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Financial Econometrics Using Stata

intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models;

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Applied Econometrics

test and the Vector Error Correction Model test for and correct cointegration.ARMA models determine the optimal AR and MA;

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Time Series Econometrics

of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series;

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Bootstrap-Verfahren bei der Berechnung von Prognosen in (G)ARCH-Modellen

In dieser Studie wurde untersucht, wie das Bootstrapping bei der Prognoseberechnung mit Hilfe von GARCH- und ARMA-GARCH-Modellen eingesetzt;

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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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GARCH Models

Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline;

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Value-at-risk forecasting with the ARMA-GARCH family of models

variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their;

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Statistics and Finance

topics in statistics, such as regression, ARMA and GARCH models, the bootstrap, and nonparametric regression using splines, are introduced as;

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Contributions to Financial Econometrics

software package. Starting with a survey of recent theoretical developments for time series models with GARCH errors, the contributions go on to;

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GARCH Models

This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst;

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Multiscale Forecasting Models

of linear and nonlinear auto-regressive models. Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs;

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Multiscale Forecasting Models

of linear and nonlinear auto-regressive models. Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs;

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Introduction to Time Series Modeling

stationary time series models, such as AR and ARMA models, as well as nonstationary time series models, including the locally stationary AR model;

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Non-Gaussian Autoregressive-Type Time Series

most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time;

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Introduction to Time Series Modeling with Applications in R

, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend;

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Quantitative Financial Risk Management

, Factor models, CAPM models, GARCH models, KMV models and credit scoring models.;

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Introduction to Time Series and Forecasting

investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time;

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Dynamic Models for Volatility and Heavy Tails

Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial;

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Dynamic Models for Volatility and Heavy Tails

Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial;

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Diagnostic Checks in Time Series

modeling--concentrates on diagnostic checks for stationary time series and covers a range of different linear and nonlinear models, from various;

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Ad arma

Ad arma;

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Multivariate Modelling of Non Stationary Economic Time Series

This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on;

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Derivatives and Internal Models

analysis, GARCH models, differential equations, finite difference schemes, Martingales and Numeraires.;

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Derivatives and Internal Models

simulation, Monte Carlo, hedge ratios, etc., including time series analysis and statistical concepts such as GARCH Models or Chi-Square-distributions;

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Derivatives and Internal Models

simulation, Monte Carlo, hedge ratios, etc., including time series analysis and statistical concepts such as GARCH Models or Chi-Square-distributions;

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Elements of Financial Risk Management

online exercises so readers can use their own data. Its unified GARCH modeling approach, empirically sophisticated and relevant yet easy to;

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Einde inhoud

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