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Three essays in financial market pre...

Liu, Yan.

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Three essays in financial market prediction.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Three essays in financial market prediction./
Author:	Liu, Yan.
Description:	123 p.
Notes:	Advisers: Richard Luger; Elena Pesavento.
Contained By:	Dissertation Abstracts International68-05A.
Subject:	Economics, Finance. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3264123
ISBN:	9780549025054

Three essays in financial market prediction.
Liu, Yan.

Three essays in financial market prediction. - 123 p.

Advisers: Richard Luger; Elena Pesavento.

Thesis (Ph.D.)--Emory University, 2007.

The dissertation comprises three essays, each of which addresses a specific problem in financial market prediction. The first essay proposes to apply a multi-step optimization method, Maximization by Parts (MBP), to estimate the Copula-GARCH models. The Copula-GARCH models allow very flexible joint distributions by splitting the marginal behaviors from the dependence relation. The Inference Functions for Margins (IFM) method is broadly adopted to estimate the Copula-GARCH models. This paper will show that the IFM method is subject to small-sample biases. I propose to apply the MBP method to estimate the models. The efficiency gain of the MBP method is supported by both simulation and empirical studies. The procedures described here are applied to the daily returns of U.S. and Canadian stock markets.

ISBN: 9780549025054Subjects--Topical Terms:

626650
Economics, Finance.

Three essays in financial market prediction.
LDR:03277nam 2200325 a 45 001 949273
005 20110525
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020 $a 9780549025054
035 $a (UMI)AAI3264123
035 $a AAI3264123
040 $a UMI $c UMI
100 1 $a Liu, Yan. $3 1067479
245 1 0 $a Three essays in financial market prediction.
300 $a 123 p.
500 $a Advisers: Richard Luger; Elena Pesavento.
500 $a Source: Dissertation Abstracts International, Volume: 68-05, Section: A, page: 2097.
502 $a Thesis (Ph.D.)--Emory University, 2007.
520 $a The dissertation comprises three essays, each of which addresses a specific problem in financial market prediction. The first essay proposes to apply a multi-step optimization method, Maximization by Parts (MBP), to estimate the Copula-GARCH models. The Copula-GARCH models allow very flexible joint distributions by splitting the marginal behaviors from the dependence relation. The Inference Functions for Margins (IFM) method is broadly adopted to estimate the Copula-GARCH models. This paper will show that the IFM method is subject to small-sample biases. I propose to apply the MBP method to estimate the models. The efficiency gain of the MBP method is supported by both simulation and empirical studies. The procedures described here are applied to the daily returns of U.S. and Canadian stock markets.
520 $a The second essay (co-authored with Qi Zhu) introduces a new estimator for the predictive regressions. The method of least squares is subject to small-sample biases in predictive regressions with highly persistent regressors. This paper presents a bias-reduced estimator that minimizes the weighted sum of squared autocorrelations of the fitted residuals. Consistency and asymptotic normality of this estimator are established under the assumption of serially uncorrelated innovations. The Monte Carlo studies demonstrate that the finite sample performance of our estimator is better than the existing methods. Our method provides weak evidence on the predictability of stock returns during the post-war period.
520 $a The third essay (coauthored with Richard Luger) suggests a two-stage method for the Value-at-Risk (VaR) estimation. The estimation of the VaR uses the square root of the variance and is subject to the non-linear transformation bias, due to a Jensen's inequality effect. This paper proposes a two-stage estimation procedure to reduce the VaR estimation bias in conventional GARCH models. The first-stage model forecasts the standard deviation directly to avoid the non-linear transformation bias. We further construct a second-stage model via quantile regression by including selected instrument variables. We illustrate the use of this two-stage model in international stock indices, foreign exchange rates, and individual stocks. The empirical results support the effectiveness of this two stage model.
590 $a School code: 0665.
650 4 $a Economics, Finance. $3 626650
650 4 $a Economics, General. $3 1017424
650 4 $a Economics, Theory. $3 1017575
690 $a 0501
690 $a 0508
690 $a 0511
710 2 $a Emory University. $3 1017429
773 0 $t Dissertation Abstracts International $g 68-05A.
790 $a 0665
790 1 0 $a Luger, Richard, $e advisor
790 1 0 $a Pesavento, Elena, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3264123