金融相依性之風險測量

Measuring Risk on Financial Interdependence

趙士綱(Shih-Kang Chao)

指導教授 ： 王耀輝

共同指導教授 ： 傅承德(Cheng-Der Fuh)

國立臺灣大學/管理學院/財務金融學研究所/碩士(2011年)

https://doi.org/10.6342/NTU.2011.00209

摘要

近年來金融危機的發生有越來越頻繁和嚴重的趨勢。其中有個明顯的現象，一家金融機構的危機似乎會連帶影響到其他的金融機構。許多財務學家開始研究如何評估來自於連帶影響造成的系統性風險。在本篇論文中，我們根據Adrian and Brunnermeier (2010)提出的廣義CoVaR，提出了一個具體的可操作定義。在此定義下，我們以多元更新理論提出解析近似公式。根據此公式，利用常態和雙指數跳躍過程，以數值法計算近似CoVaR，與利用蒙地卡羅模擬法所計算的CoVaR 做比較。此外，我們也利用常態近似CoVaR 與t 分配蒙地卡羅模擬的結果作比較，因為在t 分配下無法計算近似CoVaR。結果顯示不同模型的確會影響計算的準確度，而蒙地卡羅模擬法需要較長的計算時間，近似法在某些狀況下可以提供較準確的值並更有效率。最後我們亦提供未來研究的方向。

關鍵字

相依風險值 ； 風險值 ； 溢出效果 ； 財務相依性 ； 多元更新理論 ； 首度通過時間 ； 隨機漫步

並列摘要

Financial crisis seems to come more regularly in recent years. A prominent phenomenon is the spillover effect shown in the time of crisis. Many researchers begin to find a simple measure to characterize the risk of dependence in financial market. In this study, we propose a special case of CoVaR, which is a measure of dependence risk proposed by Adrian and Brunnermeier (2010). The asymptotic conditional distribution is derived from multivariate renewal theory under normal distribution and DEJP process in discrete time setting. The CoVaR’s are computed numerically and are compared with the benchmarks from Monte Carlo simulation.
We also compare the normal asymptotic CoVaR with the t distribution Monte Carlo simulated CoVaR since it is hard to get the asymptotic CoVaR under t distribution.
We find that model assumption is likely to affect the CoVaR values and that the Monte Carlo simulation is computationally demanding. The asymptotic CoVaR’s are suitably accurate in some most needed situations with higher time-efficiency.
Possibilities for further researches are also suggested in the conclusion.

並列關鍵字

CoVaR ； Value-at-Risk ； Spillover Effect ； Financial Interdependence ； Multivariate Renewal Theory ； First-Passage Time ； Random Walk

參考文獻

Adams, Z., F uss, R., and Gropp, R. (2010), “Modeling spillover effects among financial institutions: A state-dependent sensitivity value-at-risk (sdsvar) approach”, EBS Working Paper.

Adrian, T. and Brunnermeier, M.K. (2010), “Covar”, Working Paper.

Carlsson, H. (1983), “Remainder term estimates of the renewal function”,The Annals of Probability, 11(1), 143–157.

Dreier, I. and Kotz, S. (2002), “A note on the characteristic function of the t-distribution”, Statistics and Probability Letters, 57, 221–224.

Durret, R. (2005), Probability: Theory and Examples, Duxbury Press.