Normal view MARC view

Analysis of financial time series

Author: Tsay, Ruey S. Series: Wiley series in probability and statistics Publisher: Wiley, 2005.Edition: 2nd ed.Language: EnglishDescription: 605 p. : Graphs ; 24 cm.ISBN: 0471690740 ; 9780471690740Type of document: BookBibliography/Index: Includes bibliographical references and index
Tags: No tags from this library for this title. Log in to add tags.
Item type Current location Collection Call number Status Date due Barcode Item holds
Book Europe Campus
Main Collection
Print HB139 .T739 2005
(Browse shelf)
001228315
Available 001228315
Total holds: 0

Includes bibliographical references and index

Digitized

Analyis of Financial Time Series Contents Preface Preface to First Edition 1. Financial Time Series and Their Characteristics Asset Returns, 2 Distributional Properties of Returns, 7 1.2.1 Review of Statistical Distributions and Their Moments, 7 1.2.2 Distributions of Returns, 13 1.2.3 Multivariate Returns, 16 1.2.4 Likelihood Function of Returns, 17 1.2.5 Empirical Properties of Returns, 17 1.3 Processes Considered, 20 Exercises, 22 References, 23 2. Linear Time Series Analysis and Its Applications 2.1 2.2 2.3 2.4 Stationarity, 25 Correlation and Autocorrelation Function, 25 White Noise and Linear Time Series, 31 Simple Autoregressive Models, 32 2.4.1 Properties of AR Models, 33 2.4.2 Identifying AR Models in Practice, 40 2.4.3 Goodness of Fit, 46 2.4.4 Forecasting, 47 1.1 1.2 xvii xix 1 24 Simple Moving-Average Models, 50 2.5.1 Properties of MA Models, 51 2.5.2 Identifying MA Order, 52 2.5.3 Estimation, 53 2.5.4 Forecasting Using MA Models, 54 2.6 Simple ARMA Models, 56 2.6.1 Properties of ARMA(1,1) Models, 57 2.6.2 General ARMA Models, 58 2.6.3 Identifying ARMA Models, 59 2.6.4 Forecasting Using an ARMA Model, 61 2.6.5 Three Model Representations for an ARMA Model, 62 2.7 Unit-Root Nonstationarity, 64 2.7.1 Random Walk, 64 2.7.2 Random Walk with Drift, 65 2.7.3 Trend-Stationary Time Series, 67 2.7.4 General Unit-Root Nonstationary Models, 67 2.7.5 Unit-Root Test, 68 2.8 Seasonal Models, 72 2.8.1 Seasonal Differencing, 73 2.8.2 Multiplicative Seasonal Models, 75 2.9 Regression Models with Time Series Errors, 80 2.10 Consistent Covariance Matrix Estimation, 86 2.11 Long-Memory Models, 89 Appendix: Some SCA Commands, 91 Exercises, 93 References, 96 3. Conditional Heteroscedastic Models 3.1 3.2 3.3 Characteristics of Volatility, 98 Structure of a Model, 99 Model Building, 101 3.3.1 Testing for ARCH Effect, 101 3.4 The ARCH Model, 102 3.4.1 Properties of ARCH Models, 104 3.4.2 Weaknesses of ARCH Models, 106 3.4.3 Building an ARCH Model, 106 3.4.4 Some Examples, 109 3.5 The GARCH Model, 113 3.5.1 An Illustrative Example, 116 97 2.5 3.5.2 Forecasting Evaluation, 121 3.5.3 A Two-Pass Estimation Method, 121 3.6 The Integrated GARCH Model, 122 3.7 The GARCH-M Model, 123 3.8 The Exponential GARCH Model, 124 3.8.1 An Alternative Model Form, 125 3.8.2 An Illustrative Example, 126 3.8.3 Second Example, 126 3.8.4 Forecasting Using an EGARCH Model, 128 3.9 The Threshold GARCH Model, 130 3.10 The CHARMA Model, 131 3.10.1 Effects of Explanatory Variables, 133 3.11 Random Coefficient Autoregressive Models, 133 3.12 The Stochastic Volatility Model, 134 3.13 The Long-Memory Stochastic Volatility Model, 134 3.14 Application, 136 3.15 Alternative Approaches, 140 3.15.1 Use of High-Frequency Data, 140 3.15.2 Use of Daily Open, High, Low, and Close Prices, 143 3.16 Kurtosis of GARCH Models, 145 Appendix: Some RATS Programs for Estimating Volatility Models, 147 Exercises, 148 References, 151 4. Nonlinear Models and Their Applications 4.1 Nonlinear Models, 156 4.1.1 Bilinear Model, 156 4.1.2 Threshold Autoregressive (TAR) Model, 157 4.1.3 Smooth Transition AR (STAR) Model, 163 4.1.4 Markov