Financial Time Series Analysis: 3rd

Financial Time Series Analysis: 3rd
Basic Information
Original Title: Analysis of Financial Time Series Third Edition
Author: (MEI) Cai Rui chest (tsay, R. S.) [Translator's introduction]
Translator: Wang yuanlin Wang Hui Pan jiazhu
Series name: Turing mathematics. Statistics Series
Press: People's post and telecommunications Press
ISBN: 9787115287625
Mounting time:
Published on: February 1, August 2012
Start: 16
Page number: 1
Version: 1-1
Category: Mathematics

For more information, financial time series analysis: Version 3rd
Introduction
Books
Mathematics books
Financial Time Series Analysis: 3rd, in particular, risk value calculation, high-frequency data analysis, random fluctuation modeling, and Markov Chain Monte Carlo method. In addition, this book also systematically describes the financial metering Economy Model and Its Application in financial time series data and modeling. All models and methods use actual financial data, the command for the computer software used is provided. Compared with version 2nd, this version not only updates the data used in the previous version, but also provides R commands and instances, making it a cornerstone for understanding important statistical methods and technologies.
Financial Time Series Analysis: Version 3rd can be used as a teaching material for time series analysis. It is also applicable to senior undergraduates and graduate students interested in financial metering economics in the business, economics, mathematics, and statistics fields, it can also be used as a reference book for professionals in the business, finance, insurance and other fields.
Directory
Financial Time Series Analysis: 3rd
Chapter 1 Financial Time Series and features 1
1.1 asset return rate 2
1.2 distribution of yield 6
1.2.1 Statistical Distribution and moment review 6
1.2.2 distribution of return rate 13
1.2.3 multiple return rate 16
1.2.4 likelihood function 17
1.2.5 empirical nature of return rate 17
1.3 Other processes 19
Appendix R package 21
Exercise Question 23
References 24
Chapter 1 Linear Time Series Analysis and Its Application 25
2.1 stability 25
2.2 correlation coefficient and self-correlation function 26
2.3 white noise and Linear Time Series 31
2.4 simple Autoregressive Model 32
2.4.1 properties of the AR model 33
2.4.2 how to identify the AR model in practice 40
2.4.3 goodness of fit 46
2.4.4 prediction 47
2.5 simple moving average model 50
2.5.1 MA model nature 51
2.5.2 identify the level 52 of Ma
2.5.3 estimated 53
2.5.4 prediction with Ma model 54
2.6 simple ARMA Model 55
2.6.1 properties of the ARMA () model 56
2.6.2 General ARMA Model 57
2.6.3 recognition of ARMA model 58
2.6.4 use the ARMA Model for prediction 60
2.6.5 Three Representations of the ARMA Model 60
2.7 unit root non-stability 62
2.7.1 Random Walk 62
2.7.2 Random Walk with drift 64
2.7.3 time series with trend items 65
2.7.4 general unit root non-stable model 66
2.7.5 Unit Root Test 66
2.8 seasonal model 71
2.8.1 seasonal differentiation 72
2.8.2 multiple seasonal models 73
2.9 regression model with time series error 78
2.10 covariance matrix coincidence estimation 85
2.11 long memory model 88
Appendix some SCA commands 90
Exercise question 90
References 92
Chapter 4 conditional Differential Model 94
3.1 volatility feature 95
3.2 model structure 95
3.3 modeling 97
3.4 ARCH Model 99
3.4.1 nature of ARCH model 100
3.4.2 ARCH Model disadvantages: 102
3.4.3 ARCH Model Establishment 102
3.4.4 examples 106
3.5 Gbit/s model 113
3.5.1 instance description 115
3.5.2 evaluation of prediction 120
3.5.3 two-step estimation method 121
3.6 sum-up model 121
3.7 Gbit/s-M model 122
3.8 index, Gini model, 123
3.8.1 another model type 125
3.8.2 instance description 125
3.8.3 other example 126
3.8.4 use EGARCH Model for prediction 128
3.9 threshold. 129.
