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Parameter Estimation in Stochastic Volatility Models

This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test;

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Option Pricing with Long Memory Stochastic Volatility Models

It is now known that long memory stochastic volatility models can capture the well-documented evidence of volatility persistence. However;

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Parameter Estimation in Fractional Diffusion Models

This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years;

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Heston Model And Its Extensions In Matlab And C#

local volatility, Fourier methods applied to the model, numerical integration schemes, parameter estimation, simulation schemes, American;

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Option Pricing Models and Volatility Using ExcelVBA

on parameter estimation, but this is just the tip of the iceberg. Everyone interested in derivatives should have this book in their personal;

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Theory and Statistical Applications of Stochastic Processes

Gaussian processes, Ito integration, stochastic analysis, stochastic differential equations, fractional Brownian motion and parameter estimation;

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Analytically Tractable Stochastic Stock Price Models

stochastic volatility models, that have been developed as an answer to certain imperfections in a celebrated Black-Scholes model of option pricing;

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Stochastic Volatility Modeling

Packed with insights, Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising;

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Multiscale Stochastic Volatility for Equity, Interest Rate, and Credit Derivatives

Building upon the ideas introduced in their previous book, Derivatives in Financial Markets with Stochastic Volatility, the authors study;

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Stochastic Volatility in Financial Markets

Stochastic Volatility in Financial Markets presents advanced topics in financial econometrics and theoretical finance, and is divided into;

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Stochastic Volatility in Financial Markets

Stochastic Volatility in Financial Markets presents advanced topics in financial econometrics and theoretical finance, and is divided;

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GARCH Models

, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic;

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Applied Parameter Estimation for Chemical Engineers

important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method and its modifications for systems and processes;

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Applied Parameter Estimation for Chemical Engineers

important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method and its modifications for systems and processes;

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Statistical Methods for Financial Engineering

measures, the foundations of spot interest rate modeling, Levy processes and their financial applications, the properties and parameter estimation;

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Statistical Methods for Financial Engineering

measures, the foundations of spot interest rate modeling, Levy processes and their financial applications, the properties and parameter estimation;

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Weibull Models

analysis, parameter estimation, model validation, and application; classifies the different models and their properties; intertwines theory and;

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Item Response Theory

chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo;

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Modelling and Parameter Estimation of Dynamic Systems

Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system;

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Parameter Estimation in Reliability and Life Span Models

Offers an applications-oriented treatment of parameter estimation from both complete and censored samples; contains notations, simplified;

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Option Pricing and Estimation of Financial Models with R

Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous;

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Analytical Methods in Statistics

autoregression quantiles, neural networks, weighted empirical minimum distance estimators, implied volatility surface estimation, the Grenander estimator;

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Pathwise Estimation and Inference for Diffusion Market Models

, the market participants' aggregate view on important financial parameters such as implied volatility, discount rate, future interest rate, and;

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Pathwise Estimation and Inference for Diffusion Market Models

, the market participants' aggregate view on important financial parameters such as implied volatility, discount rate, future interest rate, and;

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Market Risk Analysis

and options, to stochastic volatility models and to modelling the implied and local volatility surfaces. All together, the Market Risk;

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Estimation in Conditionally Heteroscedastic Time Series Models

In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model;

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Dealing with Endogeneity in Regression Models with Dynamic Coefficients

issues of endogeneity in Markov-switching models and time-varying parameter models. While others have considered estimation of simultaneous;

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