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Statistical Models and Methods for Financial Markets 2008 ed. [Hardback]

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  • Formāts: Hardback, 356 pages, height x width: 234x156 mm, weight: 1550 g, XX, 356 p., 1 Hardback
  • Sērija : Springer Texts in Statistics
  • Izdošanas datums: 25-Jul-2008
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 0387778268
  • ISBN-13: 9780387778266
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Statistical Models and Methods for Financial Markets 2008 ed.Palielināt
  • Formāts: Hardback, 356 pages, height x width: 234x156 mm, weight: 1550 g, XX, 356 p., 1 Hardback
  • Sērija : Springer Texts in Statistics
  • Izdošanas datums: 25-Jul-2008
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 0387778268
  • ISBN-13: 9780387778266
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780387778266.html
Keywords:
This book is intended as a statistics textbook for masters students in mathematical finance/computational finance/financial mathematics. It is also intended for analysts in the financial industry.

This book presents statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modeling and decision making. Part I provides basic background in statistics, which includes linear regression and extensions to generalized linear models and nonlinear regression, multivariate analysis, likelihood inference and Bayesian methods, and time series analysis. It also describes applications of these methods to portfolio theory and dynamic models of asset returns and their volatilities. Part II presents advanced topics in quantitative finance and introduces a substantive-empirical modeling approach to address the discrepancy between finance theory and market data. It describes applications to option pricing, interest rate markets, statistical trading strategies, and risk management. Nonparametric regression, advanced multivariate and time series methods in financial econometrics, and statistical models for high-frequency transactions data are also introduced in this connection.The book has been developed as a textbook for courses on statistical modeling in quantitative finance in master's level financial mathematics (or engineering) and computational (or mathematical) finance programs. It is also designed for self-study by quantitative analysts in the financial industry who want to learn more about the background and details of the statistical methods used by the industry. It can also be used as a reference for graduate statistics and econometrics courses on regression, multivariate analysis, likelihood and Bayesian inference, nonparametrics, and time series, providing concrete examples and data from financial markets to illustrate the statistical methods.

Recenzijas

From the reviews:

"This book presents a comprehensive overview of how statistics can be used to solve problems in quantitative finance. The breadth and depth of the topics covered is impressive. The authors have succeeded in writing a book that bridges the gap between theory and practice in financial markets. how this book links finance theory to market practice via statistical modeling makes it original and fresh. As a result the book reflects the power of the intergrarion of financial and statistical methods in finance." (Lasse Koskinen, International Statistical Review, 2009, 77, 1)

"The book is divided into two parts: the first part introduces basic statistical methods and financial applications. Part two deals with advanced topics in quantitative finance. The book is not only useful for financial market economists, but, due to the wide range of special topics in the second part, also for students in the fields of engineering, mathematics, and statistics." (Herbert S. Buscher, Zentralblatt MATH, Vol. 1149, 2008)

This text by Lai and Zing was completed as the tumult of 2008 was unfolding, but its methods aretimeless, and future students and teachers can benefit in better times from the clear and cohesive exposition that this text provides. a useful text that anyone who teaches this material will want to consider. The list of topics covered is remarkably extensive; the exposition is always compactand often quite elegant. ((Journal of the American Statistical Association, September 2009, Vol. 104, No. 487)