3.10 charma Model 130
3.11 auto-regression model with random coefficient 132
3.12 random fluctuation model 133
3.13 long memory random fluctuation model 133
3.14 applications 135
3.15 Other Methods 138
3.15.1 application of high-frequency data 138
3.15.2 opening date, maximum price, minimum price, and closing price 141
3.16. The peak of the model is 143.
Appendix some rats procedures in volatility model estimation 144
Exercise 146
References 148
Chapter 2 Nonlinear Models and Their Applications 4th
4.1 Non-Linear Model 152
4.1.1 bilinear model 153
4.1.2 Threshold Auto-regression model 154
4.1.3 smooth transfer of AR (STAR) model 158
4.1.4 Markov transformation model 160
4.1.5 non-parameter method 162
4.1.6 function Coefficient AR model 170
4.1.7 nonlinear AR Model Addition 170
4.1.8 nonlinear state space model 171
4.1.9 Neural Network 171
4.2 nonlinear test 176
4.2.1 non-parameter test 176
4.2.2 parameter test 179
4.2.3 app 182
4.3 modeling 183
4.4 prediction 184
4.4.1 Parameter Self-Help method 184
4.4.2 evaluation of prediction 184
4.5 Applications 186
Appendix A rats program for some non-linear fluctuation models 190
Appendix B neural network S-plus command 191
Exercise 191
References 193
Chapter 2 high-frequency data analysis and market microstructure 5th
5.1 non-synchronous transactions 196
5.2 sales quotation difference 200
5.3 empirical characteristics of transaction data 201
5.4 price change model 207
5.4.1 sequence probability value model 207
5.4.2 decomposition model 210
5.5 Duration Model 214
5.5.1 ACD model 216
5.5.2 simulated 218
5.5.3 estimated 219
5.6 nonlinear persistence model 224
5.7 binary model of price change and duration 225
5.8 applications 229
Review of some probability distributions in Appendix A 234
Appendix B risk rate function 237
Appendix C: some rats for the Duration Model
Program 238
Exercise 239
References 241
Chapter 1 Continuous Time Model and Its Application 6th
6.1 option 244
6.2 some consecutive random processes 244
6.2.1 Vina process 244
6.2.2 generalized veninder process 246
6.2.3 Ito Process 247
6.3 Ito theorem 247
6.3.1 differential review 247
6.3.2 Random differential 248
6.3.3 one application 249
6.3.4 1 and? Estimated 250
6.4 distribution of stock price and logarithm return rate: 251
6.5 derivation of B-s differential equations 253
6.6 B-s pricing formula 254
6.6.1 risk-neutral world 254
6.6.2 formula 255
6.6.3 lower limit of European option 257
6.6.4 discussion 258
6.7 extended Ito's theorem 261
6.8 random points 262
6.9 skip Diffusion Model 263
6.10 estimation of Continuous Time Model 269
Appendix a B-s formula credit 270
Appendix B: an approximate 271 of the standard normal probability
Exercise 271
References 272
Chapter 1 Extreme Value Theory, quantile estimation and risk value 7th
7.1 risk value 275
7.2 risk measurement 276
7.2.1 discuss 279
7.2.2 more than 279 positions
7.2.3 expected loss: 280
7.3 metering and economic method of VaR Calculation 280
7.3.1 283 for multiple cycles
7.3.2 expected loss in normal condition distribution: 285
7.4 quantile estimate 285
7.4.1 quantile and Order Statistic 285
7.4.2 quantile regression 287
7.5 Extreme Value Theory 288
7.5.1 Review of Extreme Value Theory 288
7.5.2 estimated experience: 290
7.5.3 application of 293 to the Stock Return Rate
7.6 var Extreme Value Method 297
7.6.1 discussion 300
7.6.2 multi-period var 301
7.6.3 yield level: 302
7.7 A New Method Based on Extreme Value Theory 302
7.7.1 statistical theory 303
7.7.2 The excess mean function is 305.
7.7.3 a new method for extreme value modeling 306
7.7.4 new method-Based VaR Calculation 308
7.7.5 other parameterized Methods 309
7.7.6 use of interpretation variables 312
7.7.7 Model Test 313
7.7.8 description 314
7.8 extreme value index 318
7.8.1 D (un) condition 319
7.8.2 estimation of the extreme value index 321
7.8.3 risk value of a stable time series: 323
Exercise 324
References 326
Chapter 1 Multivariate Time Series Analysis and Its Application 8th
8.1 Weak Stability and crossover {correlation matrix 328
