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Your Essential Guide to Quantitative Hedge Fund Investing provides a conceptual framework for understanding effective hedge fund investment strategies. The book offers a mathematically rigorous exploration of different topics, framed in an easy to digest set of examples and analogies, including stories from some legendary hedge fund investors. Readers will be guided from the historical to the cutting edge, while building a framework of understanding that encompasses it all. Features Filled with novel examples and analogies from within and beyond the world of finance Suitable for practitioners and graduate-level students with a passion for understanding the complexities that lie behind the raw mechanics of quantitative hedge fund investment A unique insight from an author with experience of both the practical and academic spheres.

Handbook of Price Impact Modeling provides practitioners and students with a mathematical framework grounded in academic references to apply price impact models to quantitative trading and portfolio management. Automated trading is now the dominant form of trading across all frequencies. Furthermore, trading algorithm rise introduces new questions professionals must answer, for instance: How do stock prices react to a trading strategy? How to scale a portfolio considering its trading costs and liquidity risk? How to measure and improve trading algorithms while avoiding biases? Price impact models answer these novel questions at the forefront of quantitative finance. Hence, practitioners and students can use this Handbook as a comprehensive, modern view of systematic trading.For financial institutions, the Handbook's framework aims to minimize the firm's price impact, measure market liquidity risk, and provide a unified, succinct view of the firm's trading activity to the C-suite via analytics and tactical research. The Handbook's focus on applications and everyday skillsets makes it an ideal textbook for a master's in finance class and students joining quantitative trading desks. Using price impact models, the reader learns how to: Build a market simulator to back test trading algorithms Implement closed-form strategies that optimize trading signals Measure liquidity risk and stress test portfolios for fire sales Analyze algorithm performance controlling for common trading biases Estimate price impact models using public trading tape Finally, the reader finds a primer on the database kdb+ and its programming language q, which are standard tools for analyzing high-frequency trading data at banks and hedge funds. Authored by a finance professional, this book is a valuable resource for quantitative researchers and traders.

There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you'll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.

Explore a variety of Excel features, functions, and productivity tips for various aspects of financial modelingKey FeaturesExplore Excel's financial functions and pivot tables with this updated second editionBuild an integrated financial model with Excel for Microsoft 365 from scratchPerform financial analysis with the help of real-world use casesBook DescriptionFinancial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Excel for Microsoft 365 explores financial modeling terminologies with the help of Excel. Starting with the key concepts of Excel, such as formulas and functions, this updated second edition will help you to learn all about referencing frameworks and other advanced components for building financial models. As you proceed, you'll explore the advantages of Power Query, learn how to prepare a 3-statement model, inspect your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. Next, you'll learn how to deal with iterations and provide graphical representations of ratios, before covering best practices for effective model testing. Later, you'll discover how to build a model to extract a statement of comprehensive income and financial position, and understand capital budgeting with the help of end-to-end case studies. By the end of this financial modeling Excel book, you'll have examined data from various use cases and have developed the skills you need to build financial models to extract the information required to make informed business decisions.What you will learnIdentify the growth drivers derived from processing historical data in ExcelUse discounted cash flow (DCF) for efficient investment analysisPrepare detailed asset and debt schedule models in ExcelCalculate profitability ratios using various profit parametersObtain and transform data using Power QueryDive into capital budgeting techniquesApply a Monte Carlo simulation to derive key assumptions for your financial modelBuild a financial model by projecting balance sheets and profit and lossWho this book is forThis book is for data professionals, analysts, traders, business owners, and students who want to develop and implement in-demand financial modeling skills in their finance, analysis, trading, and valuation work. Even if you don't have any experience in data and statistics, this book will help you get started with building financial models. Working knowledge of Excel is a prerequisite.

The original theory of capital cost and capital structure put forward by Nobel Prize Winners Modigliani and Miller has since been modified by many authors, and this book discusses some of them. The book's authors have created general theory of capital cost and capital structure – the Brusov–Filatova–Orekhova (BFO) theory, which generalizes the Modigliani–Miller theory to encompass companies of an arbitrary age (and arbitrary lifetime). Despite the availability of this more general theory, the classical Modigliani–Miller theory is still widely used in practice. In this book, the authors for the first time generalize it for cases of practical relevance: for the case of variable profit; for the case of advance tax-on-profit payments and interest on debt payments; for the case of several tax-on-profit and interest on debt payments per period; and for the combination of all three effects. These generalizations lead to valuable theoretical results as well as significantly widen of practical application this theory in practice and increase of the quality of finance management of the company. As well, the book investigates the applications of said results in corporate finance, investments, taxation and ratings, where employing a generalized Modigliani–Miller theory can be very fruitful.

