Jesse Mwangi (9 results)

Condition: New. pp. 120.

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condition: Neu. Non-Linear Time Series Models | Parametric Estimation Using Estimating Functions | Jesse Mwangi | Taschenbuch | 120 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659302015 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In contrast to the traditional time series analysis, which focuses on the modeling based on the first two moments, the nonlinear GARCH models specifically take the effect of the higher moments into modeling consideration. This helps to explain and model volatility especially in financial time series. The GARCH models are able to capture financial characteristics such as volatility clustering, heavy tails and asymmetry. In much of the literature available for the GARCH models, the methods of estimating parameters include the MLE,GMM and LSE which have distributional and optimality limitations. In this book, the Optimal Estimating Function(EF) based techniques are derived for the GARCH models. The EF incorporate the Skewness and the Kurtosis moments which are common in financial data. It is shown using simulations that the Estimating Function (EF) method competes reasonably well with the MLE method especially for the non-normal data and hence provides an alternative estimation technique.Financial analysts, Econometricians and Time series scholars will find this book important in teaching and in risk computation. 120 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In contrast to the traditional time series analysis, which focuses on the modeling based on the first two moments, the nonlinear GARCH models specifically take the effect of the higher moments into modeling consideration. This helps to explain and model volatility especially in financial time series. The GARCH models are able to capture financial characteristics such as volatility clustering, heavy tails and asymmetry. In much of the literature available for the GARCH models, the methods of estimating parameters include the MLE,GMM and LSE which have distributional and optimality limitations. In this book, the Optimal Estimating Function(EF) based techniques are derived for the GARCH models. The EF incorporate the Skewness and the Kurtosis moments which are common in financial data. It is shown using simulations that the Estimating Function (EF) method competes reasonably well with the MLE method especially for the non-normal data and hence provides an alternative estimation technique.Financial analysts, Econometricians and Time series scholars will find this book important in teaching and in risk computation.…

Condition: New. Print on Demand pp. 120 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mwangi JesseDr. Jesse Mwangi Lectures at Egerton University, Mathematics Dept., Kenya. His research interests are in Time series analysis and Sample surveys.He has authored articles in peer reviewed journals and has co-authored a boo.…

Condition: New. PRINT ON DEMAND pp. 120.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In contrast to the traditional time series analysis, which focuses on the modeling based on the first two moments, the nonlinear GARCH models specifically take the effect of the higher moments into modeling consideration. This helps to explain and model volatility especially in financial time series. The GARCH models are able to capture financial characteristics such as volatility clustering, heavy tails and asymmetry. In much of the literature available for the GARCH models, the methods of estimating parameters include the MLE,GMM and LSE which have distributional and optimality limitations. In this book, the Optimal Estimating Function(EF) based techniques are derived for the GARCH models. The EF incorporate the Skewness and the Kurtosis moments which are common in financial data. It is shown using simulations that the Estimating Function (EF) method competes reasonably well with the MLE method especially for the non-normal data and hence provides an alternative estimation technique.Financial analysts, Econometricians and Time series scholars will find this book important in teaching and in risk computation.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 120 pp. Englisch.…