Nonparametric finance

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...

Descripción completa

Detalles Bibliográficos
Otros Autores: Klemelä, Jussi, 1965- author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : Wiley 2018.
Edición:1st edition
Colección:Wiley series in probability and statistics.
Materias:
Finance > Statistical methods.
Finance > Mathematical models.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630366406719
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 Rate
  • 2.2.3 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&amp
  • P 500 Data
  • 2.4.2 Daily S&amp
  • P 500 and Nasdaq‐100 Data
  • 2.4.3 Monthly S&amp
  • P 500, Bond, and Bill Data
  • 2.4.4 Daily US Treasury 10 Year Bond Data
  • 2.4.5 Daily S&amp
  • 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 Median
  • 3.1.1.3 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 Function
  • 3.2.2 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 Log‐normal Models
  • 3.3.1.1 The Normal and Log‐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 Distributions
  • 3.4.2.1 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&amp
  • P 500 Daily Returns: Maximum Likelihood
  • 3.4.3.2 Tail Index Estimation for S&amp
  • 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
  • 3.5.1.3 Sums of Dependent Random Variables
  • 3.5.2 The Limit Theorems for Maxima
  • 3.5.2.1 Weak Convergence of Maxima
  • 3.5.2.2 Extreme Value Distributions
  • 3.5.2.3 Convergence to an Extreme Value Distribution
  • 3.5.2.4 Generalized Pareto Distributions
  • 3.5.2.5 Convergence to a Generalized Pareto Distribution
  • 3.6 Univariate Stylized Facts
  • Chapter 4 Multivariate Data Analysis
  • 4.1 Measures of Dependence
  • 4.1.1 Correlation Coefficients
  • 4.1.1.1 Linear Correlation
  • 4.1.1.2 Spearman's Rank Correlation
  • 4.1.1.3 Kendall's Rank Correlation.
  • 4.1.1.4 Relations between the Correlation Coefficients
  • 4.1.2 Coefficients of Tail Dependence
  • 4.1.2.1 Tail Coefficients in Terms of the Copula
  • 4.1.2.2 Estimation of Tail Coefficients
  • 4.1.2.3 Tail Coefficients for Parametric Families
  • 4.2 Multivariate Graphical Tools
  • 4.2.1 Scatter Plots
  • 4.2.2 Correlation Matrix: Multidimensional Scaling
  • 4.2.2.1 Correlation Matrix
  • 4.2.2.2 Multidimensional Scaling
  • 4.3 Multivariate Parametric Models
  • 4.3.1 Multivariate Gaussian Distributions
  • 4.3.2 Multivariate Student Distributions
  • 4.3.3 Normal Variance Mixture Distributions
  • 4.3.4 Elliptical Distributions
  • 4.4 Copulas
  • 4.4.1 Standard Copulas
  • 4.4.1.1 Finding the Copula of a Multivariate Distribution
  • 4.4.1.2 Constructing a Multivariate Distribution from a Copula
  • 4.4.2 Nonstandard Copulas
  • 4.4.3 Sampling from a Copula
  • 4.4.3.1 Simulation from a Copula
  • 4.4.3.2 Transforming the Sample
  • 4.4.3.3 Transforming the Sample by Estimating the Margins
  • 4.4.3.4 Empirical Copula
  • 4.4.3.5 Maximum Likelihood Estimation
  • 4.4.4 Examples of Copulas
  • 4.4.4.1 The Gaussian Copulas
  • 4.4.4.2 The Student Copulas
  • 4.4.4.3 Other Copulas
  • 4.4.4.4 Empirical Results
  • Chapter 5 Time Series Analysis
  • 5.1 Stationarity and Autocorrelation
  • 5.1.1 Strict Stationarity
  • 5.1.1.1 Random Walk
  • 5.1.2 Covariance Stationarity and Autocorrelation
  • 5.1.2.1 Autocovariance and Autocorrelation for Scalar Time Series
  • 5.1.2.2 Autocovariance for Vector Time Series
  • 5.2 Model Free Estimation
  • 5.2.1 Descriptive Statistics for Time Series
  • 5.2.2 Markov Models
  • 5.2.3 Time Varying Parameter
  • 5.2.3.1 Local Likelihood
  • 5.2.3.2 Local Least Squares
  • 5.2.3.3 Time Varying Estimators for the Excess Distribution
  • 5.3 Univariate Time Series Models
  • 5.3.1 Prediction and Conditional Expectation
  • 5.3.2 ARMA Processes.
  • 5.3.2.1 Innovation Processes
  • 5.3.2.2 Moving Average Processes
