Pricing And Liquidity Of Complex And Structured Derivatives
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Pricing and Liquidity of Complex and Structured Derivatives
This book introduces the “strike of default” (SOD) benchmark concept. The author determines the SOD through cross-sectional pricing between the credit market and the option market, considering the same underlying. The idea of the SOD is to combine the implied probability of default from both markets to get a time-depending share price, at which the markets believe the underlying will default. By means of credit default swaps (CDS) and option pricing methods, the SOD is determined for any exchange-listed company, where option and CDS market data are available.
Risk Finance and Asset Pricing
Author: Charles S. Tapiero
language: en
Publisher: John Wiley & Sons
Release Date: 2010-09-24
A comprehensive guide to financial engineering that stresses real-world applications Financial engineering expert Charles S. Tapiero has his finger on the pulse of shifts coming to financial engineering and its applications. With an eye toward the future, he has crafted a comprehensive and accessible book for practitioners and students of Financial Engineering that emphasizes an intuitive approach to financial and quantitative foundations in financial and risk engineering. The book covers the theory from a practitioner perspective and applies it to a variety of real-world problems. Examines the cornerstone of the explosive growth in markets worldwide Presents important financial engineering techniques to price, hedge, and manage risks in general Author heads the largest financial engineering program in the world Author Charles Tapiero wrote the seminal work Risk and Financial Management.
Liquidity Dynamics and Risk Modeling
Author: Mazin A. M. Al Janabi
language: en
Publisher: Springer Nature
Release Date: 2024-12-09
This book presents a high-quality contribution to the applications of modern financial algorithms for liquidity risk management and its practical uses and applications to investable portfolios and mutual funds. It brings together the latest thinking on the emerging topic of contemporary liquidity risk estimations and management and includes principles, reviews, examples, and concrete financial markets applications to trading and investment portfolios. Furthermore, it explores research directions of liquidity risk management using modified Liquidity-Adjusted Value-at-Risk (L-VaR) models with the application of machine learning optimization algorithms. The book presents specific self-contained use-cases throughout, showing practical applications of the concepts discussed and providing further directions for researchers and financial markets participants. The book draws practical insights from personal experiences and applies specific examples (with the use of real-world case studies and analysis) about how the modeling techniques and machine learning optimization algorithms could address specific theoretical and practical issues of liquidity risk management and coherent asset allocation in trading and investment portfolios. It will be of interest to researchers, students, and practitioners of risk management, portfolio management, and machine learning.