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Mastering R for Quantitative Finance.
R is a powerful open source functional programming language that provides high level graphics and interfaces to other languages. Its strength lies in data analysis, graphics, visualization, and data manipulation. R is becoming a widely used modeling tool in science, engineering, and business. The bo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing,
2015.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Berlinger, Edina. | |
| 245 | 1 | 0 | |a Mastering R for Quantitative Finance. |
| 260 | |b Packt Publishing, |c 2015. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 588 | 0 | |a Print version record. | |
| 520 | |a R is a powerful open source functional programming language that provides high level graphics and interfaces to other languages. Its strength lies in data analysis, graphics, visualization, and data manipulation. R is becoming a widely used modeling tool in science, engineering, and business. The book is organized as a step-by-step practical guide to using R. Starting with time series analysis, you will also learn how to forecast the volume for VWAP Trading. Among other topics, the book covers FX derivatives, interest rate derivatives, and optimal hedging. The last chapters provide an overview on liquidity risk management, risk measures, and more. The book pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the book, you will be well versed with various financial techniques using R and will be able to place good bets while making financial decisions. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a K-means clustering on big dataLoading big matrices; Big data K-means clustering analysis; Big data linear regression analysis; Loading big data; Fitting a linear regression model on large datasets; Summary; References; Chapter 5: FX Derivatives; Terminology and notations; Currency options; Exchange options; Two-dimensional Wiener processes; The Margrabe formula; Application in R; Quanto options; Pricing formula for call quanto; Pricing a call quanto in R; Summary; References; Chapter 6: Interest Rate Derivatives and Models; The Black model; Pricing a cap with Black's model; The Vasicek model. | |
| 505 | 8 | |a The Cox-Ingersoll-Ross modelParameter estimation of interest rate models; Using the SMFI5 package; Summary; References; Chapter 7: Exotic Options; A general pricing approach; The role of dynamic hedging; How R could help a lot; A glance beyond vanillas; Greeks -- the link back to the vanilla world; Pricing the Double-no-touch option; Another way to price the Double-no-touch option; The life of a Double-no-touch option -- a simulation; Exotic options embedded in structured products; Summary; References; Chapter 8: Optimal Hedging; Hedging of derivatives; Market risk of derivatives. | |
| 505 | 8 | |a Static delta hedgeDynamic delta hedge; Comparing the performance of delta hedging; Hedging in the presence of transaction costs; Optimization of the hedge; Optimal hedging in the case of absolute transaction costs; Optimal hedging in the case of relative transaction costs; Further extensions; Summary; References; Chapter 9: Fundamental Analysis; The Basics of fundamental analysis; Collecting data; Revealing connections; Including multiple variables; Separating investment targets; Setting classification rules; Backtesting; Industry-specific investment; Summary; References. | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Finance. | |
| 650 | 6 | |a Finances. | |
| 650 | 7 | |a finance. |2 aat | |
| 650 | 7 | |a Finance |2 fast | |
| 758 | |i has work: |a Mastering R for quantitative finance (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFYhQGJKD8q8jhwhcCHpyd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Erscheint auch als: |n Druck-Ausgabe |t Berlinger, Edina. Mastering R for Quantitative Finance |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1987846 |z Texto completo |
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