  • 5.3.2.3 Autoregressive Processes
  • 5.3.2.4 ARMA Processes
  • 5.3.3 Conditional Heteroskedasticity Models
  • 5.3.3.1 ARCH Processes
  • 5.3.3.2 GARCH Processes
  • 5.3.3.3 ARCH(∞) Model
  • 5.3.3.4 Asymmetric GARCH Processes
  • 5.3.3.5 The Moment Generating function
  • 5.3.3.6 Parameter Estimation
  • 5.3.3.7 Fitting the GARCH(1,1) Model
  • 5.3.4 Continuous Time Processes
  • 5.3.4.1 The Brownian Motion
  • 5.3.4.2 Diffusion Processes and Itô's Lemma
  • 5.3.4.3 The Geometric Brownian Motion
  • 5.3.4.4 Girsanov's Theorem
  • 5.4 Multivariate Time Series Models
  • 5.4.1 MGARCH Models
  • 5.4.2 Covariance in MGARCH Models
  • 5.5 Time Series Stylized Facts
  • Chapter 6 Prediction
  • 6.1 Methods of Prediction
  • 6.1.1 Moving Average Predictors
  • 6.1.1.1 One‐Sided Moving Average
  • 6.1.1.2 Exponential Moving Average
  • 6.1.2 State Space Predictors
  • 6.1.2.1 Linear Regression
  • 6.1.2.2 Kernel Regression
  • 6.2 Forecast Evaluation
  • 6.2.1 The Sum of Squared Prediction Errors
  • 6.2.1.1 Out‐of‐Sample Sum of Squares
  • 6.2.1.2 In‐Sample Sum of Squares
  • 6.2.1.3 Visual Diagnostics
  • 6.2.2 Testing the Prediction Accuracy
  • 6.2.2.1 Diebold-Mariano Test
  • 6.2.2.2 Tests Using Sample Correlation and Covariance
  • 6.3 Predictive Variables
  • 6.3.1 Risk Indicators
  • 6.3.1.1 Default Spread
  • 6.3.1.2 Credit Spreads
  • 6.3.1.3 Volatility Indexes
  • 6.3.2 Interest Rate Variables
  • 6.3.2.1 Term Spread
  • 6.3.2.2 Real Yield
  • 6.3.3 Stock Market Indicators
  • 6.3.3.1 Dividend Price Ratio and Dividend Yield
  • 6.3.3.2 Valuation in Stock Markets
  • 6.3.3.3 Relative Valuation
  • 6.3.4 Sentiment Indicators
  • 6.3.4.1 Purchasing Managers Index
  • 6.3.4.2 Investor and Consumer Sentiment
  • 6.3.5 Technical Indicators
  • 6.4 Asset Return Prediction
  • 6.4.1 Prediction of S&amp
  • P 500 Returns
  • 6.4.1.1 S&amp.
  • P 500 Returns
  • 6.4.1.2 Linear Regression for Predicting S&amp
  • P 500 Returns
  • 6.4.2 Prediction of 10‐Year Bond Returns
  • 6.4.2.1 10‐Year Bond Returns
  • 6.4.2.2 Linear Regression for Predicting 10‐Year Bond Returns
  • Part II Risk Management
  • Chapter 7 Volatility Prediction
  • 7.1 Applications of Volatility Prediction
  • 7.1.1 Variance and Volatility Trading
  • 7.1.2 Covariance Trading
  • 7.1.3 Quantile Estimation
  • 7.1.4 Portfolio Selection
  • 7.1.5 Option Pricing
  • 7.2 Performance Measures for Volatility Predictors
  • 7.3 Conditional Heteroskedasticity Models
  • 7.3.1 GARCH Predictor
  • 7.3.1.1 Predicting the Squared Returns
  • 7.3.1.2 Predicting the Realized Volatility
  • 7.3.1.3 S&amp
  • P 500 Volatility Prediction with GARCH(1,1)
  • 7.3.2 ARCH Predictor
  • 7.3.2.1 Predicting the Squared Returns
  • 7.3.2.2 S&amp
  • P 500 Volatility Prediction with ARCH(p)
  • 7.4 Moving Average Methods
  • 7.4.1 Sequential Sample Variance
  • 7.4.2 Exponentially Weighted Moving Average
  • 7.4.2.1 Asymmetric Exponentially Weighted Moving Average
  • 7.5 State Space Predictors
  • 7.5.1 Linear Regression Predictor
  • 7.5.1.1 Prediction with Volatility and Mean
  • 7.5.1.2 Prediction with Past Squared Returns
  • 7.5.2 Kernel Regression Predictor
  • Chapter 8 Quantiles and Value‐at‐Risk
  • 8.1 Definitions of Quantiles
  • 8.2 Applications of Quantiles
  • 8.2.1 Reserve Capital
  • 8.2.1.1 Value‐at‐Risk of a Portfolio
  • 8.2.1.2 Decomposition of the Loss of a Portfolio
  • 8.2.1.3 Losses over Several Periods
  • 8.2.2 Margin Requirements
  • 8.2.3 Quantiles as a Risk Measure
  • 8.3 Performance Measures for Quantile Estimators
  • 8.3.1 Measuring the Probability of Exceedances
  • 8.3.1.1 Cross‐Validation
  • 8.3.1.2 Probability Differences
  • 8.3.1.3 Confidence of the Performance Measure
  • 8.3.1.4 Probability Differences Over All Time Intervals.
  • 8.3.2 A Loss Function for Quantile Estimation.

Ejemplares similares