This is an introduction to an investment course that focuses on basic models used in the financial industry for investment and decision making. The course begins with an overview of the investment environment in developed markets, followed by a more in-depth analysis of key investment topics. These topics include modern portfolio theory, asset pricing models, term structure of interest rates, stock and bond portfolio management and evaluation of portfolio performance. Modern finance extensively uses the concept of arbitrage, or rather the lack of it in financial markets, and the course highlights such uses in different circumstances.The course takes a hands-on approach with the aid of a software package, Maple™, the details of which will be explained during the first lecture. Consequently, most lectures will be divided between a theoretical lecture and a lab — a practical implementation of the theoretical material of the lecture. The use of the Maple™ software in this course simulates, to a certain extent, a professional environment. It allows visualizations of different concepts, minimizes tedious algebraic calculations and the use of calculus while equipping students with intuitive understanding. This is facilitated by the symbolic power of Maple™ and its excellent graphic and animation capabilities.Institutional material is surveyed very concisely, so the reader gets an appreciation of the investment'lay of the land'. It is enhanced by an eLearning unit, self-administrated quizzes as well as a stock market game, utilizing StockTrack™. StockTrack™ introduces students to trading in the real world by practicing different types of orders as well as introducing conventions common in the investment community.

Since the Global Financial Crisis, the structure of financial markets has undergone a dramatic shift. Modern markets have been “zombified” by a combination of Central Bank policy, disintermediation of commercial banks through regulation, and the growth of passive products such as ETFs. Increasingly, risk builds up beneath the surface, through a combination of excessive leverage and crowded exposure to specific asset classes and strategies. In many cases, historical volatility understates prospective risk. This book provides a practical and wide ranging framework for dealing with the credit, positioning and liquidity risk that investors face in the modern age. The authors introduce concrete techniques for adjusting traditional risk measures such as volatility during this era of unprecedented balance sheet expansion. When certain agents in the financial network behave differently or in larger scale than they have in the past, traditional portfolio theory breaks down. It can no longer account for toxic feedback effects within the network. Our feedback-based risk adjustments allow investors to size their positions sensibly in dangerous set ups, where volatility is not providing an accurate barometer of true risk. The authors have drawn from the fields of statistical physics and game theory to simplify and quantify the impact of very large agents on the distribution of forward returns, and to offer techniques for dealing with situations where markets are structurally risky yet realized volatility is low. The concepts discussed here should be of practical interest to portfolio managers, asset allocators, and risk professionals, as well as of academic interest to scholars and theorists.

Financial markets are not predictable, let alone controllable. The one thing traders and investors can control is their trading tactics, where some can have higher probability of profitability than others. This book explains, by using phase analysis, why some of the indicators, and trading tactics would work better than others, and why some indicators and trading tactics would perform poorly. Emphasis is placed on Awesome Oscillator and Accelerator Oscillator, which are based on Simple Moving Average, a popular tool employed by traders. They are then compared to Moving Average Convergence-Divergence (MACD) and MACD Histogram (MACDH), which are based on exponential moving averages. By varying the parameters of MACD and MACDH, one can change the phase or time delay, and possibly make a larger profit. This book is for practitioners, and includes all MATLAB programs used in the book.

This textbook introduces readers to the most relevant aspects of Investment Evaluation in the context of enterprise evaluation. It utilises a clear didactic concept and concisely presents representative cases, supported by calculations and their step-by-step, Excel-based solutions. In addition, the book analyses the respective benefits of the calculation models discussed from a management standpoint.

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricingInvestors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets.Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Presents mathematical models that continue to reveal essentials for understanding the relationships among the actors in the financial world: annuities, bonds, portfolio theory, and derivatives.

This book provides readers with a systematic approach to quantitative investments and bridges the gap between theory and practice, equipping students to more seamlessly enter the world of industry. A successful quantitative investment strategy requires an individual to possess a deep understanding of the financial markets, investment theories and econometric modelings, as well as the ability to program and analyze real-world data sets. In order to connect finance theories and practical industry experience, each chapter begins with a real-world finance case study. The rest of the chapter introduces fundamental insights and theories, and teaches readers to use statistical models and R programming to analyze real-world data, therefore grounding the learning process in application. Additionally, each chapter profiles significant figures in investment and quantitative studies, so that readers can more fully understand the history of the discipline. This volume willbe particularly useful to advanced students and practitioners in finance and investments.

Explore the aspects of financial modeling with the help of clear and easy-to-follow instructions and a variety of Excel features, functions, and productivity tipsKey FeaturesA non-data professional's guide to exploring Excel's financial functions and pivot tablesPrepare various models for income and cash flow statements, and balance sheetsPerform valuations and identify growth drivers with real-world case studiesBook DescriptionFinancial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Microsoft Excel 2019 explores terminologies of financial modeling with the help of Excel. This book will provides you with an overview of the steps you should follow to build an integrated financial model. You will explore the design principles, functions, and techniques of building models in a practical manner. Starting with the key concepts of Excel, such as formulas and functions, you will learn about referencing frameworks and other advanced components for building financial models. Later chapters will help you understand your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. The book takes an intuitive approach to model testing and covers best practices and practical use cases. By the end of this book, you will have examined the data from various use cases, and have the skills you need to build financial models to extract the information required to make informed business decisions.What you will learnIdentify the growth drivers derived from processing historical data in ExcelUse discounted cash flow (DCF) for efficient investment analysisBuild a financial model by projecting balance sheets and profit and lossApply a Monte Carlo simulation to derive key assumptions for your financial modelPrepare detailed asset and debt schedule models in ExcelDiscover the latest and advanced features of Excel 2019Calculate profitability ratios using various profit parametersWho this book is forThis book is for data professionals, analysts, traders, business owners, and students, who want to implement and develop a high in-demand skill of financial modeling in their finance, analysis, trading, and valuation work. This book will also help individuals that have and don't have any experience in data and stats, to get started with building financial models. The book assumes working knowledge with Excel.

