Var Methodology For Non Gaussian Finance

With the impact of the recent financial crises, more attention must be given to new models in finance rejecting “Black-Scholes-Samuelson” assumptions leading to what is called non-Gaussian finance. With the growing importance of Solvency II, Basel II and III regulatory rules for insurance companies and banks, value at risk (VaR) – one of the most popular risk indicator techniques plays a fundamental role in defining appropriate levels of equities. The aim of this book is to show how new VaR techniques can be built more appropriately for a crisis situation. VaR methodology for non-Gaussian finance looks at the importance of VaR in standard international rules for banks and insurance companies; gives the first non-Gaussian extensions of VaR and applies several basic statistical theories to extend classical results of VaR techniques such as the NP approximation, the Cornish-Fisher approximation, extreme and a Pareto distribution. Several non-Gaussian models using Copula methodology, Lévy processes along with particular attention to models with jumps such as the Merton model are presented; as are the consideration of time homogeneous and non-homogeneous Markov and semi-Markov processes and for each of these models. Contents 1. Use of Value-at-Risk (VaR) Techniques for Solvency II, Basel II and III. 2. Classical Value-at-Risk (VaR) Methods. 3. VaR Extensions from Gaussian Finance to Non-Gaussian Finance. 4. New VaR Methods of Non-Gaussian Finance. 5. Non-Gaussian Finance: Semi-Markov Models.

This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.

The study of heavy-tailed distributions allows researchers to represent phenomena that occasionally exhibit very large deviations from the mean. The dynamics underlying these phenomena is an interesting theoretical subject, but the study of their statistical properties is in itself a very useful endeavor from the point of view of managing assets and controlling risk. In this book, the authors are primarily concerned with the statistical properties of heavy-tailed distributions and with the processes that exhibit jumps. A detailed overview with a Matlab implementation of heavy-tailed models applied in asset management and risk managements is presented. The book is not intended as a theoretical treatise on probability or statistics, but as a tool to understand the main concepts regarding heavy-tailed random variables and processes as applied to real-world applications in finance. Accordingly, the authors review approaches and methodologies whose realization will be useful for developing new methods for forecasting of financial variables where extreme events are not treated as anomalies, but as intrinsic parts of the economic process.

Explore how market valuation must abandon linearity to deliver efficient resource allocation.

An in-depth guide to understanding probability distributions and financial modeling for the purposes of investment management In Financial Models with Lévy Processes and Volatility Clustering, the expert author team provides a framework to model the behavior of stock returns in both a univariate and a multivariate setting, providing you with practical applications to option pricing and portfolio management. They also explain the reasons for working with non-normal distribution in financial modeling and the best methodologies for employing it. The book's framework includes the basics of probability distributions and explains the alpha-stable distribution and the tempered stable distribution. The authors also explore discrete time option pricing models, beginning with the classical normal model with volatility clustering to more recent models that consider both volatility clustering and heavy tails. Reviews the basics of probability distributions Analyzes a continuous time option pricing model (the so-called exponential Lévy model) Defines a discrete time model with volatility clustering and how to price options using Monte Carlo methods Studies two multivariate settings that are suitable to explain joint extreme events Financial Models with Lévy Processes and Volatility Clustering is a thorough guide to classical probability distribution methods and brand new methodologies for financial modeling.

The brand is the companyÂs most important asset. In their financial statements, companies are faced with a lack of accounting recognition for the brands they have created, and value recognition for the brands they have acquired. This book studies the nature, characteristics and determinants of brand information published in companies annual and financial reports. It presents case studies on the methods of evaluating and developing brands, and analyzes annual reports published by listed companies, whose brands appear in international rankings. It reflects on the inadequacy of information and disclosed data to demonstrate the value of brands and the need to ensure that more reliable and relevant financial information is available to investors. Financial Information and Brand Value goes beyond the simple application of conceptual frameworks in order for the reader to master the practices related to brand valuation.

This book combines the fundamentals of finance with relevance and effectiveness. It allows for the practice of this subject and covers all the programs of business schools, universities' finance courses, and engineering schools. This book is a relevant tool to acquire all the knowledge required for examination success and the achievement of proven practical competences.

Combining insights from academic research and practical examples, this book aims to better understand the link between financial markets and innovation management. First, we are back to the very definition of innovation and what it means for financial and non-financial companies. Then, we analyze if efficient innovation management by companies is recognized and valued by financial markets. Finally, we focus on innovation within the financial sector: does it really create value outside the financial sector itself. Are Financial innovations value … or risk creators?