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Statistics of Financial Markets: An Introduction 4th ed. 2015 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 555 pages, height x width: 235x155 mm, weight: 8599 g, 114 Illustrations, color; 49 Illustrations, black and white; XIX, 555 p. 163 illus., 114 illus. in color., 1 Paperback / softback
  • Sērija : Universitext
  • Izdošanas datums: 11-Feb-2015
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642545386
  • ISBN-13: 9783642545382
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Statistics of Financial Markets: An Introduction 4th ed. 2015Palielināt
  • Formāts: Paperback / softback, 555 pages, height x width: 235x155 mm, weight: 8599 g, 114 Illustrations, color; 49 Illustrations, black and white; XIX, 555 p. 163 illus., 114 illus. in color., 1 Paperback / softback
  • Sērija : Universitext
  • Izdošanas datums: 11-Feb-2015
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642545386
  • ISBN-13: 9783642545382
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783642545382.html
Keywords:
This book introduces statistical application in finance, with methods of evaluating option contracts, analyzing financial time series, choosing portfolios and managing risks. The 4th edition offers new chapters on long memory models, copulae and CDO valuation.

Now in its fourth edition, this book offers a detailed yet concise introduction to the growing field of statistical applications in finance. The reader will learn the basic methods of evaluating option contracts, analyzing financial time series, selecting portfolios and managing risks based on realistic assumptions about market behavior. The focus is both on the fundamentals of mathematical finance and financial time series analysis, and on applications to given problems concerning financial markets, thus making the book the ideal basis for lectures, seminars and crash courses on the topic.

For this new edition the book has been updated and extensively revised and now includes several new aspects, e.g. new chapters on long memory models, copulae and CDO valuation. Practical exercises with solutions have also been added. Both R and Matlab Code, together with the data, can be downloaded from the book’s product page and www.quantlet.de

Recenzijas

This book provides an excellent introduction to the tools from probability and statistics necessary to analyze financial data. Clearly written and accessible, it will be very useful to students and practitioners alike"

Yacine Aļt-Sahalia, Otto Hack 1903 Professor of Finance and Economics, Princeton University

