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Numerical Studies for CVA with DWR a...

Li, Le.

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Numerical Studies for CVA with DWR and Portfolio Optimization with Mixed Normal Distribution.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Numerical Studies for CVA with DWR and Portfolio Optimization with Mixed Normal Distribution./
作者:	Li, Le.
面頁冊數:	101 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Contained By:	Dissertation Abstracts International77-10B(E).
標題:	Operations research. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10112833
ISBN:	9781339759692

Numerical Studies for CVA with DWR and Portfolio Optimization with Mixed Normal Distribution.
Li, Le.

Numerical Studies for CVA with DWR and Portfolio Optimization with Mixed Normal Distribution. - 101 p.

Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2015.

Credit value adjustment (CVA) is an adjustment added to the fair value of an over-the-counter trade due to the counterparty risk. When the exposure to the counterparty changes in the same direction as the counterparty default risk the so-called wrong-way-risk (WWR) must be taken into account. On the other hand, if these two quantities change in the opposite direction, right-way-risk (RWR) takes place. These two sides of effects are also called directional-way risk (DWR). Calculating CVA with DWR has been a computationally challenging task especially because it has to be done frequently. In this thesis, we start with the fact that the ratio of CVA with DWR to CVA under the independent exposure and default assumption depends on the means and standard deviations of exposure and default probability and their linear correlation. The CVA DWR ratio is then decomposed into two factors, a robust correlation and a profile multiplier with further economic insight into the CVA DWR ratio. The distribution free approach in this paper entails an efficient algorithm of curve based CVA DWR calculation. A numerical study illustrates the algorithm and its benefits when CVA with WWR is priced. A detailed discussion about Hull and White model is made. Some analytical results are derived. We further show the CVA DWR multiplier decomposition bridges different existing approaches that are used to calculate CVA with DWR. Thus the decomposition provide insights of DWR and better explain some phenomenon when DWR is in present. Portfolio optimization with mixed normal distribution is presented. It's shown that mixed normal distribution can better describe stock index returns than normal distribution. Under a mixed normal assumption Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) can be easily computed, which greatly reduce the computational efforts of the optimization problem. A numerical example with 5 assets is presented to show the computational efficiency.

ISBN: 9781339759692Subjects--Topical Terms:

547123
Operations research.

Numerical Studies for CVA with DWR and Portfolio Optimization with Mixed Normal Distribution.
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245 1 0 $a Numerical Studies for CVA with DWR and Portfolio Optimization with Mixed Normal Distribution.
300 $a 101 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
500 $a Advisers: Tao Pang; Wei Chen.
502 $a Thesis (Ph.D.)--North Carolina State University, 2015.
520 $a Credit value adjustment (CVA) is an adjustment added to the fair value of an over-the-counter trade due to the counterparty risk. When the exposure to the counterparty changes in the same direction as the counterparty default risk the so-called wrong-way-risk (WWR) must be taken into account. On the other hand, if these two quantities change in the opposite direction, right-way-risk (RWR) takes place. These two sides of effects are also called directional-way risk (DWR). Calculating CVA with DWR has been a computationally challenging task especially because it has to be done frequently. In this thesis, we start with the fact that the ratio of CVA with DWR to CVA under the independent exposure and default assumption depends on the means and standard deviations of exposure and default probability and their linear correlation. The CVA DWR ratio is then decomposed into two factors, a robust correlation and a profile multiplier with further economic insight into the CVA DWR ratio. The distribution free approach in this paper entails an efficient algorithm of curve based CVA DWR calculation. A numerical study illustrates the algorithm and its benefits when CVA with WWR is priced. A detailed discussion about Hull and White model is made. Some analytical results are derived. We further show the CVA DWR multiplier decomposition bridges different existing approaches that are used to calculate CVA with DWR. Thus the decomposition provide insights of DWR and better explain some phenomenon when DWR is in present. Portfolio optimization with mixed normal distribution is presented. It's shown that mixed normal distribution can better describe stock index returns than normal distribution. Under a mixed normal assumption Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) can be easily computed, which greatly reduce the computational efforts of the optimization problem. A numerical example with 5 assets is presented to show the computational efficiency.
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