Financial Data Analytics with R: Monte Carlo Validation, Chen, Jenny K
Автор: Christian Robert; George Casella Название: Monte Carlo Statistical Methods ISBN: 1441919392 ISBN-13(EAN): 9781441919397 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
Автор: Glasserman Название: Monte Carlo Methods in Financial Engineering ISBN: 0387004513 ISBN-13(EAN): 9780387004518 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: From the reviews: "Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers [...] So often, financial engineering texts are very theoretical. This book is not."
Автор: Scutari, Marco Название: The Pragmatic Programmer for Machine Learning ISBN: 0367263505 ISBN-13(EAN): 9780367263508 Издательство: Taylor&Francis Рейтинг: Цена: 11482.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Gives a holistic approach to machine learning and data science applications, from design to deployment and quality assurance, as an overarching cyclical process; Bridges machine learning and software engineering to build a shared set of best practices useful to both academia and the industry; Discusses deployment options for different types of models and data to help practitioners reason and make informed choices. Emphasizes the role of coding standards and software architecture alongside statistical rigor to implement reproducible and scalable machine learning modelsKey Features: A complete guide to software engineering for machine learning and data science applications, from choosing the right hardware to analysing algorithms and designing scalable architectures. Surveys the state of the art of the software and frameworks used to build and run machine learning applications, comparing and contrasting their trade-offs.
Comes with a complete case study in natural language understanding which illustrates the principles and the tools covered in the book. Code available from GitHub. Provides a multi-disciplinary view of how traditional software learning practices can be integrated with the workflows of domain experts and the unique characteristics of software in which data play a central role.
Автор: Jay Liebowitz Название: Developing the intuitive executive ISBN: 103249820X ISBN-13(EAN): 9781032498201 Издательство: Taylor&Francis Рейтинг: Цена: 7348.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Lai T.L. Название: Data Science and Risk Analytics in Finance and Insurance ISBN: 1439839484 ISBN-13(EAN): 9781439839485 Издательство: Taylor&Francis Рейтинг: Цена: 10717.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Brandimarte P Название: Handbook in Monte Carlo Simulation ISBN: 0470531118 ISBN-13(EAN): 9780470531112 Издательство: Wiley Рейтинг: Цена: 20426.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applications of Monte Carlo methods in financial engineering and economics.
Автор: Glasserman, Paul Название: Monte carlo methods in financial engineering ISBN: 1441918221 ISBN-13(EAN): 9781441918222 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: From the reviews: "Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers [...] So often, financial engineering texts are very theoretical. This book is not."
Описание: Financial models are an inescapable feature of modern financial markets. Yet it was over reliance on these models and the failure to test them properly that is now widely recognized as one of the main causes of the financial crisis of 2007–2011. Since this crisis, there has been an increase in the amount of scrutiny and testing applied to such models, and validation has become an essential part of model risk management at financial institutions. The book covers all of the major risk areas that a financial institution is exposed to and uses models for, including market risk, interest rate risk, retail credit risk, wholesale credit risk, compliance risk, and investment management. The book discusses current practices and pitfalls that model risk users need to be aware of and identifies areas where validation can be advanced in the future. This provides the first unified framework for validating risk management models.
Автор: Korn, Ralf Korn, Elke Kroisandt, Gerald Название: Monte carlo methods and models in finance and insurance ISBN: 1032477695 ISBN-13(EAN): 9781032477695 Издательство: Taylor&Francis Рейтинг: Цена: 6583.00 р. Наличие на складе: Нет в наличии.
Описание: Offering a unique balance between applications and calculations, this book incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Romberg method, and the Heath-Platen estimator
Автор: Korn, Ralf Название: Monte Carlo Methods and Models in Finance and Insurance ISBN: 1420076183 ISBN-13(EAN): 9781420076189 Издательство: Taylor&Francis Рейтинг: Цена: 22202.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Hautsch, Nikolaus Название: Econometrics of Financial High-Frequency Data ISBN: 3642219241 ISBN-13(EAN): 9783642219245 Издательство: Springer Рейтинг: Цена: 25853.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis.
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