Switching Model, 164 4.1.5 Nonparametric Methods, 167 4.1.6 Functional Coefficient AR Model, 175 4.1.7 Nonlinear Additive AR Model, 176 4.1.8 Nonlinear State-Space Model, 176 4.1.9 Neural Networks, 177 Nonlinearity Tests, 183 4.2.1 Nonparametric Tests, 183 4.2.2 Parametric Tests, 186 4.2.3 Applications, 190 154 4.2 4.3 4.4 Modeling, 191 Forecasting, 192 4.4.1 Parametric Bootstrap, 192 4.4.2 Forecasting Evaluation, 192 4.5 Application, 194 Appendix A: Some RATS Programs for Nonlinear Volatility Models, 199 Appendix B: S-Plus Commands for Neural Network, 200 Exercises, 200 References, 202 5. High-Frequency Data Analysis and Market Microstructure 5.1 5.2 5.3 5.4 Nonsynchronous Trading, 207 Bid­Ask Spread, 210 Empirical Characteristics of Transactions Data, 212 Models for Price Changes, 218 5.4.1Ordered Probit Model, 218 5.4.2 A Decomposition Model, 221 Duration Models, 225 206 5.5 5.5.1 The ACD Model, 227 5.5.2 Simulation, 229 5.5.3 Estimation, 232 5.6 Nonlinear Duration Models, 236 5.7 Bivariate Models for Price Change and Duration, 237 Appendix A: Review of Some Probability Distributions, 242 Appendix B: Hazard Function, 245 Appendix C: Some RATS Programs for Duration Models, 246 Exercises, 248 References, 250 6. Continuous-Time Models and Their Applications 6.1 6.2 Options, 252 Some Continuous-Time Stochastic Processes, 252 6.2.1 The Wiener Process, 253 6.2.2 Generalized Wiener Processes, 255 6.2.3 Ito Processes, 256 Ito's Lemma, 256 6.3.1 Review of Differentiation, 256 6.3.2 Stochastic Differentiation, 257 251 6.3 6.3.3 An Application, 258 6.3.4 Estimation of and , 259 6.4 Distributions of Stock Prices and Log Returns, 261 6.5 Derivation of Black­Scholes Differential Equation, 262 6.6 Black­Scholes Pricing Formulas, 264 6.6.1 Risk-Neutral World, 264 6.6.2 Formulas, 264 6.6.3 Lower Bounds of European Options, 267 6.6.4 Discussion, 268 6.7 An Extension of Ito's Lemma, 272 6.8 Stochastic Integral, 273 6.9 Jump Diffusion Models, 274 6.9.1 Option Pricing Under Jump Diffusion, 279 6.10 Estimation of Continuous-Time Models, 282 Appendix A: Integration of Black­Scholes Formula, 282 Appendix B: Approximation to Standard Normal Probability, 284 Exercises, 284 References, 285 7. Extreme Values, Quantile Estimation, and Value at Risk Value at Risk, 287 RiskMetrics, 290 7.2.1 Discussion, 293 7.2.2 Multiple Positions, 293 7.3 An Econometric Approach to VaR Calculation, 294 7.3.1 Multiple Periods, 296 7.4 Quantile Estimation, 298 7.4.1 Quantile and Order Statistics, 299 7.4.2 Quantile Regression, 300 7.5 Extreme Value Theory, 301 7.5.1 Review of Extreme Value Theory, 301 7.5.2 Empirical Estimation, 304 7.5.3 Application to Stock Returns, 307 7.6 Extreme Value Approach to VaR, 311 7.6.1 Discussion, 314 7.6.2 Multiperiod VaR, 316 7.6.3 VaR for a Short Position, 316 7.6.4 Return Level, 317 7.1 7.2 287 7.7 A New Approach Based on the Extreme Value Theory, 318 7.7.1 Statistical Theory, 318 7.7.2 Mean Excess Function, 320 7.7.3 A New Approach to Modeling Extreme Values, 322 7.7.4 VaR Calculation Based on the New Approach, 324 7.7.5 An Alternative Parameterization, 325 7.7.6 Use of Explanatory Variables, 328 7.7.7 Model Checking, 329 7.7.8 An Illustration, 330 Exercises, 335 References, 337 8. Multivariate Time Series Analysis and Its Applications 8.1 Weak Stationarity and Cross-Correlation Matrices, 340 8.1.1 Cross-Correlation Matrices, 340 8.1.2 Linear Dependence, 341 339 8.1.3 Sample Cross-Correlation Matrices, 342 8.1.4 Multivariate Portmanteau Tests, 346 8.2 Vector Autoregressive Models, 349 8.2.1 Reduced and Structural Forms, 349 8.2.2 Stationarity Condition and Moments of a VAR(1) Model, 351 8.2.3 Vector AR(p) Models, 353 8.2.4 Building a VAR(p) Model, 354 8.2.5 Impulse Response Function, 362 8.3 Vector Moving-Average Models, 365 8.4 Vector ARMA