Preface vii
Part I Basic Statistical Methods and Financial Applications
Linear Regression Models
3(34)
Ordinary least squares (OLS)
4(4)
Residuals and their sum of squares
4(1)
Properties of projection matrices
5(1)
Properties of nonnegative definite matrices
6(1)
Statistical properties of OLS estimates
7(1)
Statistical Inference
8(4)
Confidence intervals
8(2)
ANOVA (analysis of variance) tests
10(2)
Variable selection
12(4)
Test-based and other variable selection criteria
12(3)
Stepwise variable selection
15(1)
Regression diagnostics
16(3)
Analysis of residuals
17(1)
Influence diagnostics
18(1)
Extension to stochastic regressors
19(3)
Minimum-variance linear predictors
19(1)
Futures markets and hedging with futures contracts
20(1)
Inference in the case of stochastic regressors
21(1)
Bootstrapping in regression
22(3)
The plug-in principle and bootstrap resampling
22(2)
Bootstrapping regression models
24(1)
Bootstrap confidence intervals
25(1)
Generalized least squares
25(1)
Implementation and illustration
26(11)
Exercise
32(5)
Multivariate Analysis and Likelihood Inference
37(26)
Joint distribution of random variables
38(3)
Change of variables
39(1)
Mean and covariance matrix
39(2)
Principal component analysis (PCA)
41(7)
Basic definitions
41(1)
Properties of principal components
42(2)
An example: PCA of U.S. Treasury-LIBOR swap rates
44(4)
Multivariate normal distribution
48(7)
Definition and density function
48(2)
Marginal and conditional distributions
50(1)
Orthogonality and independence, with applications to regression
50(2)
Sample covariance matrix and Wishart distribution
52(3)
Likelihood inference
55(8)
Method of maximum likelihood
55(3)
Asymptotic inference
58(1)
Parametric bootstrap
59(1)
Exercises
60(3)
Basic Investment Models and Their Statistical Analysis
63(30)
Asset returns
64(3)
Definitions
64(2)
Statistical models for asset prices and returns
66(1)
Markowitz's portfolio theory
67(5)
Portfolio weights
67(1)
Geometry of efficient sets
68(1)
Computation of efficient portfolios
69(2)
Estimation of μ and σ and an example
71(1)
Capital asset pricing model (CAPM)
72(9)
The model
72(5)
Investment implications
77(1)
Estimation and testing
77(2)
Empirical studies of CAPM
79(2)
Multifactor pricing models
81(6)
Arbitrage pricing theory
81(1)
Factor analysis
82(3)
The PCA approach
85(1)
The Fama-French three-factor model
86(1)
Applications of resampling to portfolio management
87(6)
Michaud's resampled efficient frontier
87(1)
Bootstrap estimates of performance
88(1)
Exercises
89(4)
Parametric Models and Bayesian Methods
93(22)
Maximum likelihood and generalized linear models
94(3)
Numerical methods for computing MLE
94(1)
Generalized linear models
95(2)
Nonlinear regression models
97(6)
The Gauss-Newton algorithm
98(2)
Statistical inference
100(1)
Implementation and an example
101(2)
Bayesian inference
103(6)
Prior and posterior distributions
103(1)
Bayes procedures
104(1)
Bayes estimators of multivariate normal mean and covariance matrix
105(2)
Bayes estimators in Gaussian regression models
107(1)
Empirical Bayes and shrinkage estimators
108(1)
Investment applications of shrinkage estimators and Bayesian methods
109(6)
Shrinkage estimators of μ and Σ for the plug-in efficient frontier
110(1)
An alternative Bayesian approach
111(2)
Exercises
113(2)
Time Series Modeling and Forecasting
115(24)
Stationary time series analysis
115(8)
Weak stationarity
115(2)
Tests of independence
117(2)
Wold decomposition and MA, AR, and ARMA models
119(2)
Forecasting in ARMA models
121(1)
Parameter estimation and order determination
122(1)
Analysis of nonstationary time series
123(7)
Detrending
123(1)
An empirical example
124(4)
Transformation and differencing
128(1)
Unit-root nonstationarity and ARIMA models
129(1)
Linear state-space models and Kalman filtering
130(9)
Recursive formulas for Pt|t-1, xt|t-1, and x;t|t
131(2)
Dynamic linear models and time-varying betas in CAPM
133(2)
Exercises
135(4)
Dynamic Models of Asser Returns and Their Volatilities
139(24)
Stylized facts on time series of asset returns
140(4)
Moving average estimators of time-varying volatilities
144(2)
Conditional heteroskedastic models
146(9)
The ARCH model
146(1)
The GARCH model
147(5)
The integrated GARCH model
152(1)
The exponential GARCH model
152(3)
The ARMA-GARCH and ARMA-EGARCH models
155(8)
Forecasting future returns and volatilities
156(1)
Implementation and illustration
156(1)
Exercises
157(6)
Part II Advanced Topics in Quantitative Finance
Nonparametric Regression and Substantive-Empirical Modeling
163(18)
Regression functions and minimum-variance prediction
164(1)
Univariate predictors
165(5)
Running-mean/running-line smoothers and local polynomial regression
165(1)
Kernel smoothers
166(1)
Regression splines
166(3)
Smoothing cubic splines
169(1)
Selection of smoothing parameter
170(2)
The bias-variance trade-off
170(1)
Cross-validation
171(1)
Multivariate predictors
172(4)
Tensor product basis and multivariate adaptive regression splines
172(1)
Additive regression models
173(1)
Projection pursuit regression
174(1)
Neural networks
174(2)
