Theory and Econometrics of Financial Asset Pricing
Author | : Kian Guan Lim |
Publisher | : Walter de Gruyter GmbH & Co KG |
Total Pages | : 402 |
Release | : 2022-08-22 |
ISBN-10 | : 9783110673951 |
ISBN-13 | : 3110673959 |
Rating | : 4/5 (51 Downloads) |
Download or read book Theory and Econometrics of Financial Asset Pricing written by Kian Guan Lim and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-08-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will provide a firm foundation in the understanding of financial economics applied to asset pricing. It carries the real world perspective of how the market works, including behavioral biases, and also wraps that understanding in the context of a rigorous economics framework of investors’ risk preferences, underlying price dynamics, rational choice in the large, and market equilibrium other than inexplicable irrational bubbles. It concentrates on analyses of stock, credit, and option pricing. Existing highly cited finance models in pricing of these assets are covered in detail, and theory is accompanied by rigorous applications of econometrics. Econometrics contain elucidations of both the statistical theory as well as the practice of data analyses. Linear regression methods and some nonlinear methods are also covered. The contribution of this book, and at the same time, its novelty, is in employing materials in probability theory, economics optimization, econometrics, and data analyses together to provide a rigorous and sharp intellect for investment and financial decision-making. Mistakes are often made with far too often sweeping pragmatism without deeply knowing the underpinnings of how the market economics works. This book is written at a level that is both academically rigorous for university courses in investment, derivatives, risk management, as well as not too mathematically deep so that finance and banking graduate professionals can have a real journey into the frontier financial economics thinking and rigorous data analytical findings.