1st Edition
Optimal Statistical Inference in Financial Engineering
378 Pages
61 B/W Illustrations
by
Chapman & Hall
384 Pages
by
Chapman & Hall
Also available as eBook on:
Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering literature, even though this idea is fundamental to the field. Balancing statistical theory with data analysis, Optimal Statistical Inference in Financial Engineering examines how stochastic models can effectively describe actual financial data and illustrates how to... Read more
PREFACE
INTRODUCTION
ELEMENTS OF PROBABILITY
Probability and Probability Distribution
Vector Random Variable and Independence
Expectation and Conditional Distribution
Convergence and Central Limit Theorems
STATISTICAL INFERENCE
Sufficient Statistics
Unbiased Estimators
Efficient Estimators
Asymptotically Efficient Estimators
VARIOUS STATISTICAL METHODS
Interval Estimation
Most Powerful Test
Various Tests
Discriminant Analysis
STOCHASTIC PROCESSES
Elements of Stochastic Processes
Spectral Analysis
Ergodicity, Mixing, and Martingale
Limit Theorems for Stochastic Processes
Exercise
TIME SERIES ANALYSIS
Time Series Model
Estimation of Time Series Models
Model Selection Problems
Nonparametric Estimation
Prediction of Time Series
Regression for Time Series
Long Memory Processes
Local Whittle Likelihood Approach
Nonstationary Processes
Semiparametric Estimation
Discriminant Analysis for Time Series
INTRODUCTION TO STATISTICAL FINANCIAL ENGINEERING
Option Pricing Theory
Higher Order Asymptotic Option Valuation for Non-Gaussian Dependent Returns
Estimation of Portfolio
Value-at-Risk (VaR) Problems
TERM STRUCTURE
Spot Rates and Discount Bonds
Estimation Procedures for Term Structure
CREDIT RATING
Parametric Clustering for Financial Time Series
Nonparametric Clustering for Financial Time Series
Credit Rating Based on Financial Time Series
APPENDIX
REFERENCES
INDEX
INTRODUCTION
ELEMENTS OF PROBABILITY
Probability and Probability Distribution
Vector Random Variable and Independence
Expectation and Conditional Distribution
Convergence and Central Limit Theorems
STATISTICAL INFERENCE
Sufficient Statistics
Unbiased Estimators
Efficient Estimators
Asymptotically Efficient Estimators
VARIOUS STATISTICAL METHODS
Interval Estimation
Most Powerful Test
Various Tests
Discriminant Analysis
STOCHASTIC PROCESSES
Elements of Stochastic Processes
Spectral Analysis
Ergodicity, Mixing, and Martingale
Limit Theorems for Stochastic Processes
Exercise
TIME SERIES ANALYSIS
Time Series Model
Estimation of Time Series Models
Model Selection Problems
Nonparametric Estimation
Prediction of Time Series
Regression for Time Series
Long Memory Processes
Local Whittle Likelihood Approach
Nonstationary Processes
Semiparametric Estimation
Discriminant Analysis for Time Series
INTRODUCTION TO STATISTICAL FINANCIAL ENGINEERING
Option Pricing Theory
Higher Order Asymptotic Option Valuation for Non-Gaussian Dependent Returns
Estimation of Portfolio
Value-at-Risk (VaR) Problems
TERM STRUCTURE
Spot Rates and Discount Bonds
Estimation Procedures for Term Structure
CREDIT RATING
Parametric Clustering for Financial Time Series
Nonparametric Clustering for Financial Time Series
Credit Rating Based on Financial Time Series
APPENDIX
REFERENCES
INDEX
Biography
Masanobu Taniguchi, Junichi Hirukawa, Kenichiro Tamaki
This book can be recommended to scholars and PhD students interested in finance and time series.
—Journal of Times Series Analysis, April 2010