El trading cuantitativo es una modalidad de inversión basada en el análisis de variables numéricas (cuantitativas) para crear estrategias de trading sistemáticas que generen beneficios de manera consistente a lo largo del tiempo. ¿Te gustaría ser capaz de gestionar tu propio capital y desarrollar tus estrategias, pero no sabes de qué manera abordarlo correctamente? ¿Estás dispuesto a dedicarle un tiempo y esfuerzo, a tener paciencia y a comprometerte con el objetivo de generar riqueza a largo plazo? Si tus respuestas son afirmativas, en este libro aprenderás cómo hacerlo de forma ordenada, coherente y sistemática, que podrás poner en práctica por tu propia cuenta en un corto período de tiempo, con ejemplos reales de estrategias y procesos diseccionados paso a paso. Verás cómo utilizar el método científico para plantear y verificar las hipótesis de inversión, aprenderás cómo realizar los análisis de robustez de las estrategias, entenderás la importancia de aplicar una gestión estricta del riesgo y desarrollarás la psicología adecuada para enfrentarte a la incertidumbre de la operativa con capital real. Con los conceptos y habilidades que adquirirás durante la lectura y las herramientas y actualizaciones que encontrarás en la web www.laguiadetrading.com, estarás en disposición de iniciar tu propio camino, ya sea con el objetivo de gestionar tu patrimonio o el de iniciar tu firma de inversión.

Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models Covers significant developments in the field, especially in multivariate models Contains completely renewed chapters with new topics and results Handles both theoretical and applied aspects Applies to researchers in different fields (time series, econometrics, finance) Includes numerous illustrations and applications to real financial series Presents a large collection of exercises with corrections Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.

An integrated guide to C++ and computational finance This complete guide to C++ and computational finance is a follow-up and major extension to Daniel J. Duffy's 2004 edition of Financial Instrument Pricing Using C++. Both C++ and computational finance have evolved and changed dramatically in the last ten years and this book documents these improvements. Duffy focuses on these developments and the advantages for the quant developer by: Delving into a detailed account of the new C++11 standard and its applicability to computational finance. Using de-facto standard libraries, such as Boost and Eigen to improve developer productivity. Developing multiparadigm software using the object-oriented, generic, and functional programming styles. Designing flexible numerical algorithms: modern numerical methods and multiparadigm design patterns. Providing a detailed explanation of the Finite Difference Methods through six chapters, including new developments such as ADE, Method of Lines (MOL), and Uncertain Volatility Models. Developing applications, from financial model to algorithmic design and code, through a coherent approach. Generating interoperability with Excel add-ins, C#, and C++/CLI. Using random number generation in C++11 and Monte Carlo simulation. Duffy adopted a spiral model approach while writing each chapter of Financial Instrument Pricing Using C++ 2e: analyse a little, design a little, and code a little. Each cycle ends with a working prototype in C++ and shows how a given algorithm or numerical method works. Additionally, each chapter contains non-trivial exercises and projects that discuss improvements and extensions to the material. This book is for designers and application developers in computational finance, and assumes the reader has some fundamental experience of C++ and derivatives pricing. HOW TO RECEIVE THE SOURCE CODE Once you have purchased a copy of the book please send an email to the author dduffyATdatasim.nl requesting your personal and non-transferable copy of the source code. Proof of purchase is needed. The subject of the mail should be “C++ Book Source Code Request”. You will receive a reply with a zip file attachment.

Investment Behaviour explores the relationship between competing demographic factors, personal awareness and perceived attitudes to risk in shaping the behaviour of individual investors in the stock market. Arup Kumar Sarkar and Tarak Nath Sahu analyse the suitability of using Behavioural Finance theories in understanding investor behaviour across developed, developing and under-developed country contexts and in all types of stock markets. Across an in-depth study, the authors examine differing variables impacting on behaviour, give an overview of the empirical and theoretical literature, and also provide an analysis of the empirical findings of their investigation. The book promotes a greater understanding the psychological foundations of human behaviour in financial markets to facilitate the formulation of more individual-centered financial policy.

