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Record Nr. |
UNINA9910788168603321 |
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Titolo |
Mastering R for quantitative finance : use R to optimize your trading strategy and build up your own risk management system / / Edina Berlinger [and seventeen others] |
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Pubbl/distr/stampa |
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Birmingham, England : , : Packt Publishing, , 2015 |
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©2015 |
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ISBN |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (362 p.) |
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Collana |
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Community Experience Distilled |
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Disciplina |
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Soggetti |
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Finance |
R (Computer program language) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Time Series Analysis; Multivariate time series analysis; Cointegration; Vector autoregressive models; VAR implementation example; Cointegrated VAR and VECM; Volatility modeling; GARCH modeling with the rugarch package; The standard GARCH model; Exponential GARCH model (EGARCH); Threshold GARCH model (TGARCH); Simulation and forecasting; Summary; References and reading list; Chapter 2: Factor Models; Arbitrage pricing theory; Implementation of APT |
Fama-French three-factor modelModeling in R; Data selection; Estimation of APT with principal component analysis; Estimation of the Fama-French model; Summary; References; Chapter 3: Forecasting Volume; Motivation; The intensity of trading; The volume forecasting model; Implementation in R; The data; Loading the data; The seasonal component; AR(1) estimation and forecasting; SETAR estimation and forecasting; Interpreting the results; Summary; References; Chapter 4: Big Data - Advanced Analytics; Getting data from open sources; Introduction to big data analysis in R |
K-means clustering on big dataLoading big matrices; Big data K-means clustering analysis; Big data linear regression analysis; Loading big |
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