Stochastic Finance: An Introduction in Discrete Time

This fifth, newly revised edition of the classical introduction to the mathematics of finance, is based on stochastic models in discrete time. Updates include, new chapter on dynamic portfolio strategies, enhanced graphics with real-world data, improved proofs and updated references and citations.

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Stochastic Finance: An Introduction in Discrete Time

This fifth, newly revised edition of the classical introduction to the mathematics of finance, is based on stochastic models in discrete time. Updates include, new chapter on dynamic portfolio strategies, enhanced graphics with real-world data, improved proofs and updated references and citations.

92.99 In Stock
Stochastic Finance: An Introduction in Discrete Time

Stochastic Finance: An Introduction in Discrete Time

Stochastic Finance: An Introduction in Discrete Time

Stochastic Finance: An Introduction in Discrete Time

Paperback(This a revised and expnded fifth edition)

$92.99 
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Overview

This fifth, newly revised edition of the classical introduction to the mathematics of finance, is based on stochastic models in discrete time. Updates include, new chapter on dynamic portfolio strategies, enhanced graphics with real-world data, improved proofs and updated references and citations.


Product Details

ISBN-13: 9783111044811
Publisher: De Gruyter
Publication date: 07/21/2025
Series: De Gruyter Textbook
Edition description: This a revised and expnded fifth edition
Pages: 664
Product dimensions: 6.69(w) x 9.45(h) x (d)

About the Author

Hans Föllmer is Professor emeritus at Humboldt University of Berlin. He was also Professor at ETH Zurich and the University of Bonn, Distinguished Visiting Professor at the National University of Singapore, and Andrew D. White Professor-at-Large at Cornell University.

Alexander Schied is Professor at the University of Waterloo and holds the Munich Re Chair in Stochastic Finance and a University Research Chair.

Table of Contents

Introductionv
IMathematical finance in one period1
1Arbitrage theory3
1.1Assets, portfolios, and arbitrage opportunities3
1.2Absence of arbitrage and martingale measures6
1.3Derivative securities13
1.4Complete market models22
1.5Geometric characterization of arbitrage-free models26
1.6Contingent initial data30
2Preferences43
2.1Preference relations and their numerical representation44
2.2Von Neumann-Morgenstern representation50
2.3Expected utility60
2.4Uniform preferences74
2.5Robust preferences on asset profiles89
2.6Probability measures with given marginals103
3Optimality and equilibrium112
3.1Portfolio optimization and the absence of arbitrage112
3.2Exponential utility and relative entropy121
3.3Optimal contingent claims130
3.4Microeconomic equilibrium141
4Monetary measures of risk157
4.1Risk measures and their acceptance sets158
4.2Robust representation of convex risk measures163
4.3Convex risk measures on L[infinity]175
4.4Value at Risk179
4.5Measures of risk in a financial market191
4.6Shortfall risk198
IIDynamic hedging207
5Dynamic arbitrage theory209
5.1The multi-period market model209
5.2Arbitrage opportunities and martingale measures213
5.3European contingent claims219
5.4Complete markets231
5.5The binomial model234
5.6Convergence to the Black-Scholes price245
6American contingent claims257
6.1Hedging strategies for the seller257
6.2Stopping strategies for the buyer262
6.3Arbitrage-free prices272
6.4Lower Snell envelopes277
7Superhedging284
7.1P-supermartingales and upper Snell envelopes284
7.2Uniform Doob decomposition286
7.3Superhedging of American and European claims289
7.4Superhedging with derivatives298
8Efficient hedging309
8.1Quantile hedging309
8.2Hedging with minimal shortfall risk315
9Hedging under constraints326
9.1Absence of arbitrage opportunities326
9.2Uniform Doob decomposition333
9.3Upper Snell envelopes338
9.4Superhedging and risk measures345
10Minimizing the hedging error348
10.1Local quadratic risk348
10.2Minimal martingale measures358
10.3Variance-optimal hedging368
Appendix375
A.1Convexity375
A.2Absolutely continuous probability measures379
A.3The Neyman-Pearson lemma382
A.4The essential supremum of a family of random variables385
A.5Spaces of measures386
A.6Some functional analysis394
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