The main goal of this book is to present coherent mathematical models to describe an economic growth and related economic issues. The book is a continuation of the author's previous book Mathematical Dynamics of Economic Markets, which presented mathematical models of economic forces acting on the markets. In his previous book, the author described a system of ordinary differential equations, which connected together economic forces behind the product's demand, supply and prices on the market. The author focuses on a specific aspect of how to modify the said system of ordinary differential equations, in order to describe the phenomenon of economic growth. In order to achieve clarity, the author restricted himself to economic processes arising on the markets of a single-product economy. Economic growth is presented as a result of savings and investment occurring on the markets. The market's participants withdraw part of the product from markets in the form of savings and use the withdrawn product in production in the form of an investment. The withdrawal drives the product's supply on the market down while at the same time driving the product's price up, which in turn drives the product's demand down. When an impact of the product's price increase exceeds an impact of the product's demand decrease, economic growth occurs. Contrarily, one observes an economic decline in the opposite situation. The author looks into various aspects that savings and investment exert on the market. He in particular discusses the models that examine an economic growth in situations when savings and investment were done in the form of a one-time withdrawal of the product, constant-rate withdrawal of product, constant-accelerated withdrawal of product, and exponential withdrawal of product from the market. The author further examines an impact of four economic concepts on economic growth – demand, supply, investment, and debt. He presents mathematical models exploring interconnections among these concepts and studies their mutual impacts on both economic growth and decline. He builds a mathematical model in order to verify a hypothesis that weak recovery after the financial crisis could be attributed to the decline of investments that were not compensated by the decrease of an interest rate. The author also looks into the phenomenon of economic crises and builds a few mathematical models. The models of four economic crises are considered. The first model concerns the last financial crisis where an author tried to explain how relatively small disturbances on financial markets had produced a large impact on the real economy. His conclusion is that fluctuations on connected markets amplify each other, which is known as the resonance phenomenon. The second model relates to the monetary part of Japanese economic policy known as Abenomics, where the price of Japanese bonds decreases and the yield increases. The author builds a mathematical model to investigate this phenomenon. The third model is about a secular stagnation hypothesis advanced by Lawrence Summers. The author complements his model of economic growth with the external supply of product to the market. He found that external supply provided with either constant rate or constant acceleration can cause a restricted or unrestricted economic decline, respectively. The fourth model is a model describing the four stages of the Greek economic crisis (before the Eurozone, before the Euro crisis, after the Euro crisis, and during the austerity period) and two potential recovery stages (with austere and benign economic transformations).

The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.

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 is a one-stop-shop reference for risk management practitioners involved in the validation of risk models. It is a comprehensive manual about the tools, techniques and processes to be followed, focused on all the models that are relevant in the capital requirements and supervisory review of large international banks.

Over the last two decades, risk-sensitive control has evolved into an innovative and successful framework for solving dynamically a wide range of practical investment management problems.This book shows how to use risk-sensitive investment management to manage portfolios against an investment benchmark, with constraints, and with assets and liabilities. It also addresses model implementation issues in parameter estimation and numerical methods. Most importantly, it shows how to integrate jump-diffusion processes which are crucial to model market crashes.With its emphasis on the interconnection between mathematical techniques and real-world problems, this book will be of interest to both academic researchers and money managers. Risk-sensitive investment management links stochastic control and portfolio management. Because of its distinct emphasis on integrating advanced theoretical concepts into practical dynamic investment management tools, this book stands out from the existing literature in fundamental ways. It goes beyond mainstream research in portfolio management in a traditional static setting. The theoretical developments build on contemporary research in stochastic control theory, but are informed throughout by the need to construct an effective and practical framework for dynamic portfolio management.This book fills a gap in the literature by connecting mathematical techniques with the real world of investment management. Readers seeking to solve key problems such as benchmarked asset management or asset and liability management will certainly find it useful.

In the era of Big Data our society is given the unique opportunity to understand the inner dynamics and behavior of complex socio-economic systems. Advances in the availability of very large databases, in capabilities for massive data mining, as well as progress in complex systems theory, multi-agent simulation and computational social science open the possibility of modeling phenomena never before successfully achieved. This contributed volume from the Perm Winter School address the problems of the mechanisms and statistics of the socio-economics system evolution with a focus on financial markets powered by the high-frequency data analysis. ​

By studying the ability of the Normal Tempered Stable (NTS) model to fit the statistical features of intraday data at a 5 min sampling frequency, Florian Jacobs extends the research on high frequency data as well as the appliance of tempered stable models. He examines the DAX30 returns using ARMA-GARCH NTS, ARMA-GARCH MNTS (Multivariate Normal Tempered Stable) and ARMA-FIGARCH (Fractionally Integrated GARCH) NTS. The models will be benchmarked through their goodness of fit and their VaR and AVaR, as well as in an historical Backtesting.

A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.

Filling the void between surveys of the field with relatively light mathematical content and books with a rigorous, formal approach to stochastic integration and probabilistic ideas, Stochastic Financial Models provides a sound introduction to mathematical finance. The author takes a classical applied mathematical approach, focusing on calculations

Understanding and applying complex modern financial models in real life scenarios, including the Black-Litterman model for constructing an optimal portfolio while incorporating personal views. This book provides an innovative, integrated, and methodical approach to understanding complex financial models, integrating topics usually presented separately into a comprehensive whole. The book brings together financial models and high-level mathematics, reviewing the mathematical background necessary for understanding these models organically and in context. It begins with underlying assumptions and progresses logically through increasingly complex models to operative conclusions. Readers who have mastered the material will gain the tools needed to put theory into practice and incorporate financial models into real-life investment, financial, and business scenarios.Modern finance's most bothersome shortcoming is that the two basic models for building an optimal investment portfolio, Markowitz's mean-variance model and Sharpe and Treynor's Capital Asset Pricing Model (CAPM), fall short when we try to apply them using Excel Solver. This book explores these two models in detail, and for the first time in a textbook the Black-Litterman model for building an optimal portfolio constructed from a small number of assets (developed at Goldman Sachs) is thoroughly presented. The model's integration of personal views and its application using Excel templates are demonstrated. The book also offers innovative presentations of the Modigliani–Miller model and the Consumption-Based Capital Asset Pricing Model (CCAPM). Problems at the end of each chapter invite the reader to put the models into immediate use. Fundamental Models in Financial Theory is suitable for classroom use or as a reference for finance practitioners.

Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behaviour in changing complex environments. This book provides an interdisciplinary view of current technological advances and challenges concerning the application of computational intelligence techniques to financial time-series forecasting, trading and investment. The book is divided into five parts. The first part introduces the most important computational intelligence and financial trading concepts, while also presenting the most important methodologies from these different domains. The second part is devoted to the application of traditional computational intelligence techniques to the fields of financial forecasting and trading, and the third part explores the applications of artificial neural networks in these domains. The fourth part delves into novel evolutionary-based hybrid methodologies for trading and portfolio management, while the fifth part presents the applications of advanced computational intelligence modelling techniques in financial forecasting and trading. This volume will be useful for graduate and postgraduate students of finance, computational finance, financial engineering and computer science. Practitioners, traders and financial analysts will also benefit from this book.

This book discusses market microstructure environment within the context of the global financial crisis. In the first part, the market microstructure theory is recalled and the main microstructure models and hypotheses are discussed. The second part focuses on the main effects of the financial downturn through an examination of market microstructure dynamics. In particular, the effects of market imperfections and the limitations associated with microstructure models are discussed. Finally, the new regulations and recent developments for financial markets that aim to improve the market microstructure are discussed. Well-known experts on the subject contribute to the chapters in the book. A must-read for academic researchers, students and quantitative practitioners.

Quantitative Finance: An Object-Oriented Approach in C++ provides readers with a foundation in the key methods and models of quantitative finance. Keeping the material as self-contained as possible, the author introduces computational finance with a focus on practical implementation in C++. Through an approach based on C++ classes and templates, the text highlights the basic principles common to various methods and models while the algorithmic implementation guides readers to a more thorough, hands-on understanding. By moving beyond a purely theoretical treatment to the actual implementation of the models using C++, readers greatly enhance their career opportunities in the field.The book also helps readers implement models in a trading or research environment. It presents recipes and extensible code building blocks for some of the most widespread methods in risk management and option pricing.Web ResourceThe author's website provides fully functional C++ code, including additional C++ source files and examples. Although the code is used to illustrate concepts (not as a finished software product), it nevertheless compiles, runs, and deals with full, rather than toy, problems. The website also includes a suite of practical exercises for each chapter covering a range of difficulty levels and problem complexity.

Financial Asset Pricing Theory offers a comprehensive overview of the classic and the current research in theoretical asset pricing. Asset pricing is developed around the concept of a state-price deflator which relates the price of any asset to its future (risky) dividends and thus incorporates how to adjust for both time and risk in asset valuation. The willingness of any utility-maximizing investor to shift consumption over time defines a state-price deflator which provides a link between optimal consumption and asset prices that leads to the Consumption-based Capital Asset Pricing Model (CCAPM). A simple version of the CCAPM cannot explain various stylized asset pricing facts, but these asset pricing'puzzles'can be resolved by a number of recent extensions involving habit formation, recursive utility, multiple consumption goods, and long-run consumption risks. Other valuation techniques and modelling approaches (such as factor models, term structure models, risk-neutral valuation, and option pricing models) are explained and related to state-price deflators. The book will serve as a textbook for an advanced course in theoretical financial economics in a PhD or a quantitative Master of Science program. It will also be a useful reference book for researchers and finance professionals. The presentation in the book balances formal mathematical modelling and economic intuition and understanding. Both discrete-time and continuous-time models are covered. The necessary concepts and techniques concerning stochastic processes are carefully explained in a separate chapter so that only limited previous exposure to dynamic finance models is required.

Mathematisches Modell und ökonomische Theorie korrespondieren in Inhalt und Entwicklung in vielfältiger Weise. Dynamische Kontroll­ theorie und ökonomische Entwicklung passen so zueinander, daß die Hoffnung zur Entwicklung einer geschlossenen dynamischen ökonomischen Theorie von ihnen ausgeht. Will dies eine Theorie des Unternehmens sein, muß sie die bestehenden Interdependenzen in einem simultanen Ansatz einfangen und steht dann vor dem Problem der Handhabung der zunehmenden Komplexität. Die einfachste Art, mit Komplexität umzugehen ist, sie zu leugnen. Man erweitert dann zwar die klassische statische Theorie, bleibt aber bei einem Bereich stehen und formuliert für ihn dynamische Modelle. So haben sich einerseits eine dynamische Preistheorie auf der Basis dynamischer Nachfrage- und Kostenentwicklungen entwickelt und wurde andererseits neben Instandhaltungsproblemen und Abschreibungsmodellen eine ebenfalls losgelöste dynamische Investitionstheorie entfaltet, unabhängig von Preis- und ebenfalls schon behandelter dynamischer Produktionstheorie. Hier setzt der Verfasser von der Theorieseite an und postuliert den Weg zur simultanen Behandlung von Preis- und Investitionsstrategien, und dringt damit zu größerer Komplexität vor, was natürlich auch zu entsprechenden Lösungen zwingt.