Part I Option Pricing
1 Derivatives
3(8)
1.1 Recommended Literature
9(1)
1.2 Exercises
9(2)
2 Introduction to Option Management
11(26)
2.1 Arbitrage Relations
11(10)
2.2 Portfolio Insurance
21(6)
2.3 Binary One-Period Model
27(5)
2.4 Recommended Literature
32(1)
2.5 Exercises
32(5)
3 Basic Concepts of Probability Theory
37(12)
3.1 Real Valued Random Variables
37(3)
3.2 Expectation and Variance
40(1)
3.3 Skewness and Kurtosis
41(1)
3.4 Random Vectors, Dependence, Correlation
42(1)
3.5 Conditional Probabilities and Expectations
43(2)
3.6 Recommended Literature
45(1)
3.7 Exercises
45(4)
4 Stochastic Processes in Discrete Time
49(10)
4.1 Binomial Processes
49(4)
4.2 Trinomial Processes
53(1)
4.3 General Random Walks
54(1)
4.4 Geometric Random Walks
55(2)
4.5 Binomial Models with State Dependent Increments
57(1)
4.6 Recommended Literature
57(1)
4.7 Exercises
58(1)
5 Stochastic Integrals and Differential Equations
59(16)
5.1 Wiener Process
59(4)
5.2 Stochastic Integration
63(2)
5.3 Stochastic Differential Equations
65(3)
5.4 The Stock Price as a Stochastic Process
68(2)
5.5 Ito's Lemma
70(3)
5.6 Recommended Literature
73(1)
5.7 Exercises
73(2)
6 Black—Scholes Option Pricing Model
75(46)
6.1 Black—Scholes Differential Equation
75(7)
6.2 Black—Scholes Formula for European Options
82(6)
6.2.1 Numerical Approximation
86(2)
6.3 Simulation
88(11)
6.3.1 Linear Congruential Generator
89(4)
6.3.2 Fibonacci Generators
93(2)
6.3.3 Inversion Method
95(1)
6.3.4 Box—Muller Method
96(1)
6.3.5 Marsaglia Method
97(2)
6.4 Risk Management and Hedging
99(15)
6.4.1 Delta Hedging
101(3)
6.4.2 Gamma and Theta
104(3)
6.4.3 Rho and Vega
107(1)
6.4.4 Volga and Vanna
108(2)
6.4.5 Historical and Implied Volatility
110(3)
6.4.6 Realised Volatility
113(1)
6.5 Recommended Literature
114(1)
6.6 Exercises
114(7)
7 Binomial Model for European Options
121(12)
7.1 Cox—Ross—Rubinstein Approach to Option Pricing
122(3)
7.2 Discrete Dividends
125(5)
7.2.1 Dividends as a Percentage of the Stock Price
127(1)
7.2.2 Dividends as a Fixed Amount of Money
128(2)
7.3 Recommended Literature
130(1)
7.4 Exercises
130(3)
8 American Options
133(14)
8.1 Arbitrage Relations for American Options
133(7)
8.2 The Trinomial Model
140(4)
8.3 Recommended Literature
144(1)
8.4 Exercises
144(3)
9 Exotic Options
147(14)
9.1 Compound Options, Option on Option
148(2)
9.2 Chooser Options or "As You Wish" Options
150(1)
9.3 Barrier Options
150(2)
9.4 Asian Options
152(2)
9.5 Lookback Options
154(2)
9.6 Cliquet Options
156(1)
9.7 Basket Options
157(1)
9.8 Recommended Literature
158(1)
9.9 Exercises
158(3)
10 Interest Rates and Interest Rate Derivatives
161(38)
10.1 Definitions and Notation
162(3)
10.1.1 Money Market Account
164(1)
10.2 Risk Neutral Valuation and Numeraire Measures
165(6)
10.2.1 Principles of Risk Neutral Valuation
165(1)
10.2.2 Change of Numeraire
166(1)
10.2.3 Equivalent Martingale Measure
167(1)
10.2.4 Traditional Risk Neutral Numeraire
168(1)
10.2.5 Other Choices of Numeraire
169(2)
10.3 Interest Rate Derivatives
171(6)
10.3.1 Forward Rate Agreement
171(1)
10.3.2 Interest Rate Swap
171(2)
10.3.3 The Black Model
173(1)
10.3.4 Bond Option
174(1)
10.3.5 Caps and Floors
175(1)
10.3.6 Swaption
176(1)
10.4 Interest Rate Modeling
177(9)
10.4.1 Short Rate Models
178(3)
10.4.2 Heath Jarrow Morton Framework
181(3)
10.4.3 LIBOR Market Model
184(2)
10.5 Bond Valuation
186(3)
10.5.1 The Bond Valuation Equation
186(1)
10.5.2 Solving the Zero Bond Valuation
187(2)
10.6 Calibrating Interest Rate Models
189(6)
10.6.1 CIR Model: Estimation
189(2)
10.6.2 CIR Model: Implementation Results
191(1)
10.6.3 LMM: Discretization of the Forward Rate
192(1)
10.6.4 LMM: Instantaneous Volatility Function
193(1)
10.6.5 LMM: Implementation Results
194(1)
10.7 Recommended Literature
195(1)
10.8 Exercises
196(3)
Part II Statistical Models of Financial Time Series
