國立高雄大學圖資館 |

Three essays in Finance Economics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Three essays in Finance Economics.
Author:	Jiang, Chuanliang.
Description:	175 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-11(E), Section: A.
Notes:	Adviser: Zhijie Xiao.
Contained By:	Dissertation Abstracts International74-11A(E).
Subject:	Economics, Finance.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3587590
ISBN:	9781303248962

Three essays in Finance Economics.
Jiang, Chuanliang.

Three essays in Finance Economics. - 175 p.

Source: Dissertation Abstracts International, Volume: 74-11(E), Section: A.

Thesis (Ph.D.)--Boston College, 2013.

This dissertation contains three essays. It provides an application of quantile regression in Financial Economics.

ISBN: 9781303248962Subjects--Topical Terms:

212585
Economics, Finance.

Three essays in Finance Economics.
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020 $a 9781303248962
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100 1 $a Jiang, Chuanliang. $3 660365
245 1 0 $a Three essays in Finance Economics.
300 $a 175 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-11(E), Section: A.
500 $a Adviser: Zhijie Xiao.
502 $a Thesis (Ph.D.)--Boston College, 2013.
520 $a This dissertation contains three essays. It provides an application of quantile regression in Financial Economics.
520 $a The first essay investigates whether tail dependence makes a difference in the estimation of systemic risk. This chapter develops a common framework based on a copula model to estimate several popular return-based systemic risk measures: Delta Conditional Value at Risk (DeltaCoVaR) and its modification (DeltaCoVaR≤); and Marginal Expected Shortfall (MES) and its extension, systemic risk measure (SRISK). By eliminating the discrepancy of the marginal distribution, copula models provide the flexibility to concentrate only on the effects of dependence structure on the systemic risk measure. We estimate the systemic risk contributions of four financial industries consisting of a large number of institutions for the sample period from January 2000 to December 2010. First, we found that the linear quantile regression estimation of DeltaCoVaR, proposed by Adrian and Brunnermeier (AB hereafter) (2011), is inadequate to completely capture the non-linear contagion tail effect, which tends to underestimate systemic risk in the presence of lower tail dependence. Second, DeltaCoVaR originally proposed by AB (2011) is in conflict with dependence measures. By comparison, the modified version of DeltaCoVaR ≤ put forward by Girardi et al. (2011) and MES, proposed by Acharya et al. (2010), are more consistent with dependence measures, which conforms with the widely held notion that stronger dependence strength results in higher systemic risk. Third, the modified DeltaCoVaR≤ is observed to have a strong correlation with tail dependence. In contrast, MES is found to have a strong empirical relationship with firms' conditional CAPM beta. SRISK, however, provides further connection with firms' level characteristics by accounting for information on market capitalization and liability. This stylized fact seems to imply that DeltaCoVaR≤ is more in line with the "too interconnected to fail" paradigm, while SRISK is more related to the "too big to fail" paradigm. In contrast, MES offers a compromise between these two paradigms.
520 $a The second essay proposes a quantile regression approach to stock return prediction. I show that incorporating distributional information together with combining model information can produce a superior forecast for the conditional mean as well as the entire distribution of future equity premium, which significantly outperforms the forecast that utilizes either source of information alone. Meanwhile, the order of combination strategies appears to make a difference in the efficiency of pooling both distributional information and model information. It turns out that aggregating distributional information in the first step, followed by combining model information in the second step is more advantageous in return forecast than the alternative combination strategies which reverse the order of combination strategy. Furthermore, the forecast based on LASSO model selection can be significantly improved as well if the distributional information is further incorporated. In other word, aggregating distributional information via combining multiple quantiles estimators contributes to the improvement of forecasts obtained either from model combination or model selection. This paper not only investigates the forecast of conditional mean, but also studies the forecast of the whole distribution of future stock returns. The approaches of quantile combination together with either model combination or model selection turn out to deliver statistically and economically significant out-of-sample forecasts relative to a historical average benchmark.
520 $a The third essay proposes a quantile-based approach to efficiently estimate the conditional beta coefficient without assuming a parametric structure on the distribution of data generating process. Multiple quantiles estimates are combined in a weighting scheme to utilize distributional information across different quantile of the distribution. Monte Carlo simulation demonstrated that combining multiple quantile estimates can substantially improve the estimation efficiency for beta risk estimates in the absence of Gaussian distribution. The robustness of quantile-based beta estimates are pronounced during financial crisis when the distribution of stock returns deviates most from normality. I also explored the performance of different beta estimators in an application of portfolio management analysis and found that beta estimates from the proposed quantile combination approaches are superior to the OLS estimates in constructing Global Minimum Variance Portfolio, which generates lower variance of portfolio but does not come at the expense of persistent lower returns.
590 $a School code: 0016.
650 4 $a Economics, Finance. $3 212585
690 $a 0508
710 2 $a Boston College. $b GSAS - Economics. $3 660366
773 0 $t Dissertation Abstracts International $g 74-11A(E).
790 $a 0016
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3587590