Complex dynamic processes of life and sciences generate risks that have to be taken. The need for clear and distinctive definitions of different kinds of risks, adequate methods and parsimonious models is obvious. The identification of important risk factors and the quantification of risk stemming from an interplay between many risk factors is a prerequisite for mastering the challenges of risk perception, analysis and management successfully. The increasing complexity of stochastic systems, especially in finance, have catalysed the use of advanced statistical methods for these tasks. The methodological approach to solving risk management tasks may, however, be undertaken from many different angles. A financial insti­ tution may focus on the risk created by the use of options and other derivatives in global financial processing, an auditor will try to evalu­ ate internal risk management models in detail, a mathematician may be interested in analysing the involved nonlinearities or concentrate on extreme and rare events of a complex stochastic system, whereas a statis­ tician may be interested in model and variable selection, practical im­ plementations and parsimonious modelling. An economist may think about the possible impact of risk management tools in the framework of efficient regulation of financial markets or efficient allocation of capital.

The recent global financial crisis has made the inadequacies of the scientific state of economics and finance glaringly obvious, as these disciplines gave the false reassurance that such a self-destructive phenomenon could not happen. A similar phenomenon arose in the 1930's, when the pitfalls of the dominant economic theories were sharply exposed. Since then, the same analytical framework, in its new versions, has revealed a huge number of other empirical and experimental failures. On the other hand, the founders of the currently dominant theories in economics and finance (i.e. the standard paradigm) such as Walras (1834-1910), Modigliani (1918-2003) and Miller (1923-2000) have identified mathematical contradictions within their own foundational models, the root cause of which no one has yet discovered. The standard paradigm has thus lost the reason for its existence in the light of experience, experiments and logical rigour. This book identifies the heuristic cause of these external and internal contradictions of the standard paradigm and remedies these problems by offering a new paradigm which can explain and predict observed economic behaviour, and resolve the extant behavioural, empirical and experimental puzzles. The new paradigm offers a dramatically improved understanding of economic behaviour at the micro as well as macro level of the economy within an over-arching framework comprising the real and the financial sectors. It does so in a rigorous but simple and clear way, using an axiomatic approach. It also offers policy recommendations on how the economy should be managed to avoid severe swings. It therefore is of great interest to scholars and practitioners in economics and finance.

This book presents papers on continuous-time consumption­ investment models by Suresh Sethi and various co-authors. Sir Isaac Newton said that he saw so far because he stood on the shoulders of gi­ ants. Giants upon whose shoulders Professor Sethi and colleagues stand are Robert Merton, particularly Merton's (1969, 1971, 1973) seminal papers, and Paul Samuelson, particularly Samuelson (1969). Karatzas, Lehoczky, Sethi and Shreve (1986), henceforth KLSS, re­ produced here as Chapter 2, reexamine the model proposed by Mer­ ton. KLSS use methods of modern mathematical analysis, taking care to prove the existence of integrals, check the existence and (where appro­ priate) the uniqueness of solutions to equations, etc. KLSS find that un­ der some conditions Merton's solution is correct; under others, it is not. In particular, Merton's solution for aHARA utility-of-consumption is correct for some parameter values and not for others. The problem with Merton's solution is that it sometimes violates the constraints against negative wealth and negative consumption stated in Merton (1969) and presumably applicable in Merton (1971 and 1973). This not only affects the solution at the zero-wealth, zero-consumption boundaries, but else­ where as well. Problems with Merton's solution are analyzed in Sethi and Taksar (1992), reproduced here as Chapter 3.

praise for FISCHER BLACK AND THE REVOLUTIONARY IDEA OF FINANCE'The story of Fischer Black.... is remarkable both because of the creativity of the man and because of the revolution he brought to Wall Street.... Mehrling's book is fascinating.'—FINANCIAL TIMES'A fascinating history of things we take for granted in our everyday financial lives.'—THE NEW YORK TIMES'Mehrling's book is essential reading for anyone interested in the development of modern finance or the life of an idiosyncratic creative genius.'—PUBLISHERS WEEKLY'Fischer Black was more than a vital force in the development of finance theory. He was also a character. Perry Mehrling has captured both sides of the picture: the evolution of thinking about the pricing of risk and time, as well as the thinkers, especially this fascinating eccentric, who worked it out.'—ROBERT M. SOWLO, Nobel laureate and Institute Professor of Economics, Emeritus, Massachusetts Institute of Technology'Although I worked closely with Fischer for nine years at Goldman Sachs and clearly recognized both his genius and the breadth and originality of his ideas, until I read this book, I had only the vaguest grasp of the source of his inspiration and no understanding at all of the source of his many idiosyncrasies.'—BOB LITTERMAN, Partner, Kepos Capital'Perry Mehrling has done a remarkable job of tracing the intellectual and personal development of one of the most original and complex thinkers of our generation. Fischer Black deserved it: a charming and brilliant book about a charming and brilliant man.'—ROBERT E. LUCAS JR., Nobel laureate and Professor of Economics, The University of Chicago

This book describes some models from the theory of investment which are mainly characterized by three features. Firstly, the decision-maker acts in a dynamic environment. Secondly, the distributions of the random variables are only incompletely known at the beginning of the planning process. This is termed as decision-making under conditions of uncer­ tainty. Thirdly, in large parts of the work we restrict the analysis to binary decision models. In a binary model, the decision-maker must choose one of two actions. For example, one decision means to undertake the invest­ ·ment project in a planning period, whereas the other decision prescribes to postpone the project for at least one more period. The analysis of dynamic decision models under conditions of uncertainty is not a very common approach in economics. In this framework the op­ timal decisions are only obtained by the extensive use of methods from operations research and from statistics. It is the intention to narrow some of the existing gaps in the fields of investment and portfolio analysis in this respect. This is done by combining techniques that have been devel­ oped in investment theory and portfolio selection, in stochastic dynamic programming, and in Bayesian statistics. The latter field indicates the use of Bayes'theorem for the revision of the probability distributions of the random variables over time.

