LEADER 05298nam 2200685 450 001 9910788168603321 005 20220701192145.0 010 $a1-78355-208-5 035 $a(CKB)2670000000601167 035 $a(EBL)1987846 035 $a(SSID)ssj0001494642 035 $a(PQKBManifestationID)11909560 035 $a(PQKBTitleCode)TC0001494642 035 $a(PQKBWorkID)11449434 035 $a(PQKB)11310054 035 $a(Au-PeEL)EBL1987846 035 $a(CaPaEBR)ebr11032371 035 $a(CaONFJC)MIL750734 035 $a(OCoLC)905308486 035 $a(CaSebORM)9781783552078 035 $a(MiAaPQ)EBC1987846 035 $a(PPN)228050111 035 $a(EXLCZ)992670000000601167 100 $a20150330h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMastering R for quantitative finance $euse R to optimize your trading strategy and build up your own risk management system /$fEdina Berlinger [and seventeen others] 205 $a1st edition 210 1$aBirmingham, England :$cPackt Publishing,$d2015. 210 4$dİ2015 215 $a1 online resource (362 p.) 225 1 $aCommunity Experience Distilled 300 $aDescription based upon print version of record. 311 $a1-78355-207-7 311 $a1-336-19448-0 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; 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 327 $aFama-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 327 $aK-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 327 $aThe 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 327 $aStatic 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 327 $aChapter 10: Technical Analysis, Neural Networks, and Logoptimal Portfolios 330 $aThis book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R. 410 0$aCommunity experience distilled. 606 $aFinance 606 $aR (Computer program language) 615 0$aFinance. 615 0$aR (Computer program language). 676 $a332 702 $aBerlinger$b Edina 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788168603321 996 $aMastering R for quantitative finance$93714869 997 $aUNINA