For those starting out as practitioners of mathematical finance, this is an ideal introduction. It provides the reader with a clear understanding of the intuition behind derivatives pricing, how models are implemented, and how they are used and adapted in practice. Strengths and weaknesses of different models, e.g. Black-Scholes, stochastic volatility, jump-diffusion and variance gamma, are examined. Both the theory and the implementation of the industry-standard LIBOR market model are considered in detail. Uniquely, the book includes extensive discussion of the ideas behind the models, and is even-handed in examining various approaches to the subject. Thus each pricing problem is solved using several methods. Worked examples and exercises, with answers, are provided in plenty, and computer projects are given for many problems. The author brings to this book a blend of practical experience and rigorous mathematical background, and supplies here the working knowledge needed to become a good quantitative analyst.

Advances in Quantitative Analysis of Finance and Accounting (New Series) is an annual publication designed to disseminate developments in the quantitative analysis of finance and accounting. The publication is a forum for statistical and quantitative analyses of issues in finance and accounting as well as applications of quantitative methods to problems in financial management, financial accounting, and business management. The objective is to promote interaction between academic research in finance and accounting and applied research in the financial community and the accounting profession.

Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on https://github.com/lingyixu/Quant-Finance-With-Python-Code.

A Comprehensive Guide to Quantitative Financial Risk Management Written by an international team of experts in the field, Quantitative Financial Risk Management: Theory and Practice provides an invaluable guide to the most recent and innovative research on the topics of financial risk management, portfolio management, credit risk modeling, and worldwide financial markets. This comprehensive text reviews the tools and concepts of financial management that draw on the practices of economics, accounting, statistics, econometrics, mathematics, stochastic processes, and computer science and technology. Using the information found in Quantitative Financial Risk Management can help professionals to better manage, monitor, and measure risk, especially in today's uncertain world of globalization, market volatility, and geo-political crisis. Quantitative Financial Risk Management delivers the information, tools, techniques, and most current research in the critical field of risk management. This text offers an essential guide for quantitative analysts, financial professionals, and academic scholars.

Tools for Computational Finance offers a clear explanation of computational issues arising in financial mathematics. The new third edition is thoroughly revised and significantly extended, including an extensive new section on analytic methods, focused mainly on interpolation approach and quadratic approximation. Other new material is devoted to risk-neutrality, early-exercise curves, multidimensional Black-Scholes models, the integral representation of options and the derivation of the Black-Scholes equation. New figures, more exercises, and expanded background material make this guide a real must-to-have for everyone working in the world of financial engineering.

Written by a physicist with extensive experience as a risk/finance quant, this book treats a wide variety of topics. Presenting the theory and practice of quantitative finance and risk, it delves into the 'how to' and 'what it's like' aspects not covered in textbooks or papers. A 'Technical Index' indicates the mathematical level for each chapter.This second edition includes some new, expanded, and wide-ranging considerations for risk management: Climate Change and its long-term systemic risk; Markets in Crisis and the Reggeon Field Theory; 'Smart Monte Carlo' and American Monte Carlo; Trend Risk — time scales and risk, the Macro-Micro model, singular spectrum analysis; credit risk: counterparty risk and issuer risk; stressed correlations — new techniques; and Psychology and option models.Solid risk management topics from the first edition and valid today are included: standard/advanced theory and practice in fixed income, equities, and FX; quantitative finance and risk management — traditional/exotic derivatives, fat tails, advanced stressed VAR, model risk, numerical techniques, deals/portfolios, systems, data, economic capital, and a function toolkit; risk lab — the nuts and bolts of risk management from the desk to the enterprise; case studies of deals; Feynman path integrals, Green functions, and options; and 'Life as a Quant' — communication issues, sociology, stories, and advice.

Advances in Quantitative Analysis of Finance and Accounting (New Series) is an annual publication designed to disseminate developments in the quantitative analysis of finance and accounting. The publication is a forum for statistical and quantitative analyses of issues in finance and accounting as well as applications of quantitative methods to problems in financial management, financial accounting, and business management. The objective is to promote interaction between academic research in finance and accounting and applied research in the financial community and the accounting profession.