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VaR Methodology for Non-Gaussian Finance

methodology for non-Gaussian finance looks at the importance of VaR in standard international rules for banks and insurance companies; gives the first;

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Handbook of Heavy Tailed Distributions in Finance

issues, i.e., probabilistic, statistical and econometric modelling under non- Gaussian assumptions, as well as the applications of the stable and;

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Structural Vector Autoregressive Analysis

Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This;

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Structural Vector Autoregressive Analysis

Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This;

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Statistical Modeling Using Local Gaussian Approximation

most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation;

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Multivariate Time Series Analysis

illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR;

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Non-Gaussian Random Vibration Fatigue Analysis and Accelerated Test

This book discusses the theory, method and application of non-Gaussian random vibration fatigue analysis and test. The main contents;

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Bayesian Estimation and Tracking

for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and;

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Var Models in Macroeconomics - New Developments and Applications

of macroeconomic and financial aggregates is based on VAR models. VAR models also have been used successfully for economic and business forecasting, for;

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Gaussian and Non-Gaussian Linear Time Series and Random Fields

with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted;

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Mastering Value at Risk

financial institutions and corporate treasuries across the world, Value at Risk (VaR) is rapidly emerging as the dominant methodology for estimating;

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Non-Gaussian Autoregressive-Type Time Series

This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates;

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Stable Non-Gaussian Random Processes

This book presents similarity between Gaussian and non-Gaussian stable multivariate distributions and introduces the one-dimensional stable;

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Implementing Value at Risk

Implementing Value at Risk Philip Best Value at Risk (VAR) is an estimate of the potential loss on a trading or investment portfolio. Its;

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The Var Implementation Handbook

[flap]For investors, risk is about the odds of losing money, and Value at Risk (VaR) is grounded in that common-sense fact. VAR modeling;

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Measuring Financial Risk Modelling

. This book is mainly divided into two parts. The first part conducts analysis about how to opt for an appropriate approach to measuring;

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An Outline of Financial Economics

follows an analytical and geometric methodology, explaining technical terms and mathematical operations in clear, non-technical language, and;

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An Outline of Financial Economics

follows an analytical and geometric methodology, explaining technical terms and mathematical operations in clear, non-technical language, and;

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Gaussian Process Regression Analysis for Functional Data

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis;

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Statistical Portfolio Estimation

processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a;

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Statistical Portfolio Estimation

processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a;

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Mathematical Theories of Machine Learning - Theory and Applications

step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose;

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State-Space Models

theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series;

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State-Space Models

theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series;

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Statistical Inference in Financial and Insurance Mathematics with R

, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments;

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Algorithms and Architectures for Cryptography and Source Coding in Non-Volatile Flash Memories

-constrained embedded systems such as non-volatile flash memories. A new concept for elliptic curve cryptography is presented, which uses an arithmetic;

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Financial Signal Processing and Machine Learning

portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures;

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