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E-raamat: Game-Theoretic Foundations for Probability and Finance: Theory and Applications to Prediction, Science, and Finance 2nd Revised edition [Wiley Online]

(Rutgers, The State Universityof New Jersey, USA), (Royal Holloway, University of London, Egham, Surrey, England)
  • Formaat: 480 pages, Photos: 0 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 100 B&W, 0 Color
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 07-Jun-2019
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118548035
  • ISBN-13: 9781118548035
Teised raamatud teemal:
  • Wiley Online
  • Hind: 125,77 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Formaat: 480 pages, Photos: 0 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 100 B&W, 0 Color
  • Sari: Wiley Series in Probability and Statistics
  • Ilmumisaeg: 07-Jun-2019
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118548035
  • ISBN-13: 9781118548035
Teised raamatud teemal:

Game-theoretic probability and finance come of age

Glenn Shafer and Vladimir Vovk’s Probability and Finance, published in 2001, showed that perfect-information games can be used to define mathematical probability. Based on fifteen years of further research, Game-Theoretic Foundations for Probability and Finance presents a mature view of the foundational role game theory can play. Its account of probability theory opens the way to new methods of prediction and testing and makes many statistical methods more transparent and widely usable. Its contributions to finance theory include purely game-theoretic accounts of Ito’s stochastic calculus, the capital asset pricing model, the equity premium, and portfolio theory.

Game-Theoretic Foundations for Probability and Finance is a book of research. It is also a teaching resource. Each chapter is supplemented with carefully designed exercises and notes relating the new theory to its historical context.

Praise from early readers

“Ever since Kolmogorov's Grundbegriffe, the standard mathematical treatment of probability theory has been measure-theoretic. In this ground-breaking work, Shafer and Vovk give a game-theoretic foundation instead. While being just as rigorous, the game-theoretic approach allows for vast and useful generalizations of classical measure-theoretic results, while also giving rise to new, radical ideas for prediction, statistics and mathematical finance without stochastic assumptions. The authors set out their theory in great detail, resulting in what is definitely one of the most important books on the foundations of probability to have appeared in the last few decades.” – Peter Grünwald, CWI and University of Leiden

“Shafer and Vovk have thoroughly re-written their 2001 book on the game-theoretic foundations for probability and for finance. They have included an account of the tremendous growth that has occurred since, in the game-theoretic and pathwise approaches to stochastic analysis and in their applications to continuous-time finance. This new book will undoubtedly spur a better understanding of the foundations of these very important fields, and we should all be grateful to its authors.” – Ioannis Karatzas, Columbia University

