LEADER 04037nam 2200517 450 001 9910813981003321 005 20200520144314.0 010 $a1-119-40912-8 010 $a1-119-40911-X 010 $a1-119-40913-6 035 $a(CKB)4100000000981154 035 $a(Au-PeEL)EBL5313432 035 $a(CaPaEBR)ebr11519798 035 $a(OCoLC)1004769900 035 $a(CaSebORM)9781119409106 035 $a(MiAaPQ)EBC5313432 035 $a(EXLCZ)994100000000981154 100 $a20180322h20182018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aNonparametric finance /$fby Jussi Sakari Klemela 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2018. 210 4$d©2018 215 $a1 online resource (72 pages) $cillustrations, graphs 225 1 $aWiley series in probability and statistics 311 $a1-119-40910-1 320 $aIncludes bibliographical references and index. 330 $aAn Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end. Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance is emphasized over statistical significance throughout, and R code is provided to help readers reproduce the research, computations, and figures being discussed. Strong graphical content clarifies the methods and demonstrates essential visualization techniques, while deep mathematical and statistical insight backs up practical applications. Written for the leading edge of finance, Nonparametric Finance: ? Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction ? Provides risk management guidance through volatility prediction, quantiles, and value-at-risk ? Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more ? Discusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles ? Provides supplementary R code and numerous graphics to reinforce complex content Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world advantage. Jussi Klemelä, PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visua... 410 0$aWiley series in probability and statistics. 606 $aFinance$xStatistical methods 606 $aFinance$xMathematical models 615 0$aFinance$xStatistical methods. 615 0$aFinance$xMathematical models. 676 $a332.0151954 700 $aKlemela?$b Jussi$f1965-$0879060 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910813981003321 996 $aNonparametric finance$94023574 997 $aUNINA