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Stochastic Processes and Calculus: An Elementary Introduction with Applications 1st ed. 2016 [Hardback]

  • Formāts: Hardback, 391 pages, height x width: 235x155 mm, weight: 7391 g, 21 Illustrations, color; 24 Illustrations, black and white; XVIII, 391 p. 45 illus., 21 illus. in color., 1 Hardback
  • Sērija : Springer Texts in Business and Economics
  • Izdošanas datums: 18-Dec-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319234277
  • ISBN-13: 9783319234274
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  • Formāts: Hardback, 391 pages, height x width: 235x155 mm, weight: 7391 g, 21 Illustrations, color; 24 Illustrations, black and white; XVIII, 391 p. 45 illus., 21 illus. in color., 1 Hardback
  • Sērija : Springer Texts in Business and Economics
  • Izdošanas datums: 18-Dec-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319234277
  • ISBN-13: 9783319234274
This textbook gives a comprehensive introduction to stochastic processes and calculus in the fields of finance and economics, more specifically mathematical finance and time series econometrics. Over the past decades stochastic calculus and processes have gained great importance, because they play a decisive role in the modeling of financial markets and as a basis for modern time series econometrics. Mathematical theory is applied to solve stochastic differential equation and to derive limiting results for statistical inference on nonstationary processes.This introduction is elementary and rigorous at the same time. On the one hand it gives a basic and illustrative presentation of the relevant topics without using many technical derivations. On the other hand many of the procedures are presented at a technically advanced level: for a thorough understanding, they are to be proven. In order to meet both requirements jointly, the present book is equipped with a lot of challenging p

roblems at the end of each chapter as well as with the corresponding detailed solutions. Thus the virtual text - augmented with more than 60 basic examples and 40 illustrative figures - is rather easy to read while a part of the technical arguments is transferred to the exercise problems and their solutions.

Introduction.- Part I Time Series Modeling.- Basic Concepts from Probability Theory.- Autoregressive Moving Average Processes (ARMA).- Spectra of Stationary Processes.- Long Memory and Fractional Integration.- Processes with Autoregressive Conditional Heteroskedasticity (ARCH).- Part II Stochastic Integrals.- Wiener Processes (WP).- Riemann Integrals.- Stieltjes Integrals.- Ito Integrals.- Ito"s Lemma.- Part III Applications.- Stochastic Differential Equations (SDE).- Interest Rate Models.- Asymptotics of Integrated Processes.- Trends, Integration Tests and Nonsense Regressions.- Cointegration Analysis.

Recenzijas

The book is quite readable and can be used as a textbook for the application of mathematical theory in the area of econometrics. Also, a mathematician might benefit from an intuitive exposition of some different and specific types of integration appearing in the theory of stochastic processes. The book might then serve as starting point for a more detailed study of the mathematical foundation of the topics presented. (Ludger Overback, Mathematical Reviews, October, 2016)

The book covers both discrete and continuous time stochastic processes, and it is of course in the second area where mathematical intricacies abound. All this is very much up to date and provides a most useful introduction to modern time series methods for anybody wishing to understand the mechanics without having to dig too deep into the mathematical foundations. (Walter Krämer, Statistics Papers, Vol. 57, 2016)

The construction of this book is based on the author experience of 15 years of teaching stochastic processes and calculus. book is therefore a very successful work on the task of providing the largest number of readers an introduction to stochastic processes and calculus simultaneously accessible and rigorous, with a wide exemplification of applications in various fields. Very important for readers in the fields of mathematics, finance and econometrics and also in biology, engineering or physics, but not only. (Prof. Dr. Manuel Alberto M. Ferreira, Acta Scientiae et Intellectus, Vol. 2 (2), 2016)