Preface xi
Acknowledgments xv
Part I Examples in Discrete Time 1(108)
1 Borel's Law of Large Numbers
5(26)
1.1 A Protocol for Testing Forecasts
6(2)
1.2 A Game-Theoretic Generalization of Borel's Theorem
8(8)
1.3 Binary Outcomes
16(2)
1.4 Slackenings and Supermartingales
18(1)
1.5 Calibration
19(2)
1.6 The Computation of Strategies
21(1)
1.7 Exercises
21(3)
1.8 Context
24(7)
2 Bernoulli's and De Moivre's Theorems
31(24)
2.1 Game-Theoretic Expected Value and Probability
33(4)
2.2 Bernoulli's Theorem for Bounded Forecasting
37(2)
2.3 A Central Limit Theorem
39(6)
2.4 Global Upper Expected Values for Bounded Forecasting
45(1)
2.5 Exercises
46(3)
2.6 Context
49(6)
3 Some Basic Supermartingales
55(14)
3.1 Kolmogorov's Martingale
56(1)
3.2 Doleans's Supermartingale
56(2)
3.3 Hoeffding's Supermartingale
58(5)
3.4 Bernstein's Supermartingale
63(3)
3.5 Exercises
66(1)
3.6 Context
67(2)
4 Kolmogorov's Law of Large Numbers
69(24)
4.1 Stating Kolmogorov's Law
70(3)
4.2 Supermartingale Convergence Theorem
73(7)
4.3 How Skeptic Forces Convergence
80(1)
4.4 How Reality Forces Divergence
81(1)
4.5 Forcing Games
82(4)
4.6 Exercises
86(3)
4.7 Context
89(4)
5 The Law of the Iterated Logarithm
93(16)
5.1 Validity of the Iterated-Logarithm Bound
94(5)
5.2 Sharpness of the Iterated-Logarithm Bound
99(1)
5.3 Additional Recent Game-Theoretic Results
100(4)
5.4 Connections with Large Deviation Inequalities
104(1)
5.5 Exercises
104(2)
5.6 Context
106(3)
Part II Abstract Theory in Discrete Time 109(86)
6 Betting on a Single Outcome
111(24)
6.1 Upper and Lower Expectations
113(2)
6.2 Upper and Lower Probabilities
115(3)
6.3 Upper Expectations with Smaller Domains
118(3)
6.4 Offers
121(4)
6.5 Dropping the Continuity Axiom
125(2)
6.6 Exercises
127(4)
6.7 Context
131(4)
7 Abstract Testing Protocols
135(22)
7.1 Terminology and Notation
136(1)
7.2 Supermartingales
136(6)
7.3 Global Upper Expected Values
142(3)
7.4 Lindeberg's Central Limit Theorem for Martingales
145(1)
7.5 General Abstract Testing Protocols
146(5)
7.6 Making the Results of Part I Abstract
151(2)
7.7 Exercises
153(2)
7.8 Context
155(2)
8 Zero-One Laws
157(18)
8.1 Levy's Zero-One Law
158(2)
8.2 Global Upper Expectation
160(2)
8.3 Global Upper and Lower Probabilities
162(1)
8.4 Global Expected Values and Probabilities
163(2)
8.5 Other Zero-One Laws
165(4)
8.6 Exercises
169(1)
8.7 Context
170(5)
9 Relation to Measure-Theoretic Probability
175(20)
9.1 Wile's Theorem
176(4)
9.2 Measure-Theoretic Representation of Upper Expectations
180(9)
9.3 Embedding Game-Theoretic Martingales in Probability Spaces
189(2)
9.4 Exercises
191(1)
9.5 Context
192(3)
Part III Applications in Discrete Time 195(110)
10 Using Testing Protocols in Science and Technology
197(32)
10.1 Signals in Open Protocols
198(3)
10.2 Cournot's Principle
201(1)
10.3 Daltonism
202(5)
10.4 Least Squares
207(5)
10.5 Parametric Statistics with Signals
212(3)
10.6 Quantum Mechanics
215(2)
10.7 Jeffreys's Law
217(8)
10.8 Exercises
225(1)
10.9 Context
226(3)
11 Calibrating Lookbacks and p-Values
229(24)
11.1 Lookback Calibrators
230(5)
11.2 Lookback Protocols
235(6)
11.3 Lookback Compromises
241(1)
11.4 Lookbacks in Financial Markets
242(3)
11.5 Calibrating p-Values
245(3)
11.6 Exercises
248(2)
11.7 Context
250(3)
12 Defensive Forecasting
253(52)
12.1 Defeating Strategies for Skeptic
255(4)
12.2 Calibrated Forecasts
259(5)
12.3 Proving the Calibration Theorems
264(6)
12.4 Using Calibrated Forecasts for Decision Making
270(4)
12.5 Proving the Decision Theorems
274(12)
12.6 From Theory to Algorithm
286(5)
12.7 Discontinuous Strategies for Skeptic
291(4)
12.8 Exercises
295(4)
12.9 Context
299(6)
Part IV Game-Theoretic Finance 305(114)
13 Emergence of Randomness in Idealized Financial Markets
309(30)
13.1 Capital Processes and Instant Enforcement
310(2)
13.2 Emergence of Brownian Randomness
312(8)
13.3 Emergence of Brownian Expectation
320(5)
13.4 Applications of Dubins-Schwarz
325(6)
13.5 Getting Rich Quick with the Axiom of Choice
331(2)
13.6 Exercises
333(1)
13.7 Context
334(5)
14 A Game-Theoretic Ito Calculus
339(32)
14.1 Martingale Spaces
340(8)
14.2 Conservatism of Continuous Martingales
348(2)
14.3 Ito Integration
350(5)
14.4 Covariation and Quadratic Variation
355(2)
14.5 Ito's Formula
357(1)
14.6 Doleans Exponential and Logarithm
358(2)
14.7 Game-Theoretic Expectation and Probability
360(1)
14.8 Game-Theoretic Dubins-Schwarz Theorem
361(1)
14.9 Coherence
362(1)
14.10 Exercises
363(2)
14.11 Context
365(6)
15 Numeraires in Market Spaces
371(14)
15.1 Market Spaces
372(3)
15.2 Martingale Theory in Market Spaces
375(1)
15.3 Girsanov's Theorem
376(6)
15.4 Exercises
382(1)
15.5 Context
382(3)
16 Equity Premium and CAPM
385(18)
16.1 Three Fundamental Continuous I-Martingales
387(2)
16.2 Equity Premium
389(2)
16.3 Capital Asset Pricing Model
391(4)
16.4 Theoretical Performance Deficit
395(1)
16.5 Sharpe Ratio
396(1)
16.6 Exercises
397(1)
16.7 Context
398(5)
17 Game-Theoretic Portfolio Theory
403(16)
17.1 Stroock-Varadhan Martingales
405(2)
17.2 Boosting Stroock-Varadhan Martingales
407(6)
17.3 Outperforming the Market with Dubins-Schwarz
413(1)
17.4 Jeffreys's Law in Finance
414(1)
17.5 Exercises
415(1)
17.6 Context
416(3)
Terminology and Notation 419(6)
List of Symbols 425(4)
References 429(26)
Index 455
Glenn Shafer is University Professor at Rutgers University.

Vladimir Vovk is Professor in the Department of Computer Science at Royal Holloway, University of London.

Shafer and Vovk are the authors of Probability and Finance: It's Only a Game, published by Wiley and co-authors of Algorithmic Learning in a Random World. Shafer's other previous books include A Mathematical Theory of Evidence and The Art of Causal Conjecture.