Nonparametric finance / by Jussi Sakari Klemelä.
An 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 f...
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| Format: | Electronic eBook |
| Language: | English |
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Hoboken, NJ :
John Wiley & Sons, Inc.,
[2018]
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| Series: | Wiley series in probability and statistics
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| 100 | 1 | |a Klemelä, Jussi, |d 1965- |e author. | |
| 245 | 1 | 0 | |a Nonparametric finance / |c by Jussi Sakari Klemelä. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, Inc., |c [2018] | |
| 300 | |a 1 online resource | ||
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| 490 | 0 | |a Wiley series in probability and statistics | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |a Cover; Title Page; Copyright; Contents; Preface; Chapter 1 Introduction; 1.1 Statistical Finance; 1.2 Risk Management; 1.3 Portfolio Management; 1.4 Pricing of Securities; Part I Statistical Finance; Chapter 2 Financial Instruments; 2.1 Stocks; 2.1.1 Stock Indexes; 2.1.1.1 Definition of a Stock Index; 2.1.1.2 Uses of Stock Indexes; 2.1.1.3 Examples of Stock Indexes; 2.1.2 Stock Prices and Returns; 2.1.2.1 Initial Price Data; 2.1.2.2 Sampling of Prices; 2.1.2.3 Stock Returns; 2.2 Fixed Income Instruments; 2.2.1 Bonds; 2.2.2 Interest Rates; 2.2.2.1 Definitions of Interest Rates. | |
| 505 | 8 | |a 2.2.2.2 The Risk Free Rate2.2.3 Bond Prices and Returns; 2.3 Derivatives; 2.3.1 Forwards and Futures; 2.3.1.1 Forwards; 2.3.1.2 Futures; 2.3.2 Options; 2.3.2.1 Calls and Puts; 2.3.2.2 Applications of Options; 2.3.2.3 Exotic Options; 2.4 Data Sets; 2.4.1 Daily S & P 500 Data; 2.4.2 Daily S & P 500 and Nasdaqâ#x80;#x90;100 Data; 2.4.3 Monthly S & P 500, Bond, and Bill Data; 2.4.4 Daily US Treasury 10 Year Bond Data; 2.4.5 Daily S & P 500 Components Data; Chapter 3 Univariate Data Analysis; 3.1 Univariate Statistics; 3.1.1 The Center of a Distribution; 3.1.1.1 The Mean and the Conditional Mean. | |
| 505 | 8 | |a 3.1.1.2 The Median and the Conditional Median3.1.1.3 The Mode and the Conditional Mode; 3.1.2 The Variance and Moments; 3.1.2.1 The Variance and the Conditional Variance; 3.1.2.2 The Upper and Lower Partial Moments; 3.1.2.3 The Upper and Lower Conditional Moments; 3.1.3 The Quantiles and the Expected Shortfalls; 3.1.3.1 The Quantiles and the Conditional Quantiles; 3.1.3.2 The Expected Shortfalls; 3.2 Univariate Graphical Tools; 3.2.1 Empirical Distribution Function Based Tools; 3.2.1.1 The Empirical Distribution Function; 3.2.1.2 The Tail Plots; 3.2.1.3 Regression Plots of Tails. | |
| 505 | 8 | |a 3.2.1.4 The Empirical Quantile Function3.2.2 Density Estimation Based Tools; 3.2.2.1 The Histogram; 3.2.2.2 The Kernel Density Estimator; 3.3 Univariate Parametric Models; 3.3.1 The Normal and Logâ#x80;#x90;normal Models; 3.3.1.1 The Normal and Logâ#x80;#x90;normal Distributions; 3.3.1.2 Modeling Stock Prices; 3.3.2 The Student Distributions; 3.3.2.1 Properties of Student Distributions; 3.3.2.2 Estimation of the Parameters of a Student Distribution; 3.4 Tail Modeling; 3.4.1 Modeling and Estimating Excess Distributions; 3.4.1.1 Modeling Excess Distributions; 3.4.1.2 Estimation. | |
| 505 | 8 | |a 3.4.2 Parametric Families for Excess Distributions3.4.2.1 The Exponential Distributions; 3.4.2.2 The Pareto Distributions; 3.4.2.3 The Gamma Distributions; 3.4.2.4 The Generalized Pareto Distributions; 3.4.2.5 The Weibull Distributions; 3.4.2.6 A Three Parameter Family; 3.4.3 Fitting the Models to Return Data; 3.4.3.1 S & P 500 Daily Returns: Maximum Likelihood; 3.4.3.2 Tail Index Estimation for S & P 500 Components; 3.5 Asymptotic Distributions; 3.5.1 The Central Limit Theorems; 3.5.1.1 Sums of Independent Random Variables; 3.5.1.2 Sums of Independent and Identically Distributed Random Variables. | |
| 520 | |a An 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 KlemelA, PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance. | ||
| 650 | 0 | |a Finance |x Statistical methods. | |
| 650 | 0 | |a Finance |x Mathematical models. | |
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| 650 | 7 | |a Finance |x Statistical methods |2 fast | |
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