn it into profitable strategy. You'll discover your trading personality and use it as a jumping-off point to create the ideal algo system that works the way you work, so you can achieve your goals faster. Coverage includes learning to recognize opportunities and identify a sound premise, and detailed discussion on seasonal patterns, interest rate-based trends, volatility, weekly and monthly patterns, the 3-day cycle, and much more—with an emphasis on trading as the best teacher. By actually making trades, you concentrate your attention on the market, absorb the effects on your money, and quickly resolve problems that impact profits. Algorithmic trading began as a \"ridiculous\" concept in the 1970s, then became an \"unfair advantage\" as it evolved into the lynchpin of a successful trading strategy. This book gives you the background you need to effectively reap the benefits of this important trading method. * Navigate confusing markets * Find the right trades and make them * Build a successful algo trading system * Turn insights into profitable strategies Algorithmic trading strategies are everywhere, but they're not all equally valuable. It's far too easy to fall for something that worked brilliantly in the past, but with little hope of working in the future. A Guide to Creating a Successful Algorithmic Trading Strategy shows you how to choose the best, leave the rest, and make more money from your trades.