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Financial Modeling Under Non-Gaussian Distributions

2007-04-05 Mathematics
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Release : 2007-04-05
Genre : Mathematics
Kind : eBook
Book Rating : 964/5 ( reviews)

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Book Synopsis Financial Modeling Under Non-Gaussian Distributions by : Eric Jondeau

Download or read book Financial Modeling Under Non-Gaussian Distributions written by Eric Jondeau. This book was released on 2007-04-05. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Financial Models with Levy Processes and Volatility Clustering

2011-02-08 Business & Economics
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Release : 2011-02-08
Genre : Business & Economics
Kind : eBook
Book Rating : 262/5 ( reviews)

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Book Synopsis Financial Models with Levy Processes and Volatility Clustering by : Svetlozar T. Rachev

Download or read book Financial Models with Levy Processes and Volatility Clustering written by Svetlozar T. Rachev. This book was released on 2011-02-08. Available in PDF, EPUB and Kindle. Book excerpt: 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.

VaR Methodology for Non-Gaussian Finance

2013-05-06 Business & Economics
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Release : 2013-05-06
Genre : Business & Economics
Kind : eBook
Book Rating : 983/5 ( reviews)

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Book Synopsis VaR Methodology for Non-Gaussian Finance by : Marine Habart-Corlosquet

Download or read book VaR Methodology for Non-Gaussian Finance written by Marine Habart-Corlosquet. This book was released on 2013-05-06. Available in PDF, EPUB and Kindle. Book excerpt: 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.

A Non-Gaussian Pricing Model for Structured Products

2018
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Release : 2018
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Kind : eBook
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Book Synopsis A Non-Gaussian Pricing Model for Structured Products by : Denis Zuev

Download or read book A Non-Gaussian Pricing Model for Structured Products written by Denis Zuev. This book was released on 2018. Available in PDF, EPUB and Kindle. Book excerpt: The paper aims to reconstruct the empirical premia of the structured products with two underlying assets. We apply various models that differ in probability distributions of the underlying price processes.Pricing techniques, currently worldwide accepted, are based on the Black-Scholes model modifications with Gaussian distributions. Conventionally a correlation between underlying price processes is not considered. In order to achieve the overall objective the paper suggests a pricing model of structured products. The model considers a non-Gaussian realistic market framework for pricing the underlying assets and takes into account their correlation.The theoretical and methodological basis of our research is quantitative finance, evolutionary equations, dynamical systems and field theory.The paper presents an example of pricing a range of structured products.We find that the approach to the theoretical premium valuation of the complex financial instrument is interrelated bijectively with statistical properties of the underlying assets. In particular, the paper presents the effectiveness of our model with regard to the structured derivatives with the correlated assets that obey non-Gaussian distributions. The fair value of the structured product evaluated using our model outperforms estimates obtained by means of other methods as it allows lower fair price of the derivatives.The results of our research may be beneficial to academics, market participants including market analysts, risk-managers and developers of financial products.We have concluded that market participants carry extra costs due to the simple models of the structured products' fair value pricing they apply.The proposed model looks especially promising within the context of the complex derivatives market which growth has been accompanied by low liquidity and high premia, in the absence of a unique framework for pricing the structured products that would be consistent with financial market practice.

Decision Making with Quantitative Financial Market Data

2021-03-01 Business & Economics
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Release : 2021-03-01
Genre : Business & Economics
Kind : eBook
Book Rating : 807/5 ( reviews)

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Book Synopsis Decision Making with Quantitative Financial Market Data by : Alain Ruttiens

Download or read book Decision Making with Quantitative Financial Market Data written by Alain Ruttiens. This book was released on 2021-03-01. Available in PDF, EPUB and Kindle. Book excerpt: Use of quantitative data, especially in financial markets, may provide rapid results due to the ease-of-use and availability of fast computational software, but this book advises caution and helps to understand and avoid potential pitfalls. It deals with often underestimated issues related to the use of financial quantitative data, such as non-stationarity issues, accuracy issues and modeling issues. It provides practical remedies or ways to develop new calculation methodologies to avoid pitfalls in using data, as well as solutions for risk management issues in financial market. The book is intended to help professionals in financial industry to use quantitative data in a safer way.

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