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Author: Paul Darbyshire
Publisher: John Wiley & Sons
ISBN: 1119967376
Category : Business & Economics
Languages : en
Pages : 215
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Book Description
The second book in Darbyshire and Hampton’s Hedge Fund Modelling and Analysis series, Hedge Fund Modelling and Analysis Using MATLAB® takes advantage of the huge library of built-in functions and suite of financial and analytic packages available to MATLAB®. This allows for a more detailed analysis of some of the more computationally intensive and advanced topics, such as hedge fund classification, performance measurement and mean-variance optimisation. Darbyshire and Hampton’s first book in the series, Hedge Fund Modelling and Analysis Using Excel & and VBA, is seen as a valuable supplementary text to this book. Starting with an overview of the hedge fund industry the book then looks at a variety of commercially available hedge fund data sources. After covering key statistical techniques and methods, the book discusses mean-variance optimisation, hedge fund classification and performance with an emphasis on risk-adjusted return metrics. Finally, common hedge fund market risk management techniques, such as traditional Value-at-Risk methods, modified extensions and expected shortfall are covered. The book’s dedicated website, www.darbyshirehampton.com provides free downloads of all the data and MATLAB® source code, as well as other useful resources. Hedge Fund Modelling and Analysis Using MATLAB® serves as a definitive introductory guide to hedge fund modelling and analysis and will provide investors, industry practitioners and students alike with a useful range of tools and techniques for analysing and estimating alpha and beta sources of return, performing manager ranking and market risk management.
Author: Paul Darbyshire
Publisher: John Wiley & Sons
ISBN: 1119967376
Category : Business & Economics
Languages : en
Pages : 215
Get Book Here
Book Description
The second book in Darbyshire and Hampton’s Hedge Fund Modelling and Analysis series, Hedge Fund Modelling and Analysis Using MATLAB® takes advantage of the huge library of built-in functions and suite of financial and analytic packages available to MATLAB®. This allows for a more detailed analysis of some of the more computationally intensive and advanced topics, such as hedge fund classification, performance measurement and mean-variance optimisation. Darbyshire and Hampton’s first book in the series, Hedge Fund Modelling and Analysis Using Excel & and VBA, is seen as a valuable supplementary text to this book. Starting with an overview of the hedge fund industry the book then looks at a variety of commercially available hedge fund data sources. After covering key statistical techniques and methods, the book discusses mean-variance optimisation, hedge fund classification and performance with an emphasis on risk-adjusted return metrics. Finally, common hedge fund market risk management techniques, such as traditional Value-at-Risk methods, modified extensions and expected shortfall are covered. The book’s dedicated website, www.darbyshirehampton.com provides free downloads of all the data and MATLAB® source code, as well as other useful resources. Hedge Fund Modelling and Analysis Using MATLAB® serves as a definitive introductory guide to hedge fund modelling and analysis and will provide investors, industry practitioners and students alike with a useful range of tools and techniques for analysing and estimating alpha and beta sources of return, performing manager ranking and market risk management.
Author: Paul Darbyshire
Publisher: Wiley
ISBN: 9781118879542
Category : Business & Economics
Languages : en
Pages : 288
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Book Description
Use powerful C# algorithms and Object Oriented Programming (OOP) to aid in hedge fund decision making Hedge fund managers cannot afford to ignore their risk/return profiles, and taking advantage of new technologies is an excellent way to minimize risk and capitalize on various investment styles. As Hedge Fund Analysis and Modeling Using C# demonstrates, the C# programming language is perfectly suited to hedge fund analysis. This book serves as a complete course in hedge fund modeling and provides a primer on C# and Object Oritented Programming (OOP) that will allow you to manage risk easily and make the most of key statistics. Covering both basic and risk-adjusted performance measures, Hedge Fund Analysis and Modeling Using C# moves from simple to sophisticated analysis techniques, using worked examples to show you exactly how to manage return in an era of volatility and financial risk. You'll have access to: Complete guidance on using C# and Objected Oriented Programming (OOP) for analysis using non-normal returns data and other key statistics Bonus content on a companion website containing C# programs, algorithms, and data available for download Real world modeling exercises that demonstrate the identification of risk and return factors Complete guidance for optimizing hedge fund decisions using quantitative strategies This is the only book on the market that guides you through using C# to model hedge fund risks and returns. Along with its companion titles on Excel/VBA analysis and MATLAB analysis, Hedge Fund Analysis and Modeling Using C# contributes important guidance for hedge fund managers who want to take advantage of technological platforms for optimal fund performance.
