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The second book in Darbyshire and Hampton's Hedge Fund Modelling and Analysis series, Hedge Fund Modelling and Analysis Using MATLAB(r) takes advantage of the huge library of built-in functions and suite of financial and analytic packages available to MATLAB(r). 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…mehr

Produktbeschreibung
The second book in Darbyshire and Hampton's Hedge Fund Modelling and Analysis series, Hedge Fund Modelling and Analysis Using MATLAB(r) takes advantage of the huge library of built-in functions and suite of financial and analytic packages available to MATLAB(r). 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(r) source code, as well as other useful resources.

Hedge Fund Modelling and Analysis Using MATLAB(r) 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.
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Autorenporträt
Paul Darbyshire gained his PhD in Theoretical Physics from King's College London and then began his career working has a Quantitative Analyst and Trader at HSBC on the Exotic Derivatives and Structured Products desk. He has subsequently been involved in the development and implementation of a variety of trading and risk management platforms for a number of major investment banks around the globe. Since 2005, Paul has been responsible for the analysis and design of cutting-edge algorithms in the development of behavioural finance and decision making models at the University of Oxford. Paul also provides many private equity firms, hedge funds and investment management companies with senior consultancy in areas such as dynamic portfolio optimisation, trading platform design, software engineering and risk management. David Hampton gained his PhD in Electrical Engineering from the Queen's University of Belfast and an international MBA from Institut Superieur de Gestion in Paris, New York and Tokyo before joining Bank of America Capital Markets in London. David was previously an Adjunct Finance Professor at Skema Business School in Sophia Antipolis where he taught Financial Engineering and Excel/VBA Programming at the MSc level.  At EDHEC Business School in Nice, he was responsible for managing their range of five MSc courses as Assistant Dean of the Financial Economics Track. An NFA registered CTA since 1996, David has been active as a consultant to the hedge fund community and as a Hedge Fund Manager with particular expertise in Global Macro Managed Futures and Long Short Equity investment styles. Both David and Paul are Directors of darbyshirehampton; an innovative quantitative research, advisory, and consultancy firm specialising in hedge funds and the alternative investment industry. Website: www.darbyshirehampton.com.