Financial Modeling A Backward Stochastic Differential Equations Perspective
Series: Springer Finance
Subseries: Springer Finance Textbooks
Crépey, Stéphane
2013, XIX, 459 p. 13 illus. in color. With online files/update.

• About this textbook
  

Provides a unique, BSDE-based perspective on financial modeling and computational finance areas as for example on the pricing and hedging theory, across all asset classes
A unified presentation of all kinds of numerical schemes: semi-explicit, deterministic (PDEs), simulation (Monte Carlo and American Monte Carlo)
Illustrates both the theoretical and practical interest of BSDEs for financial applications​
Backward stochastic differential equations (BSDEs) provide a general mathematical framework for solving pricing and risk management questions of financial derivatives. They are of growing importance for nonlinear pricing problems such as CVA computations that have been developed since the crisis. Although BSDEs are well known to academics, they are less familiar to practitioners in the financial industry. In order to fill this gap, this book revisits financial modeling and computational finance from a BSDE perspective, presenting a unified view of the pricing and hedging theory across all asset classes. It also contains a review of quantitative finance tools, including Fourier techniques, Monte Carlo methods, finite differences and model calibration schemes. With a view to use in graduate courses in computational finance and financial modeling, corrected problem sets and Matlab sheets have been provided.
Stéphane Crépey’sbook starts with a few chapters on classical stochastic processes material, and then... fasten your seatbelt... the author starts traveling backwards in time through backward stochastic differential equations (BSDEs). This does not mean that one has to read the book backwards, like a manga! Rather, the possibility to move backwards in time, even if from a variety of final scenarios following a probability law, opens a multitude of possibilities for all those pricing problems whose solution is not a straightforward expectation. For example, this allows for framing problems like pricing with credit and funding costs in a rigorous mathematical setup. This is, as far as I know, the first book written for several levels of audiences, with applications to financial modeling and using BSDEs as one of the main tools, and as the song says: "it's never as good as the first time".

Damiano Brigo, Chair of Mathematical Finance, Imperial College London
While the classical theory of arbitrage free pricing has matured, and is now well understood and used by the finance industry, the theory of BSDEs continues to enjoy a rapid growth and remains a domain restricted to academic researchers and a handful of practitioners. Crépey’s book presents this novel approach to a wider community of researchers involved in mathematical modeling in finance. It is clearly an essential reference for anyone interested in the latest developments in financial mathematics.
Marek Musiela, Deputy Director of the Oxford-Man Institute of Quantitative Finance

Content Level » Graduate
Keywords » 91G20,91G60, 91G80 - backward stochastic differential equations - computational finance - financial modeling - partial differential equations - pricing and hedging
Related subjects »Computational Science & Engineering - Dynamical Systems & Differential Equations - Quantitative Finance
Table of contents
Part I: An Introductory Course in Stochastic Processes.- 1.Some classes of Discrete-Time Stochastic Processes.-2.Some Classes of Continuous-Time Stochastic Processes.- 3.Elements of Stochastic Analysis.- Part II: Pricing Equations.- 4.Martingale Modeling.- 5.Benchmark Models.- Part III: Numerical Solutions.- 6.Monte Carlo Methods.- 7.Tree Methods.- 8.Finite Differences.- 9.Callibration Methods.- Part IV: Applications.- 10.Simulation/ Regression Pricing Schemes in Diffusive Setups.- 11.Simulation/ Regression Pricing Schemes in Pure Jump Setups.- Part V: Jump-Diffusion Setup with Regime Switching (**).- 12.Backward Stochastic Differential Equations.- 13.Analytic Approach.- 14.Extensions.- Part VI: Appendix.- A.Technical Proofs (**).- B.Exercises.- C.Corrected Problem Sets.

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