11 Introduction: Definitions and Concepts
199(38)
11.1 Some Definitions
200(6)
11.2 Statistical Analysis of German and British Stock Returns
206(3)
11.3 Expectations and Efficient Markets
209(5)
11.4 Econometric Models: A Brief Summary
214(10)
11.4.1 Stock Prices: The CAPM
214(1)
11.4.2 Exchange Rate: Theory of the Interest Rate Parity
215(2)
11.4.3 Term Structure: The Cox—Ingersoll—Ross Model
217(3)
11.4.4 Options: The Black—Scholes Model
220(1)
11.4.5 The Market Price of Risk
221(3)
11.5 The Random Walk Hypothesis
224(2)
11.6 Unit Root Tests
226(7)
11.6.1 Dickey—Fuller Test
226(3)
11.6.2 The KPSS Test
229(2)
11.6.3 Variance Ratio Tests
231(2)
11.7 Recommended Literature
233(1)
11.8 Exercises
234(3)
12 ARIMA Time Series Models
237(26)
12.1 Moving Average Processes
238(1)
12.2 Autoregressive Process
239(4)
12.3 ARMA Models
243(1)
12.4 Partial Autocorrelation
244(3)
12.5 Estimation of Moments
247(4)
12.5.1 Estimation of the Mean Function
248(1)
12.5.2 Estimation of the Covariance Function
249(1)
12.5.3 Estimation of the ACF
250(1)
12.6 Portmanteau Statistics
251(1)
12.7 Estimation of AR(p) Models
252(1)
12.8 Estimation of MA(q) and ARMA(p, q) Models
253(5)
12.9 Recommended Literature
258(1)
12.10 Exercises
258(5)
13 Time Series with Stochastic Volatility
263(54)
13.1 ARCH and GARCH Models
265(20)
13.1.1 ARCH(1): Definition and Properties
267(7)
13.1.2 Estimation of ARCH(1) Models
274(4)
13.1.3 ARCH(q): Definition and Properties
278(1)
13.1.4 Estimation of an ARCH(q) Model
279(1)
13.1.5 Generalized ARCH (GARCH)
280(2)
13.1.6 Estimation of GARCH(p, q) Models
282(3)
13.2 Extensions of the GARCH Model
285(5)
13.2.1 Exponential GARCH
285(2)
13.2.2 Threshold ARCH Models
287(1)
13.2.3 Risk and Returns
288(1)
13.2.4 Estimation Results for DAX and FTSE 100 Returns
289(1)
13.3 Shortfalls of GARCH
290(8)
13.3.1 Recent Challenges to GARCH Models
290(7)
13.3.2 Volatility Forecasting for DAX and FTSE 100 Returns
297(1)
13.4 Multivariate GARCH Models
298(9)
13.4.1 The Vec Specification
299(3)
13.4.2 The BEKK Specification
302(1)
13.4.3 The CCC Model
303(1)
13.4.4 The DCC Model
303(1)
13.4.5 An Empirical Illustration
304(3)
13.5 Continuous-Time GARCH Models
307(5)
13.5.1 COGARCH(1,1): Definition and Properties
308(1)
13.5.2 Relation Between GARCH and COGARCH
309(1)
13.5.3 Estimation of the COGARCH(1,1) Model
310(1)
13.5.4 Extensions of the COGARCH Model
311(1)
13.6 Recommended Literature
312(1)
13.7 Exercises
313(4)
14 Long Memory Time Series
317(22)
14.1 Definition of Long Range Dependence
318(1)
14.2 Fractional Integration and Long-Memory
319(2)
14.3 Long Memory and Self-similar Processes
321(3)
14.4 Detection of the Long Memory
324(3)
14.4.1 Rescaled Range and Rescaled Variance Test
324(2)
14.4.2 Semiparametric Test
326(1)
14.4.3 Tests for Spurious Long Memory
326(1)
14.5 Estimation of the Long Memory Parameter
327(3)
14.5.1 Exact Maximum Likelihood Estimator
327(1)
14.5.2 Regression on the Periodogram
328(1)
14.5.3 Gaussian Semiparametric Estimator
329(1)
14.6 Long Memory Models
330(4)
14.6.1 ARFIMA Model
330(1)
14.6.2 GARCH Long Memory Models
331(2)
14.6.3 FIAPARCH Model
333(1)
14.6.4 HYGARCH Model
334(1)
14.7 An Empirical Illustration
334(3)
14.8 Recommended Literature
337(2)
15 Non-parametric and Flexible Time Series Estimators
339(20)
15.1 Non-parametric Regression
340(2)
15.2 Construction of the Estimator
342(2)
15.3 Empirical Illustration
344(1)
15.4 Flexible Volatility Estimators
345(1)
15.5 Pricing Options with ARCH-Models
346(6)
15.6 Application to the Valuation of DAX Calls
352(3)
15.7 Recommended Literature
355(4)
Part III Selected Financial Applications
16 Value-at-Risk and Backtesting
359(14)
16.1 Forecast and VaR Models
360(3)
16.2 Backtesting with Expected Shortfall
363(1)
16.3 Backtesting in Action
364(5)
16.4 Recommended Literature
369(1)
16.5 Exercises
369(4)
