Buch, Englisch, Band 64, 856 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1478 g
Buch, Englisch, Band 64, 856 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1478 g
Reihe: Stochastic Modelling and Applied Probability
ISBN: 978-3-642-12057-2
Verlag: Springer
Finance is chosen as the area of application because much of the recent research on stochastic numerical methods has been driven by challenges in quantitative finance.
Moreover, the volume introduces readers to the modern benchmark approach that provides a general framework for modeling in finance and insurance beyond the standard risk-neutral approach. It requires undergraduate background in mathematical or quantitative methods, is accessible to a broad readership, including those who are only seeking numerical recipes, and includes exercises that help the reader develop a deeper understanding of the underlying mathematics.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Mathematische Analysis Differentialrechnungen und -gleichungen
Weitere Infos & Material
Stochastic Differential Equations with Jumps.- Exact Simulation of Solutions of SDEs.- Benchmark Approach to Finance and Insurance.- Stochastic Expansions.- to Scenario Simulation.- Regular Strong Taylor Approximations with Jumps.- Regular Strong Itô Approximations.- Jump-Adapted Strong Approximations.- Estimating Discretely Observed Diffusions.- Filtering.- Monte Carlo Simulation of SDEs.- Regular Weak Taylor Approximations.- Jump-Adapted Weak Approximations.- Numerical Stability.- Martingale Representations and Hedge Ratios.- Variance Reduction Techniques.- Trees and Markov Chain Approximations.- Solutions for Exercises.