Updated look at financial modeling and Monte Carlo simulation with software by Oracle Crystal Ball This revised and updated edition of the bestselling book on financial modeling provides the tools and techniques needed to perform spreadsheet simulation. It answers the essential question of why risk analysis is vital to the decision-making process, for any problem posed in finance and investment. This reliable resource reviews the basics and covers how to define and refine probability distributions in financial modeling, and explores the concepts driving the simulation modeling process. It also discusses simulation controls and analysis of simulation results. The second edition of Financial Modeling with Crystal Ball and Excel contains instructions, theory, and practical example models to help apply risk analysis to such areas as derivative pricing, cost estimation, portfolio allocation and optimization, credit risk, and cash flow analysis. It includes the resources needed to develop essential skills in the areas of valuation, pricing, hedging, trading, risk management, project evaluation, credit risk, and portfolio management. Offers an updated edition of the bestselling book covering the newest version of Oracle Crystal Ball Contains valuable insights on Monte Carlo simulation—an essential skill applied by many corporate finance and investment professionals Written by John Charnes, the former finance department chair at the University of Kansas and senior vice president of global portfolio strategies at Bank of America, who is currently President and Chief Data Scientist at Syntelli Solutions, Inc. Risk Analytics and Predictive Intelligence Division (Syntelli RAPID) Engaging and informative, this book is a vital resource designed to help you become more adept at financial modeling and simulation.

The modern field of asset pricing asks for sound pricing models grounded on the theory of financial economies a la Ingersoll (1987) as weIl as for accu­ rate estimation techniques a la Hamilton (1994b) when it comes to empirical inferences of the specified model. The idea behind this book on hand is to provide the reader with a canonical framework that shows how to bridge the gap between the continuous-time pricing practice in financial engineering and the capital market data inevitably only available at discrete time intervals. Three major financial markets are to be examined for which we select the equity market, the bond market, and the electricity market. In each mar­ ket we derive new valuation models to price selected financial instruments in continuous-time. The decision criterium for choosing a continuous-time model­ ing framework is the richness of the stochastic theory available for continuous­ time processes with Merton's pioneering contributions to financial economics, collected in Merton (1992). The continuous-time framework, reviewed and as­ sessed by Sundaresan (2000), allows us to obtain analytical pricing formulae that would be unavailable in a discrete time setting. However, at the time of implementing the derived theoretical pricing models on market data, that is necessarily sampled at discrete time intervals, we work with so-called exact discrete time equivalents a la Bergstrom (1984). We show how to conveniently work within astate space framework which we derive in a general setting as weIl as explicitly for each of the three applications.

Everything you need to get a grip on the complex world of derivatives Written by the internationally respected academic/finance professional author team of Sebastien Bossu and Philipe Henrotte, An Introduction to Equity Derivatives is the fully updated and expanded second edition of the popular Finance and Derivatives. It covers all of the fundamentals of quantitative finance clearly and concisely without going into unnecessary technical detail. Designed for both new practitioners and students, it requires no prior background in finance and features twelve chapters of gradually increasing difficulty, beginning with basic principles of interest rate and discounting, and ending with advanced concepts in derivatives, volatility trading, and exotic products. Each chapter includes numerous illustrations and exercises accompanied by the relevant financial theory. Topics covered include present value, arbitrage pricing, portfolio theory, derivates pricing, delta-hedging, the Black-Scholes model, and more. An excellent resource for finance professionals and investors looking to acquire an understanding of financial derivatives theory and practice Completely revised and updated with new chapters, including coverage of cutting-edge concepts in volatility trading and exotic products An accompanying website is available which contains additional resources including powerpoint slides and spreadsheets. Visit www.introeqd.com for details.

A revolutionary new alternative-investment approach that protects investors from another meltdown High-net-worth investors are allocating about 20% of assets into alternative investment strategies—up from 3% in 2000 Walker was named one of Barron's 2009 Top 1,000 Advisers and one of the Top 25 Wealth Advisers by the Philadelphia Business Journal He worked for Alex.Brown, which took Starbucks and Microsoft public

