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Essays on Financial Econometrics.

Chen, Rui.

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Essays on Financial Econometrics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays on Financial Econometrics./
Author:	Chen, Rui.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	226 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-03, Section: A.
Contained By:	Dissertations Abstracts International82-03A.
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28025069
ISBN:	9798672145990

Essays on Financial Econometrics.
Chen, Rui.

Essays on Financial Econometrics. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 226 p.

Source: Dissertations Abstracts International, Volume: 82-03, Section: A.

Thesis (Ph.D.)--Duke University, 2020.

This item must not be sold to any third party vendors.

This dissertation contains my research results on two topics of financial econometrics. The first topic is jump regression where the observation selection procedure can be viewed as the analogy of dimension reduction for the classical big "P" problem in statistics to the big "N" problem in financial econometrics. The second topic is about estimation and testing of time series models for Value-at-Risk (VaR) and Expected Shortfall (ES), which is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its VaR. The first chapter, which is joint work with Jia Li, Viktor Todorov and George Tauchen, develops an efficient mixed-scale estimator for jump regressions using high-frequency asset returns. A novel bootstrap procedure is proposed to make inference about our estimator, which has a non-standard asymptotic distribution that cannot be made asymptotically pivotal via studentization. The Monte Carlo analysis indicates good finite-sample performance of the general specification test and confidence intervals based on the bootstrap. When the method is applied to a high-frequency panel of Dow stock prices together with the market index defined by the S&P 500 index futures over the period 2007-2014, we observe remarkable temporal stability in the way that stocks react to market jumps.The second chapter is co-authored with Andrew J. Patton and Johanna F. Ziegel. We use recent results from statistical decision theory to overcome the problem of "elicitability" for ES by jointly modelling ES and VaR, and propose new time series models for these risk measures. Estimation and inference methods are provided for the proposed models and confirmed via simulation studies to have good nite-sample properties. We apply these models to daily returns on four international equity indices, and nd the proposed new ES-VaR models outperform forecasts based on GARCH or rolling window models.The third chapter is my single-authored paper which proposes a consistent specification test of dynamic joint models for VaR and ES. To overcome the intractability problem of the asymptotic distribution of the test statistics under the null hypothesis, the subsampling approximation is used to get the asymptotic critical values. A Monte Carlo study shows that the proposed test has better empirical size and power performance in finite samples than other existing tests.

ISBN: 9798672145990Subjects--Index Terms:

Financial econometrics

Essays on Financial Econometrics.
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500 $a Source: Dissertations Abstracts International, Volume: 82-03, Section: A.
500 $a Advisor: Patton, Andrew.
502 $a Thesis (Ph.D.)--Duke University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation contains my research results on two topics of financial econometrics. The first topic is jump regression where the observation selection procedure can be viewed as the analogy of dimension reduction for the classical big "P" problem in statistics to the big "N" problem in financial econometrics. The second topic is about estimation and testing of time series models for Value-at-Risk (VaR) and Expected Shortfall (ES), which is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its VaR. The first chapter, which is joint work with Jia Li, Viktor Todorov and George Tauchen, develops an efficient mixed-scale estimator for jump regressions using high-frequency asset returns. A novel bootstrap procedure is proposed to make inference about our estimator, which has a non-standard asymptotic distribution that cannot be made asymptotically pivotal via studentization. The Monte Carlo analysis indicates good finite-sample performance of the general specification test and confidence intervals based on the bootstrap. When the method is applied to a high-frequency panel of Dow stock prices together with the market index defined by the S&P 500 index futures over the period 2007-2014, we observe remarkable temporal stability in the way that stocks react to market jumps.The second chapter is co-authored with Andrew J. Patton and Johanna F. Ziegel. We use recent results from statistical decision theory to overcome the problem of "elicitability" for ES by jointly modelling ES and VaR, and propose new time series models for these risk measures. Estimation and inference methods are provided for the proposed models and confirmed via simulation studies to have good nite-sample properties. We apply these models to daily returns on four international equity indices, and nd the proposed new ES-VaR models outperform forecasts based on GARCH or rolling window models.The third chapter is my single-authored paper which proposes a consistent specification test of dynamic joint models for VaR and ES. To overcome the intractability problem of the asymptotic distribution of the test statistics under the null hypothesis, the subsampling approximation is used to get the asymptotic critical values. A Monte Carlo study shows that the proposed test has better empirical size and power performance in finite samples than other existing tests.
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