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Nonparametric finance /
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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
[2018]
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| Colección: | Wiley series in probability and statistics
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.