1 Introduction
1(12)
1.1 Summary
1(1)
1.2 Finance
1(2)
1.3 Econometrics
3(3)
1.4 Mathematics
6(1)
1.5 Problems and Solutions
7(6)
References
10(3)
Part I Time Series Modeling
2 Basic Concepts from Probability Theory
13(32)
2.1 Summary
13(1)
2.2 Random Variables
13(9)
2.3 Joint and Conditional Distributions
22(7)
2.4 Stochastic Processes (SP)
29(6)
2.5 Problems and Solutions
35(10)
References
42(3)
3 Autoregressive Moving Average Processes (ARMA)
45(32)
3.1 Summary
45(1)
3.2 Moving Average Processes
45(6)
3.3 Lag Polynomials and Invertibility
51(5)
3.4 Autoregressive and Mixed Processes
56(12)
3.5 Problems and Solutions
68(9)
References
75(2)
4 Spectra of Stationary Processes
77(26)
4.1 Summary
77(1)
4.2 Definition and Interpretation
77(7)
4.3 Filtered Processes
84(5)
4.4 Examples of ARMA Spectra
89(6)
4.5 Problems and Solutions
95(8)
References
101(2)
5 Long Memory and Fractional Integration
103(24)
5.1 Summary
103(1)
5.2 Persistence and Long Memory
103(5)
5.3 Fractionally Integrated Noise
108(5)
5.4 Generalizations
113(5)
5.5 Problems and Solutions
118(9)
References
125(2)
6 Processes with Autoregressive Conditional Heteroskedasticity (ARCH)
127(24)
6.1 Summary
127(1)
6.2 Time-Dependent Heteroskedasticity
127(3)
6.3 ARCH Models
130(5)
6.4 Generalizations
135(7)
6.5 Problems and Solutions
142(9)
References
148(3)
Part II Stochastic Integrals
7 Wiener Processes (WP)
151(28)
7.1 Summary
151(1)
7.2 From Random Walk to Wiener Process
151(6)
7.3 Properties
157(4)
7.4 Functions of Wiener Processes
161(9)
7.5 Problems and Solutions
170(9)
References
177(2)
8 Riemann Integrals
179(20)
8.1 Summary
179(1)
8.2 Definition and Fubini's Theorem
179(4)
8.3 Riemann Integration of Wiener Processes
183(3)
8.4 Convergence in Mean Square
186(4)
8.5 Problems and Solutions
190(9)
References
197(2)
9 Stieltjes Integrals
199(14)
9.1 Summary
199(1)
9.2 Definition and Partial Integration
199(3)
9.3 Gaussian Distribution and Autocovariances
202(2)
9.4 Standard Ornstein-Uhlenbeck Process
204(3)
9.5 Problems and Solutions
207(6)
Reference
211(2)
10 Ito Integrals
213(26)
10.1 Summary
213(1)
10.2 A Special Case
213(5)
10.3 General Ito Integrals
218(4)
10.4 (Quadratic) Variation
222(7)
10.5 Problems and Solutions
229(10)
References
237(2)
11 Ito's Lemma
239(22)
11.1 Summary
239(1)
11.2 The Univariate Case
239(6)
11.3 Bivariate Diffusions with One WP
245(5)
11.4 Generalization for Independent WP
250(4)
11.5 Problems and Solutions
254(7)
Reference
258(3)
Part III Applications
12 Stochastic Differential Equations (SDE)
261(24)
12.1 Summary
261(1)
12.2 Definition and Existence
261(4)
12.3 Linear Stochastic Differential Equations
265(7)
12.4 Numerical Solutions
272(1)
12.5 Problems and Solutions
273(12)
References
282(3)
13 Interest Rate Models
285(18)
13.1 Summary
285(1)
13.2 Ornstein-Uhlenbeck Process (OUP)
285(3)
13.3 Positive Linear Interest Rate Models
288(4)
13.4 Nonlinear Models
292(4)
13.5 Problems and Solutions
296(7)
References
302(1)
14 Asymptotics of Integrated Processes
303(28)
14.1 Summary
303(1)
14.2 Limiting Distributions of Integrated Processes
303(7)
14.3 Weak Convergence of Functions
310(7)
14.4 Multivariate Limit Theory
317(4)
14.5 Problems and Solutions
321(10)
References
329(2)
15 Trends, Integration Tests and Nonsense Regressions
331(22)
15.1 Summary
331(1)
15.2 Trend Regressions
331(5)
15.3 Integration Tests
336(5)
15.4 Nonsense Regression
341(3)
15.5 Problems and Solutions
344(9)
References
352(1)
16 Cointegration Analysis
353(30)
16.1 Summary
353(1)
16.2 Error-Correction and Cointegration
353(5)
16.3 Cointegration Regressions
358(7)
16.4 Cointegration Testing
365(8)
16.5 Problems and Solutions
373(10)
References
381(2)
References 383(6)
Index 389
Uwe Hassler studied mathematics and economics at Freie Universität Berlin and specialized in statistics and econometrics at the London School of Economics. He completed his doctoral studies in 1993 at Freie Universität. Hassler published in leading field journals such as Econometric Theory, Journal of Econometrics and Journal of Time Series Analysis. His main research interests are within the field of time series analysis. Since 2003 he is Professor of Statistics and Econometric Methods at Goethe University Frankfurt, Germany. Prior to joining Goethe University he held permanent or visiting positions at leading universities in Darmstadt, Munich and Muenster (Germany), and in Madrid (Spain). He has been teaching stochastic processes and calculus for 15 years.