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Statistical Inference in Multifractal Random Walk Models for Financial Time Series

Random Walk models can capture the statistical relation between returns and return periods, thus facilitating a more accurate representation;

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Optimal Statistical Inference in Financial Engineering

Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering;

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Large Covariance and Autocovariance Matrices

matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models;

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Large Covariance and Autocovariance Matrices

matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models;

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Statistical Mechanics & Random Walks

In this book, the authors gather and present topical research in the study of statistical mechanics and random walk principles and;

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The Econometrics of Financial Markets

routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and;

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Probability and Statistical Inference

series, and random processes. *Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation;

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Probability and Statistical Inference

series, and random processes. *Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation;

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Statistical Inference for Financial Engineering

This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for;

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Linear Models and TimeSeries Analysis

, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series;

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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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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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Regression Models For Time Series Analysis

. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a;

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GARCH Models

financial series are provided. * Supporting website featuring R codes, Fortran programs and data sets. * Presents a large collection of problems and;

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Models for Probability and Statistical Inference

for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions;

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Modeling Volatility in Financial Time Series

assumptions. This book assesses how both models perform in replicating financial time series. The model parameters are estimated on historical returns;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Statistical Methods for Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will;

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Statistical Inference in Financial and Insurance Mathematics with R

Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample;

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Frontiers In Statistics

methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re;

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Frontiers In Statistics

methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re;

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Statistical Inference for Piecewise-deterministic Markov Processes

trivial. Responding to new developments in the field as well as to current research interests and needs, Statistical inference for piecewise;

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Statistical Methods Of Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will;

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Random Fields for Spatial Data Modeling

analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the;

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The Black-Scholes-Merton Model as an Idealization of Discrete-Time Economies

of discrete-time economies where the stock price process is driven by a binomial random walk, it is less known that the BSM model idealizes discrete;

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The Black-Scholes-Merton Model as an Idealization of Discrete-Time Economies

of discrete-time economies where the stock price process is driven by a binomial random walk, it is less known that the BSM model idealizes discrete;

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Economic Modeling and Inference

measurement error, imperfect control, and random utility shocks for inference. When all implications of optimization and equilibrium are imposed;

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