17 Copulae and Value at Risk
373(40)
17.1 Copulae
375(2)
17.2 Copula Classes
377(10)
17.2.1 Simplest Copulae
378(1)
17.2.2 Elliptical Copulae
378(4)
17.2.3 Archimedean Copulae
382(3)
17.2.4 Hierarchical Archimedean Copulae
385(1)
17.2.5 Generalizations
386(1)
17.3 Monte Carlo Simulation
387(4)
17.3.1 Conditional Inverse Method
387(4)
17.3.2 Marshal—Olkin Method
391(1)
17.4 Copula Estimation
391(5)
17.4.1 Full Maximum Likelihood Estimation
393(1)
17.4.2 Inference for Margins
393(1)
17.4.3 Canonical Maximum Likelihood
394(1)
17.4.4 Gaussian Copula Estimation
395(1)
17.4.5 t-Copula Estimation
396(1)
17.5 Asset Allocation
396(1)
17.6 Value-at-Risk of the Portfolio Returns
397(11)
17.6.1 VaR of the P&L
401(4)
17.6.2 Three-Dimensional Portfolio
405(3)
17.7 Recommended Literature
408(3)
17.8 Exercises
411(2)
18 Statistics of Extreme Risks
413(38)
18.1 Risk Measures
413(2)
18.2 Data Description
415(3)
18.3 Estimation Methods
418(22)
18.3.1 The Block Maxima Method
419(10)
18.3.2 The Peaks-over-Threshold (POT) Method
429(11)
18.4 Backtesting
440(1)
18.5 EVT for Time Series
441(5)
18.6 Recommended Literature
446(1)
18.7 Exercises
447(4)
19 Neural Networks
451(26)
19.1 From Perceptron to Non-linear Neuron
452(7)
19.2 Back Propagation
459(2)
19.3 Neural Networks in Non-parametric Regression Analysis
461(6)
19.4 Forecasts of Financial Time Series with Neural Networks
467(4)
19.5 Quantifying Risk with Neural Networks
471(4)
19.6 Recommended Literature
475(2)
20 Volatility Risk of Option Portfolios
477(14)
20.1 Description of the Data
478(3)
20.2 Principal Component Analysis of the VDAX's Dynamics
481(2)
20.3 Stability Analysis of the VDAX's Dynamics
483(2)
20.4 Measure of the Implied Volatility's Risk
485(2)
20.5 Recommended Literature
487(1)
20.6 Exercises
487(4)
21 Non-parametric Estimators for the Probability of Default
491(8)
21.1 Logistic Regression
491(2)
21.2 Semi-parametric Model for Credit Rating
493(4)
21.3 Credit Ratings with Neural Networks
497(2)
22 Credit Risk Management and Credit Derivatives
499(24)
22.1 Basic Concepts
499(2)
22.2 The Bernoulli Model
501(1)
22.3 The Poisson Model
502(2)
22.3.1 Bernoulli vs. Poisson
503(1)
22.4 The Industrial Models
504(4)
22.4.1 CreditMetrics™ and KMV Models
504(1)
22.4.2 CreditRisk+ Model
505(2)
22.4.3 Other Models
507(1)
22.5 One Factor Models
508(2)
22.6 Copulae and Loss Distributions
510(4)
22.7 Collateralized Debt Obligations
514(7)
22.8 Exercises
521(2)
A Technical Appendix 523(12)
A.1 Integration Theory
523(4)
A.2 Portfolio Strategies
527(8)
Symbols and Notations 535(4)
References 539(12)
Index 551
Jürgen Franke is a professor of applied mathematical statistics at the University of Kaiserslautern, member of the graduate school `Mathematics as a Key Technology' and since 2000 he has been an advisor to the Financial Mathematics Group of the Fraunhofer Institute for Industrial Mathematics, Kaiserslautern. His research focuses on nonlinear time series and nonparametric statistics with applications in financial time series and risk analysis.

Wolfgang Karl Härdle is the Ladislaus von Bortkievicz Professor of Statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. the Centre for Applied Statistics and Economics and director of the CRC649 Economic Risk and also oft the IRTG 1792 highdimensional nonstationary time series. He teaches quantitative finance and semiparametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected ISI member and advisor to the Guanghua School of Management, Peking University.





Christian Matthias Hafner is a professor of econometrics at the Université Catholique de Louvain and President of the Louvain School of Statistics, Biostatistics and Actuarial Sciences (LSBA). His work is mainly concerned with applied non- and semiparametric statistics, time series analysis, volatility models, and financial econometrics.