This volume provides the definitive treatment of fortune's formula or the Kelly capital growth criterion as it is often called. The strategy is to maximize long run wealth of the investor by maximizing the period by period expected utility of wealth with a logarithmic utility function. Mathematical theorems show that only the log utility function maximizes asymptotic long run wealth and minimizes the expected time to arbitrary large goals. In general, the strategy is risky in the short term but as the number of bets increase, the Kelly bettor's wealth tends to be much larger than those with essentially different strategies. So most of the time, the Kelly bettor will have much more wealth than these other bettors but the Kelly strategy can lead to considerable losses a small percent of the time. There are ways to reduce this risk at the cost of lower expected final wealth using fractional Kelly strategies that blend the Kelly suggested wager with cash. The various classic reprinted papers and the new ones written specifically for this volume cover various aspects of the theory and practice of dynamic investing. Good and bad properties are discussed, as are fixed-mix and volatility induced growth strategies. The relationships with utility theory and the use of these ideas by great investors are featured.Contents:The Early Ideas and Contributions:Introduction to the Early Ideas and ContributionsExposition of a New Theory on the Measurement of Risk (translated by Louise Sommer) (D Bernoulli)A New Interpretation of Information Rate (J R Kelly, Jr)Criteria for Choice among Risky Ventures (H A Latané)Optimal Gambling Systems for Favorable Games (L Breiman)Optimal Gambling Systems for Favorable Games (E O Thorp)Portfolio Choice and the Kelly Criterion (E O Thorp)Optimal Investment and Consumption Strategies under Risk for a Class of Utility Functions (N H Hakansson)On Optimal Myopic Portfolio Policies, with and without Serial Correlation of Yields (N H Hakansson)Evidence on the “Growth-Optimum-Model” (R Roll)Classic Papers and Theories:Introduction to the Classic Papers and TheoriesCompetitive Optimality of Logarithmic Investment (R M Bell and T M Cover)A Bound on the Financial Value of Information (A R Barron and T M Cover)Asymptotic Optimality and Asymptotic Equipartition Properties of Log-Optimum Investment (P H Algoet and T M Cover)Universal Portfolios (T M Cover)The Cost of Achieving the Best Portfolio in Hindsight (E Ordentlich and T M Cover)Optimal Strategies for Repeated Games (M Finkelstein and R Whitley)The Effect of Errors in Means, Variances and Co-Variances on Optimal Portfolio Choice (V K Chopra and W T Ziemba)Time to Wealth Goals in Capital Accumulation (L C MacLean, W T Ziemba, and Y Li)Survival and Evolutionary Stability of Rule the Kelly (I V Evstigneev, T Hens, and K R Schenk-Hoppé)Application of the Kelly Criterion to Ornstein-Uhlenbeck Processes (Y Lv and B K Meister)The Relationship of Kelly Optimization to Asset Allocation:Introduction to the Relationship of Kelly Optimization to Asset AllocationSurvival and Growth with a Liability: Optimal Portfolio Strategies in Continuous Time (S Browne)Growth versus Security in Dynamic Investment Analysis (L C MacLean, W T Ziemba, and G Blazenko)Capital Growth with Security (L C MacLean, R Sanegre, Y Zhao, and W T Ziemba)

A guide to modeling analyses for financial and sports gambling markets, with a focus on major current events Addressing the highly competitive and risky environments of current-day financial and sports gambling markets, Forecasting in Financial and Sports Gambling Markets details the dynamic process of constructing effective forecasting rules based on both graphical patterns and adaptive drift modeling (ADM) of cointegrated time series. The book uniquely identifies periods of inefficiency that these markets oscillate through and develops profitable forecasting models that capitalize on irrational behavior exhibited during these periods. Providing valuable insights based on the author's firsthand experience, this book utilizes simple, yet unique, candlestick charts to identify optimal time periods in financial markets and optimal games in sports gambling markets for which forecasting models are likely to provide profitable trading and wagering outcomes. Featuring detailed examples that utilize actual data, the book addresses various topics that promote financial and mathematical literacy, including: Higher order ARMA processes in financial markets The effects of gambling shocks in sports gambling markets Cointegrated time series with model drift Modeling volatility Throughout the book, interesting real-world applications are presented, and numerous graphical procedures illustrate favorable trading and betting opportunities, which are accompanied by mathematical developments in adaptive model forecasting and risk assessment. A related web site features updated reviews in sports and financial forecasting and various links on the topic. Forecasting in Financial and Sports Gambling Markets is an excellent book for courses on financial economics and time series analysis at the upper-undergraduate and graduate levels. The book is also a valuable reference for researchers and practitioners working in the areas of retail markets, quant funds, hedge funds, and time series. Also, anyone with a general interest in learning about how to profit from the financial and sports gambling markets will find this book to be a valuable resource.

This book extends the KMG framework (Keynes, Meltzer, Goodwin) and focuses on financial issues. It integrates Tobin's macroeconomic portfolio approach and emphasizes the issue of stock-flow consistency.

Mit der Finanzkrise ist auch die klassische ökonomische Theoriebildung in eine Krise geraten: In effizienten Märkten sollte es eigentlich keine Asset-Blasen und Börsencrashs geben. Als interdisziplinärer Ansatz der Wirtschaftswissenschaften und der Physik besteht der Anspruch der Ökonophysik darin, Methoden aus der statistischen Physik auf das komplexe System des Finanzmarktes anzuwenden und damit ökonomische Phänomene qualitativ und quantitativ zu modellieren. Tobias Preis gibt einen fundierten Einblick in das noch junge interdisziplinäre Forschungsgebiet und entwickelt ein Orderbuchmodell zur Finanzmarktsimulation auf der Grundlage von Multiagentensystemen. Mit diesem mikroskopischen Modell lassen sich viele empirische Eigenschaften von realen Börsendaten reproduzieren.

Portfolio Construction and Risk Budgeting reflects how things have changed since the financial crisis. Containing the basics of portfolio theory with cutting-edge investment management research, Bernd Scherer provides a critical review of a range of portfolio management techniques highlighting strengths, weaknesses and how to implement quantitatively-driven portfolio construction.