| dc.identifier.uri | http://hdl.handle.net/11401/76051 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | To capture the heavy tails and the volatility clustering of asset returns is always an important topic in Â…nancial market. We studies two projects related to the Alpha Stable distribution and Classical Tempered Stable(CTS) distribution respectively which both have desired properties to accommodate heavy-tails and capture skewness in Â…nancial series. (1) In the major part of the Â…rst project, we introduce the algorithm of indirect inference method. By using the skewed-t distribution as an auxiliary model which is easier to handle, we can estimate the parameters of the Alpha Stable distribution since these two models have the same numbers of parameters and each of them plays a similar role. We also estimate of the parameters of the alpha stable distribution with McColloch method, Characteristic Function Based method and MLE method respectively. Finally, we provide an empirical application on S&P 500 returns and make comparisons between these four methods. (2) In the second project, we discuss the Gaussian Â…rm value model and the Classical Tempered Stable Â…rm value model. By pointing out the drawbacks of application of MertonÂ’s model on Â…rm value, we introduce the classical tempered stable distribution and make the market Â…rm value process follows a CTS distribution instead of Gaussian distribution. We estimate the parameters of the CTS, and calculate the Â…rm value and default probability. By comparing these two models, the results suggest that CTS Â…rm value model has a better potential to predict the default probability of a Â…rm since it can better capture the heavy tails of the asset returns. | |
| dcterms.abstract | To capture the heavy tails and the volatility clustering of asset returns is always an important topic in nancial market. We studies two projects related to the Alpha Stable distribution and Classical Tempered Stable(CTS) distribution respectively which both have desired properties to accommodate heavy-tails and capture skewness in nancial series. (1) In the major part of the rst project, we introduce the algorithm of indirect inference method. By using the skewed-t distribution as an auxiliary model which is easier to handle, we can estimate the parameters of the Alpha Stable distribution since these two models have the same numbers of parameters and each of them plays a similar role. We also estimate of the parameters of the alpha stable distribution with McColloch method, Characteristic Function Based method and MLE method respectively. Finally, we provide an empirical application on S&P 500 returns and make comparisons between these four methods. (2) In the second project, we discuss the Gaussian rm value model and the Classical Tempered Stable rm value model. By pointing out the drawbacks of application of Merton s model on rm value, we introduce the classical tempered stable distribution and make the market rm value process follows a CTS distribution instead of Gaussian distribution. We estimate the parameters of the CTS, and calculate the rm value and default probability. By comparing these two models, the results suggest that CTS rm value model has a better potential to predict the default probability of a rm since it can better capture the heavy tails of the asset returns. | |
| dcterms.available | 2017-09-18T23:49:54Z | |
| dcterms.contributor | Kim, Aaron | en_US |
| dcterms.contributor | Rachev, Svetlozar | en_US |
| dcterms.contributor | Rachev, Svetlozar Zari | en_US |
| dcterms.contributor | Glimm, James | en_US |
| dcterms.contributor | Xiao, Keli. | en_US |
| dcterms.creator | Mo, Hua | |
| dcterms.dateAccepted | 2017-09-18T23:49:54Z | |
| dcterms.dateSubmitted | 2017-09-18T23:49:54Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 78 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/76051 | |
| dcterms.issued | 2016-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-18T23:49:54Z (GMT). No. of bitstreams: 1
Mo_grad.sunysb_0771E_12693.pdf: 452805 bytes, checksum: 9e60dabb90d208a458dc364c8e3573b4 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Finance | |
| dcterms.subject | Alpha Stable Distribution, Classical Tempered Stable Distribution, Firm Value Model, Indirect Inference, Merton Model | |
| dcterms.title | Estimation of Stable Distribution and Its Application to Credit Risk | |
| dcterms.type | Dissertation | |
