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Innovations in Derivatives Markets: Fixed Income Modeling, Valuation Adjustments, Risk Management, and Regulation

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Book Series: Springer Proceedings in Mathematics & Statistics ISSN: 2194-1009 ISBN: 9783319334455 9783319334462 Year: Volume: 165 Pages: 449 DOI: 10.1007/978-3-319-33446-2 Language: English
Publisher: Springer Nature
Subject: Mechanical Engineering --- Therapeutics --- Biotechnology --- Business and Management --- Chemical Technology
Added to DOAB on : 2017-03-08 12:08:06
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This book presents 20 peer-reviewed chapters on current aspects of derivatives markets and derivative pricing. The contributions, written by leading researchers in the field as well as experienced authors from the financial industry, present the state of the art in:• Modeling counterparty credit risk: credit valuation adjustment, debit valuation adjustment, funding valuation adjustment, and wrong way risk.• Pricing and hedging in fixed-income markets and multi-curve interest-rate modeling.• Recent developments concerning contingent convertible bonds, the measuring of basis spreads, and the modeling of implied correlations.The recent financial crisis has cast tremendous doubts on the classical view on derivative pricing. Now, counterparty credit risk and liquidity issues are integral aspects of a prudent valuation procedure and the reference interest rates are represented by a multitude of curves according to their different periods and maturities.A panel discussion included in the book (featuring Damiano Brigo, Christian Fries, John Hull, and Daniel Sommer) on the foundations of modeling and pricing in the presence of counterparty credit risk provides intriguing insights on the debate.

An Invitation to Statistics in Wasserstein Space

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Book Series: SpringerBriefs in Probability and Mathematical Statistics ISBN: 9783030384388 Year: Pages: 147 DOI: 10.1007/978-3-030-38438-8 Language: English
Publisher: Springer Nature
Subject: Mathematics
Added to DOAB on : 2020-05-14 09:30:34
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This open access book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics in the space of probability measures when endowed with the geometry of optimal transportation. Further to reviewing state-of-the-art aspects, it also provides an accessible introduction to the fundamentals of this current topic, as well as an overview that will serve as an invitation and catalyst for further research. Statistics in Wasserstein spaces represents an emerging topic in mathematical statistics, situated at the interface between functional data analysis (where the data are functions, thus lying in infinite dimensional Hilbert space) and non-Euclidean statistics (where the data satisfy nonlinear constraints, thus lying on non-Euclidean manifolds). The Wasserstein space provides the natural mathematical formalism to describe data collections that are best modeled as random measures on Euclidean space (e.g. images and point processes). Such random measures carry the infinite dimensional traits of functional data, but are intrinsically nonlinear due to positivity and integrability restrictions. Indeed, their dominating statistical variation arises through random deformations of an underlying template, a theme that is pursued in depth in this monograph. ; Gives a succinct introduction to necessary mathematical background, focusing on the results useful for statistics from an otherwise vast mathematical literature. Presents an up to date overview of the state of the art, including some original results, and discusses open problems. Suitable for self-study or to be used as a graduate level course text. Open access.

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