Author: Paul Darbyshire
Publisher: John Wiley & Sons
ISBN: 111994564X
Category : Business & Economics
Languages : en
Pages : 294
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Book Description
Co-authored by two respected authorities on hedge funds and asset management, this implementation-oriented guide shows you how to employ a range of the most commonly used analysis tools and techniques both in industry and academia, for understanding, identifying and managing risk as well as for quantifying return factors across several key investment strategies. The book is also suitable for use as a core textbook for specialised graduate level courses in hedge funds and alternative investments. The book provides hands-on coverage of the visual and theoretical methods for measuring and modelling hedge fund performance with an emphasis on risk-adjusted performance metrics and techniques. A range of sophisticated risk analysis models and risk management strategies are also described in detail. Throughout, coverage is supplemented with helpful skill building exercises and worked examples in Excel and VBA. The book's dedicated website, www.darbyshirehampton.com provides Excel spreadsheets and VBA source code which can be freely downloaded and also features links to other relevant and useful resources. A comprehensive course in hedge fund modelling and analysis, this book arms you with the knowledge and tools required to effectively manage your risks and to optimise the return profile of your investment style.
Author: Greg N. Gregoriou
Publisher: John Wiley & Sons
ISBN: 0471730041
Category : Business & Economics
Languages : en
Pages : 178
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Book Description
Introducing Data Envelopment Analysis (DEA) -- a quantitative approach to assess the performance of hedge funds, funds of hedge funds, and commmodity trading advisors. Steep yourself in this approach with this important new book by Greg Gregoriou and Joe Zhu. "This book steps beyond the traditional trade-off between single variables for risk and return in the determination of investment portfolios. For the first time, a comprehensive procedure is presented to compose portfolios using multiple measures of risk and return simultaneously. This approach represents a watershed in portfolio construction techniques and is especially useful for hedge fund and CTA offerings." -- Richard E. Oberuc, CEO, Burlington Hall Asset Management, Inc. Chairman, Foundation for Managed Derivatives Research Order your copy today!
Author: Francois Duc
Publisher: John Wiley & Sons
ISBN: 0470740795
Category : Business & Economics
Languages : en
Pages : 262
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Book Description
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.
Author: Paul Darbyshire
Publisher: John Wiley & Sons
ISBN: 1118879562
Category : Business & Economics
Languages : en
Pages : 363
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Book Description
Use powerful C++ algorithms and Object Oriented Programming (OOP) to aid in hedge fund decision making Low interest rates, overcrowded markets and greater regulatory oversight are just some of the many reasons it is close to impossible for hedge funds to draw competitive returns. The solution for many hedge fund managers, quantitative investment analysts and risk managers is to adopt new technologies, platforms and programming languages to better manage their risks and maximise the benefits of their return profiles. Hedge Fund Modelling and Analysis is a full course in the latest analytic strategies for hedge fund investing, complete with a one-of-a-kind primer on both C++ and object oriented programming (OOP). Covering both basic and risk-adjusted performance measures, this practitioner's guide enables you to manage risk easily and make the most of key statistics with simple and advanced analysis techniques. This highly anticipated third book in the widely used Hedge Fund Modelling and Analysis series is the only guide available for applying the powerful C++ language to revolutionise hedge fund trading. Even if you've never worked with code before, the focused overview of C++ gives you everything you need to navigate the technical aspects of object oriented programming, which enables you to build sophisticated analysis programs from small units of reusable code. This book is your breakthrough introduction to winning with hedge funds in the new reality of trading. Jumpstart your new approach to beating the markets with: All the guidance and hands-on support you need to use quantitative strategies to optimise hedge fund decision-making. Illustrative modelling exercises and worked-out problems demonstrating what to expect when assessing risk and return factors in the real world. A companion website offering additional C++ programs, algorithms and data to download. Make reading Hedge Fund Modelling and Analysis your new routine and gain all the insight and relevant information you need to beat the markets.