Models, 371 8.4.1 Marginal Models of Components, 375 8.5 Unit-Root Nonstationarity and Cointegration, 376 8.5.1 An Error-Correction Form, 379 8.6 Cointegrated VAR Models, 380 8.6.1 Specification of the Deterministic Function, 382 8.6.2 Maximum Likelihood Estimation, 383 8.6.3 A Cointegration Test, 384 8.6.4 Forecasting of Cointegrated VAR Models, 385 8.6.5 An Example, 385 Threshold Cointegration and Arbitrage, 390 8.7.1 Multivariate Threshold Model, 391 8.7.2 The Data, 392 8.7 8.7.3 Estimation, 393 Appendix A: Review of Vectors and Matrices, 395 Appendix B: Multivariate Normal Distributions, 399 Appendix C: Some SCA Commands, 400 Exercises, 401 References, 402 9. Principal Component Analysis and Factor Models 9.1 A Factor Model, 406 9.2 Macroeconometric Factor Models, 407 9.2.1 A Single-Factor Model, 408 9.2.2 Multifactor Models, 412 9.3 Fundamental Factor Models, 414 9.3.1 BARRA Factor Model, 414 9.3.2 Fama­French Approach, 420 9.4 Principal Component Analysis, 421 9.4.1 Theory of PCA, 421 9.4.2 Empirical PCA, 422 9.5 Statistical Factor Analysis, 426 9.5.1 Estimation, 428 9.5.2 Factor Rotation, 429 9. 5.3 Applications, 430 9.6 Asymptotic Principal Component Analysis, 436 9.6.1 Selecting the Number of Factors, 437 9.6.2 An Example, 437 Exercises, 440 References, 441 10. Multivariate Volatility Models and Their Applications 10.1 Exponentially Weighted Estimate, 444 10.2 Some Multivariate GARCH Models, 447 10.2.1 Diagonal VEC Model, 447 10.2.2 BEKK Model, 451 10.3 Reparameterization, 454 10.3.1 Use of Correlations, 454 10.3.2 Cholesky Decomposition, 455 10.4 GARCH Models for Bivariate Returns, 459 10.4.1 Constant-Correlation Models, 459 10.4.2 Time-Varying Correlation Models, 464 443 405 10.4.3 Some Recent Developments, 470 10.5 Higher Dimensional Volatility Models, 471 10.6 Factor­Volatility Models, 477 10.7 Application, 480 10.8 Multivariate t Distribution, 482 Appendix: Some Remarks on Estimation, 483 Exercises, 488 References, 489 11. State-Space Models and Kalman Filter 11.1 Local Trend Model, 490 11.1.1 Statistical Inference, 493 11.1.2 Kalman Filter, 495 11.1.3 Properties of Forecast Error, 496 11.1.4 State Smoothing, 498 11.1.5 Missing Values, 501 11.1.6 Effect of Initialization, 503 11.1.7 Estimation, 504 11.1.8 S-Plus Commands Used, 505 11.2 Linear State-Space Models, 508 11.3 Model Transformation, 509 11.3.1 CAPM with Time-Varying Coefficients, 510 11.3.2 ARMA Models, 512 11.3.3 Linear Regression Model, 518 11.3.4 Linear Regression Models with ARMA Errors, 519 11.3.5 Scalar Unobserved Component Model, 521 11.4 Kalman Filter and Smoothing, 523 11.4.1 Kalman Filter, 523 11.4.2 State Estimation Error and Forecast Error, 525 11.4.3 State Smoothing, 526 11.4.4 Disturbance Smoothing, 528 11.5 Missing Values, 531 11.6 Forecasting, 532 11.7 Application, 533 Exercises, 540 References, 541 490 12. Markov Chain Monte Carlo Methods with Applications 12.1 Markov Chain Simulation, 544 12.2 Gibbs Sampling, 545 12.3 Bayesian Inference, 547 12.3.1 Posterior Distributions, 547 12.3.2 Conjugate Prior Distributions, 548 12.4 Alternative Algorithms, 551 12.4.1 Metropolis Algorithm, 551 12.4.2 Metropolis--Hasting Algorithm, 552 12.4.3 Griddy Gibbs, 552 12.5 Linear Regression with Time Series Errors, 553 12.6 Missing Values and Outliers, 558 12.6.1 Missing Values, 559 12.6.2 Outlier Detection, 561 12.7 Stochastic Volatility Models, 565 12.7.1 Estimation of Univariate Models, 566 12.7.2 Multivariate Stochastic Volatility Models, 571 12.8 A New Approach to SV Estimation, 578 12.9 Markov Switching Models, 588 12.10 Forecasting, 594 12.11 Other Applications, 597 Exercises, 597 References, 598 Index 543 601

There are no comments for this item.

Log in to your account to post a comment.
Koha 18.11 - INSEAD Catalogue
Home | Contact Us | What's Koha?