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The Estimation of Vector Multiplicat...

Ding, Hao.

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The Estimation of Vector Multiplicative Error Model on Contaminated Data and Its Applications in Forecasting Volatilities.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The Estimation of Vector Multiplicative Error Model on Contaminated Data and Its Applications in Forecasting Volatilities./
Author:	Ding, Hao.
Description:	230 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
Subject:	Engineering, System Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3576378
ISBN:	9781303561665

The Estimation of Vector Multiplicative Error Model on Contaminated Data and Its Applications in Forecasting Volatilities.
Ding, Hao.

The Estimation of Vector Multiplicative Error Model on Contaminated Data and Its Applications in Forecasting Volatilities. - 230 p.

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2013.

This thesis studies the estimations of vector Multiplicative Error Model (MEM) under different kinds of model mismatches and its application in forecasting. In the first part of the thesis, two estimation methods, Maximum Likelihood (ML) method and Generalized Method of Moments (GMM), which have previously been used on vector MEM, are compared through different situations of data contaminations. From the comparison results it is found that both ML and GMM estimators are suspected to outliers in data. Therefore in the second part of the thesis a novel estimator is proposed: Weighted Empirical Likelihood (WEL) estimator. It is shown to be more robust than ML and GMM estimators in simulations, and also in forecasting realized volatility and bipower volatility of S&P 500 stock index including the current financial crisis period. The forecast ability of vector MEM is further addressed in the third part of the thesis, where an alternative decomposition of realized volatility is proposed, and vector MEM is used to model and forecast the two components of realized volatility. From the realized volatility forecasts of S&P 500, NASDAQ and Dow Jones, this decomposition together with vector MEM are illustrated to have superior performances over three competing models which have been applied on forecasting realized volatility before.

ISBN: 9781303561665Subjects--Topical Terms:

1018128
Engineering, System Science.

The Estimation of Vector Multiplicative Error Model on Contaminated Data and Its Applications in Forecasting Volatilities.
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020 $a 9781303561665
035 $a (MiAaPQ)AAI3576378
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100 1 $a Ding, Hao. $3 2095867
245 1 4 $a The Estimation of Vector Multiplicative Error Model on Contaminated Data and Its Applications in Forecasting Volatilities.
300 $a 230 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
500 $a Adviser: Kai-Pui Lam.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2013.
520 $a This thesis studies the estimations of vector Multiplicative Error Model (MEM) under different kinds of model mismatches and its application in forecasting. In the first part of the thesis, two estimation methods, Maximum Likelihood (ML) method and Generalized Method of Moments (GMM), which have previously been used on vector MEM, are compared through different situations of data contaminations. From the comparison results it is found that both ML and GMM estimators are suspected to outliers in data. Therefore in the second part of the thesis a novel estimator is proposed: Weighted Empirical Likelihood (WEL) estimator. It is shown to be more robust than ML and GMM estimators in simulations, and also in forecasting realized volatility and bipower volatility of S&P 500 stock index including the current financial crisis period. The forecast ability of vector MEM is further addressed in the third part of the thesis, where an alternative decomposition of realized volatility is proposed, and vector MEM is used to model and forecast the two components of realized volatility. From the realized volatility forecasts of S&P 500, NASDAQ and Dow Jones, this decomposition together with vector MEM are illustrated to have superior performances over three competing models which have been applied on forecasting realized volatility before.
590 $a School code: 1307.
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Economics, Finance. $3 626650
650 4 $a Business Administration, Management. $3 626628
690 $a 0790
690 $a 0508
690 $a 0454
710 2 $a The Chinese University of Hong Kong (Hong Kong). $3 1017547
773 0 $t Dissertation Abstracts International $g 75-03B(E).
790 $a 1307
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3576378