Derivative Pricing : A Problem-Based Primer

Ambrose Lo

ISBN 10: 1138033359 / ISBN 13: 9781138033351

Edité par Taylor & Francis Ltd Jun 2018, 2018

A propos de cet article

Neuware.

N° de réf. du vendeur 9781138033351

Détails bibliographiques

• Titre: Derivative Pricing : A Problem-Based Primer
• Éditeur: Taylor & Francis Ltd Jun 2018
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 1138033359
• ISBN à 13 chiffres: 9781138033351
• Reliure: Buch
• État de l'article: Neu

A propos de ce titre

Synopsis

The proliferation of financial derivatives over the past decades, options in particular, has underscored the increasing importance of derivative pricing literacy among students, researchers, and practitioners. Derivative Pricing: A Problem-Based Primer demystifies the essential derivative pricing theory by adopting a mathematically rigorous yet widely accessible pedagogical approach that will appeal to a wide variety of audience. Abandoning the traditional "black-box" approach or theorists' "pedantic" approach, this textbook provides readers with a solid understanding of the fundamental mechanism of derivative pricing methodologies and their underlying theory through a diversity of illustrative examples. The abundance of exercises and problems makes the book well-suited as a text for advanced undergraduates, beginning graduates as well as a reference for professionals and researchers who need a thorough understanding of not only "how," but also "why" derivative pricing works. It is especially ideal for students who need to prepare for the derivatives portion of the Society of Actuaries Investment and Financial Markets Exam.

Features

• Lucid explanations of the theory and assumptions behind various derivative pricing models.
• Emphasis on intuitions, mnemonics as well as common fallacies.
• Interspersed with illustrative examples and end-of-chapter problems that aid a deep understanding of concepts in derivative pricing.
• Mathematical derivations, while not eschewed, are made maximally accessible.
• A solutions manual is available for qualified instructors.

The Author

Ambrose Lo is currently Assistant Professor of Actuarial Science at the Department of Statistics and Actuarial Science at the University of Iowa. He received his Ph.D. in Actuarial Science from the University of Hong Kong in 2014, with dependence structures, risk measures, and optimal reinsurance being his research interests. He is a Fellow of the Society of Actuaries (FSA) and a Chartered Enterprise Risk Analyst (CERA). His research papers have been published in top-tier actuarial journals, such as ASTIN Bulletin: The Journal of the International Actuarial Association, Insurance: Mathematics and Economics, and Scandinavian Actuarial Journal.

À propos de l?auteur

Ambrose Lo is currently Assistant Professor of Actuarial Science at the Department of Statistics and Actuarial Science at the University of Iowa. He received his Ph.D. in Actuarial Science from the University of Hong Kong in 2014, with dependence structures, risk measures, and optimal reinsurance being his research interests. He is a Fellow of the Society of Actuaries (FSA) and a Chartered Enterprise Risk Analyst (CERA). His research papers have been published in top-tier actuarial journals, such as ASTIN Bulletin: The Journal of the International Actuarial Association, Insurance: Mathematics and Economics, and Scandinavian Actuarial Journal.