Author: G. Gregoriou
Publisher: Springer
ISBN: 0230295207
Category : Business & Economics
Languages : en
Pages : 229
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Book Description
This book proposes new tools and models to price options, assess market volatility, and investigate the market efficiency hypothesis. In particular, it considers new models for hedge funds and derivatives of derivatives, and adds to the literature of testing for the efficiency of markets both theoretically and empirically.
Author: Christian Schmidiger
Publisher:
ISBN:
Category :
Languages : en
Pages : 225
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Book Description
Driven by vast historical growth and the recent crises, the hedge fund industry has undergone several changes. This thesis presents studies on the analysis of hedge fund returns within changing market states by applying different constant, asymmetric and time-varying factor loading models. Considered models include the CAPM, Fama-French 3-factor model, Carhart 4-factor model, Fama-French 5-Factor model, Agarwal-Naik 8-factor model and the Fung-Hsieh 7- and 8-factor models. In addition, and unlike previous research, 94 hedge fund strategy styles have been analysed individually to test whether the model performances differ among approaches.The first full-sample analysis exhibits generally low explanatory power whereby the more sophisticated models perform superiorly. Equity strategies, especially long-only funds, exhibit high adjusted R-Squared among all models, while fixed income, fundamental and technical hedge funds result in low significance. The CUSUM control chart based crisis/non-crisis dummy cannot substantially improve the explanatory power of the models. Hedge fund alpha and factor significance varies considerably among strategies and the power of the models remains similarly poor. Asymmetric up/down models exhibit slightly improved explanatory power while the significance of alpha diminishes. Replacing the conditional up/down variable by the crisis/non-crisis setting resulted in inferior results. Empirical analysis with asymmetric higher-moment models approves the asymmetries in hedge fund returns partially. Moreover, a time-varying approach substantially improves the explanatory power of all models while hedge fund alpha further diminishes. All dynamic models exhibit significant exposures on macro state variables for a high proportion of funds. To summarise, it has been shown how simple models can be fitted to increase the explanatory power. As a result, the adjusted R-Squared were improved by 73%. On a strategy level, equity funds are explained the best while fixed income, fundamental and technical hedge funds are the most difficult to analyse.
Author: François-Éric Racicot
Publisher: Netbiblo S.L.
ISBN: 9788497453783
Category : Business mathematics
Languages : en
Pages : 0
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Book Description
In this book, we present several new empirical models and new estimation methods for financial models of returns. Our new empirical models are based on a generalized version of the Hausman test using higher moments and cumulants. Our methods rely on higher moments and cumulants as instruments to improve the well-known GMM technique, which we call the GMM-C or the Haus-C estimators. Then, we generalize these new estimators to panel data resorting to our new empirical models of hedge fund returns. Finally, we feature an innovative application of the Kalman filter for our new empirical models of hedge fund returns, in order to obtain a dynamic version of the alpha and beta parameters.
Author: Christian Alexander Wegener
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832527397
Category : Business & Economics
Languages : en
Pages : 285
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Book Description
The present work advances the research on hedge fund returns in three main areas. Firstly, their statistical properties are assessed in order to understand by what degree the returns of this alternative asset class are subject to non-normality, autocorrelation and heteroscedasticity. Secondly, state-of-the-art econometric approaches are used for the purpose of analyzing whether and to what extent monthly hedge fund returns are forecastable. Thirdly, an effort is made to identify and explain which economic risks affect the performance of the different hedge fund strategy styles in which way. The empirical results suggest that monthly hedge fund returns are forecastable by means of multivariate regression models which rely on economic predictors such as changes in interest rates or changes in business outlooks. Accounting for the fact that hedge fund returns are non-normally distributed, heteroscedastic and time-varying in their exposure to pervasive risk factors, the devised econometric models are found to deliver significant out-of-sample predictive power. The thesis at hand also documents that the interdependencies between the monthly changes of envisaged risk factors and the subsequent hedge fund returns remain remarkably stable throughout time. In essence, the performance of hedge funds appears to be sensitive to common business cycle movements. Altogether, the results are relevant to researchers in search of a description and application of contemporary return prediction methods as well as to investors in need of a better understanding of the drivers of hedge fund returns.