8.1.1 crossover {correlation matrix 329
8.1.2 linear dependency 330
8.1.3 sample crossover {correlation matrix 331
8.1.4 Multi-Element mixing test 335
8.2 vector autoregressive model 336
8.2.1 simplified form and structure form 337
8.2.2 smoothing condition and moment 339 of VaR (1) Model
8.2.3 vector AR (p) model: 340
8.2.4 create a VAR (p) model 342
8.2.5 pulsed response function 349
8.3 vector Moving Average Model 354
8.4 Vector ARMA Model 357
8.5 unit root non-stability and Coordination 362
8.6 covariance VAR model 366
8.6.1 concrete description of deterministic functions 368
8.6.2 Maximum Likelihood Estimation 368
8.6.3 conformity test 369
8.6.4 prediction of the covariance VAR model 370
8.6.5 example 370
8.7 threshold coordination and arbitrage 375
8.7.1 multivariate threshold model 376
8.7.2 377 data
8.7.3 estimated 377
8.8 paired transactions 379
8.8.1 theoretical framework 379
8.8.2 transaction policy 380
8.8.3 simple example 380
Appendix A vector and matrix 385
Appendix B Multivariate Normal Distribution: 389
Appendix C some SCA command 390
Exercise 391
References 393
Chapter 1 Principal Component Analysis and Factor Model 9th
9.1 Factor Model 395
9.2 macroeconomic Factor Model 397
9.2.1 Single Factor Model 397
9.2.2 multi-factor model 401
9.3 fundamental factor model 403
9.3.1 Barra Factor Model 403
9.3.2 Fama-French method 408
9.4 Principal Component Analysis 408
9.4.1 PCA theory 408
9.4.2 experienced PCA 410
9.5 statistical factor analysis 413
9.5.1 estimated 414
9.5.2 Rotating Factor 415
9.5.3 app 416
9.6 progressive Principal Component Analysis 420
9.6.1 factor count: 421
9.6.2 example 422
Exercise 424
References 425
Chapter 1 multivariate fluctuation Model and Its Application 10th
10.1 exponential weighted estimate 427
10.2 multi-dimensional model: 429
10.2.1 diagonal VEC model 430
10.2.2 Bekk models 432
10.3 re-parameterization 435
10.3.1 application of correlation coefficient 435
10.3.2 Cholesky Decomposition 436
10.4 Dual Rate of Return of 439.
10.4.1 common correlation model 439
10.4.2 time-varying model 442
10.4.3 dynamic model 446
10.5 higher-dimensional fluctuation model 452
10.6 factor fluctuation model 457
10.7 applications 459
10.8 TB distribution 461
Appendix comments to estimates 462
Exercise 466
References 467
Chapter 2 state space model and Kalman Filter 11th
11.1 local trend model 469
11.1.1 Statistical Inference 472
11.1.2 Kalman Filter 473
11.1.3 nature of prediction error 475
11.1.4 smooth 476
11.1.5 missing 480
11.1.6 initialization 480
11.1.7 estimated 481
The S-plus command 482 used in 11.1.8
11.2 linear state space model 485
11.3 model conversion 486
11.3.1 CAPM 487 with Time-Varying Coefficients
11.3.2 ARMA Model 489
11.3.3 Linear Regression Model 495
11.3.4 linear regression model with ARMA error 496
11.3.5 Model of Non-observed items in a pure Volume 497
11.4 Kalman filtering and smoothing 499
11.4.1 Kalman Filter 499
11.4.2 state estimation error and prediction error 501
11.4.3 smooth 502
11.4.4 disturbance smoothing 504
11.5 missing value 506
11.6 prediction 507
11.7 applications 508
Exercise 515
References 516
Chapter 1 Markov Chain Monte Carlo Method and Its Application 12th
12.1 Markov Chain simulation 517
12.2 sample of 518 jobs
12.3 Bayesian inference 520
12.3.1 posterior distribution 520
12.3.2 prior distribution of condensed values: 521
12.4 other algorithms 524
12.4.1 Metropolis algorithm 524
12.4.2 Metropolis-Hasting algorithm 525
12.4.3 sample of lattice garms, 525
12.5 linear regression with Time Series Error 526
12.6 missing value and Abnormal Value 530
12.6.1 missing value: 531
12.6.2 abnormal value recognition 532
12.7 random fluctuation model 537
12.7.1 estimation of the mona1 model: 537
12.7.2 multivariate random fluctuation model 542
12.8 new method for estimating the random fluctuation model 549
12.9 Markov transformation model 556
12.10 prediction 563
12.11 other applications 564
Exercise 564
References 565
Index 568

This book is from: China Interactive publishing network