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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 管中閔(Chung-Ming Kuan) | |
| dc.contributor.author | Hui-Ching Chuang | en |
| dc.contributor.author | 莊惠菁 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:20:26Z | - |
| dc.date.available | 2013-11-03 | |
| dc.date.copyright | 2012-07-18 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-06-22 | |
| dc.identifier.citation | Bibliography
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66058 | - |
| dc.description.abstract | Default/Bankruptcy prediction has a long history in finance.
Intensity (instantaneous default probability) model has gain more and more attentions in recent default/bankruptcy studies. In this work, we discussion two aspects in intensity modeling: model specification test and regime-switching generalized intensity model. Model specification test is an important issue for empirical researchers to judge the goodness of fit of the model to the data. In intensity models, Das et. al. (2007) and Lando and Nielsen (2010) needs to specified a bin size that may affect the test results. I will introduce a new specification test based on the martingale properties of aggregate default intensities. Simulation results show that martingale-based test has better power than time-transformed specification test without considering the estimation effects. The second part of this thesis, I will introduce a default prediction model with regime-switching effects in the intensity function, such that the default intensity is affected by both observable risk factors and unobservable regime indicators. In particular, the level of the intensity function and the risk exposures to observable risk factors in the intensity function are specified as state dependent. We provide an estimation algorithm when the state variable is Markovian and illustrate the proposed model using the default data of US-listed companies during 1990-2009. Our test indicates that the regime switching effect in the intensity function is significant. Regime-switching intensity model outperforms classical intensity model in the in-sample fit and out-of-sample default predictions. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:20:26Z (GMT). No. of bitstreams: 1 ntu-101-D94723007-1.pdf: 677433 bytes, checksum: 62f65caecab99d6686e4244d03af05b2 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | Contents
1 Introduction to Default Prediction Modeling 2 1.1 Introduction. . . 2 1.2 Statistical Methods in Default Modeling. . . 2 1.2.1 Linear Discrimination Analysis. . . 3 1.2.2 Binary Response Models. . . 6 1.2.3 Survival Analysis. . . 7 1.3 Conclusion. . . 10 2 Model Specication Tests 11 2.1 Introduction. . . . 11 2.2 Cox Process. . . 11 2.3 Specication Tests Using Martingale Properties. . . 16 2.4 The Time-Changed Methods. . . 19 2.5 Simulation Results. . . 21 2.5.1 Five Settings in simulation study. . . 23 2.5.2 Bootstrap and Number of Firms. . . 25 2.6 Conclusion. . . 26 3 Regime Switching Intensity Model 38 3.1 Introduction. . . 38 3.2 Related Literature. . . 42 3.3 Methodology. . . 45 3.3.1 Covariate selection in intensity model. . . 45 3.3.2 Regime-switching intensity model. . . 47 3.4 Empirical analysis. . . 50 3.4.1 Data and covariates selection. . . 50 3.4.2 Is regime-switching eect statistically signicant? . 52 3.4.3 MLEs of various models. . . 54 3.4.4 Default number tting. . . 56 3.4.5 Out-of-sample default prediction performance. . . 58 3.5 Conclusion. . . 59 4 Conclusions 76 5 Bibliography 78 | |
| dc.language.iso | en | |
| dc.subject | 縮減式模型 | zh_TW |
| dc.subject | 破產預測 | zh_TW |
| dc.subject | 時間轉換檢定 | zh_TW |
| dc.subject | 平睹過程檢定 | zh_TW |
| dc.subject | 平賭差分過程檢定 | zh_TW |
| dc.subject | 馬可夫轉換模型 | zh_TW |
| dc.subject | 脆弱模型 | zh_TW |
| dc.subject | time-transformed test | en |
| dc.subject | frailty models | en |
| dc.subject | Markov-switching modeling | en |
| dc.subject | martingale difference test | en |
| dc.subject | bankruptcy prediction | en |
| dc.subject | reduced-form model | en |
| dc.subject | martingale test | en |
| dc.title | 違約強度模型之計量分析與應用 | zh_TW |
| dc.title | PREDICTING DEFAULTS WITH INTENSITY MODELS: METHODS AND EMPIRICAL EVIDENCES | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 俞明德(Min-Teh Yu),莊文議(Wen-I Chuang),徐之強(Chih-Chiang Hsu),葉錦徽(Jin-Huei Yeh) | |
| dc.subject.keyword | 破產預測,縮減式模型,時間轉換檢定,平睹過程檢定,平賭差分過程檢定,馬可夫轉換模型,脆弱模型, | zh_TW |
| dc.subject.keyword | bankruptcy prediction,reduced-form model,time-transformed test,martingale test,martingale difference test,Markov-switching modeling,frailty models, | en |
| dc.relation.page | 83 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-06-25 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
| 顯示於系所單位： | 財務金融學系 | |
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