"Stochastic Finance provides an introduction to mathematical finance that is unparalleled in its accessibility. Through classroom testing the authors have identified common pain points for students and their approach takes great care to help the reader overcome these difficulties and foster understanding where comparable texts often do not"--
"Stochastic Finance provides an introduction to mathematical finance that is unparalleled in its accessibility. Through classroom testing the authors have identified common pain points for students and their approach takes great care to help the reader overcome these difficulties and foster understanding where comparable texts often do not"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Amanda Turner is Professor of Statistics at the University of Leeds. She received her Ph.D. from the University of Cambridge in Scaling Limits of Stochastic Processes in 2007. Before moving to Leeds, she taught probability and stochastic processes for finance at Lancaster University and the University of Geneva for over fifteen years. She is a founding member of the Royal Statistical Society's Applied Probability Section and is heavily involved in the London Mathematical Society, including as a member of council since 2021. When not doing mathematics, she enjoys mountaineering and skiing.
Inhaltsangabe
Preface Acknowledgements Part I. Discrete-Time Models for Finance: 1. Introduction to finance 2. Discrete probability 3. Binomial or CRR model 4. Finite market model 5. Discrete Black-Scholes model Part II. Continuous-Time Models for Finance: 6. Continuous probability 7. Brownian motion 8. Stochastic integration 9. The Black-Scholes model A Supplementary material Bibliography Symbol index Index.
Preface Acknowledgements Part I. Discrete-Time Models for Finance: 1. Introduction to finance 2. Discrete probability 3. Binomial or CRR model 4. Finite market model 5. Discrete Black-Scholes model Part II. Continuous-Time Models for Finance: 6. Continuous probability 7. Brownian motion 8. Stochastic integration 9. The Black-Scholes model A Supplementary material Bibliography Symbol index Index.
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