A modeling approach that combines domain knowledge with nonparametric regression
176(5)
Penalized spline models and estimation of forward rates
177(1)
A semiparametric penalized spline model for the forward rate curve of corporate debt
178(1)
Exercises
179(2)
Option Pricing and Market Data
181(18)
Option prices and pricing theory
182(6)
Options data and put-call parity
182(1)
The Black-Scholes formulas for European Options
183(4)
Optimal stopping and American Options
187(1)
Implied volatility
188(4)
Alternatives to and modifications of the Black-Scholes model and pricing theory
192(7)
The implied volatility function (IVF) model
192(1)
The constant elasticity of variance (CEV) model
192(1)
The stochastic volatility (SV) model
193(1)
Nonparametric methods
194(1)
A Combined substantive-empirical approach
195(2)
Exercises
197(2)
Advanced Multivariate and Time Series Methods in Financial Econometrics
199(40)
Canonical correlation analysis
200(3)
Cross-covariance and correlation matrices
200(1)
Canonical correlations
201(2)
Multivariate regression analysis
203(2)
Least squares estimates in multivariate regression
203(1)
Reduced-rank regression
203(2)
Modified Cholesky decomposition and high-dimensional covariance matrices
205(1)
Multivaariate time series
206(11)
Stationarity and cross-correlation
206(1)
Dimension reduction via PCA
206(1)
Linear regression with stochastic regressors
207(4)
Unit-root tests
211(2)
Cointegrated VAR
213(4)
Long-memory models and regime switching/structural change
217(8)
Long memory in integrated models
217(2)
Change-point AR-GARCH models
219(5)
Regime-switching models
224(1)
Stochastic volatility and multivariate volatility models
225(4)
Stochastic volatility models
225(3)
Multivariate volatility models
228(1)
Generalized method of moments (GMM)
229(10)
Instrumental variables for linear relationships
229(2)
Generalized moment restrictions and GMM estimation
231(2)
An example: Comparison of different short-term interest rate models
233(1)
Exercises
234(5)
Interest Rate Markets
239(36)
Elements of interest rate markets
240(7)
Bank account (money market account) and short rates
241(1)
Zero-coupon bonds and spot rates
241(3)
Forward rates
244(1)
Swap rates and interest rate swaps
245(2)
Caps, floors, and swaptions
247(1)
Yield curve estimation
247(5)
Nonparametric regression using spline basis functions
248(1)
Parametric models
248(4)
Multivariate time series of bond yields and other interest rates
252(3)
Stochastic interest rates and short-rate models
255(6)
Vasicek, Cox-Ingersoll-Ross, and Hull-White models
258(1)
Bond option prices
259(1)
Black-Karasinski model
260(1)
Multifactor affine yield models
261(1)
Stochastic forward rate dynamics and pricing of LIBOR and swap rate derivatives
261(6)
Standard market formulas based on Black's model of forward prices
262(1)
Arbitrage-free pricing: martingales and numeraires
263(1)
LIBOR and swap market models
264(2)
The HJM models of the instantaneous forward rate
266(1)
Parameter estimation and model selection
267(8)
Calibrating interest rate models in the financial industry
267(3)
Econometric approach to fitting term-Structure models
270(1)
Volatility smiles and a substantive-empirical approach
271(1)
Exercises
272(3)
Statistical Trading Strategies
275(30)
Technical analysis, trading Strategies, and data-snooping checks
277(9)
Technical analysis
277(2)
Momentum and contrarian strategies
279(1)
Pairs trading strategies
279(3)
Empirical testing of the profitability of trading strategies
282(3)
Value investing and knowledge-based trading strategies
285(1)
High-frequency data, market microstructure, and associated trading strategies
286(14)
Institutional background and stylized facts about transaction data
287(4)
Bid-ask bounce and nonsynchronous trading models
291(1)
Modeling time intervals between trades
292(5)
Inference on underlying price process
297(2)
Real-time trading systems
299(1)
Transaction costs and dynamic trading
300(5)
Estimation and analysis of transaction costs
300(1)
Heterogeneous trading objectives and strategies
300(1)
Multiperiod trading and dynamic strategies
301(1)
Exercises
302(3)
Statistical Methods in Risk Management
305(20)
Financial risks and measures of market risk
306(3)
Types of financial risks
306(1)
Internal models for capital requirements
307(1)
VaR and other measures of market risk
307(2)
Statistical models for VaR and ES
309(3)
The Gaussian convention and the t-modification
309(1)
Applications of PCA and an example
310(1)
Time series models
311(1)
Backtesting VaR models
311(1)
Measuring risk for nonlinear portfolios
312(6)
Local valuation via Taylor expansions
312(2)
Full valuation via Monte Carlo
314(1)
Multivariate copula functions
314(2)
Variance reduction techniques
316(2)
Stress testing and extreme value theory
318(7)
Stress testing
318(1)
Extraordinary losses and extreme value theory
318(3)
Scenario analysis and Monte Carlo simulations
321(1)
Exercises
321(4)
Appendix A. Martingale Theory and Central Limit Theorems 325(6)
Appendix B. Limit Theorems for Stationary Processes 331(2)
Appendix C. Limit Theorems Underlying Unit-Root Tests and Cointegration 333(4)
References 337(12)
Index 349