VaR Assessing of Gold Passbook、Futures and Options

• Main Authors: Yi-Yun Ho, 何怡芸
• Other Authors: Chui-Chun Tsai
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/23026683987841782006

碩士 === 靜宜大學 === 會計學系研究所 === 98 === Following the financial innovation and information technology progression, as well as the hedge of financial institutions devoted to innovation commodities. Derivative instruments are the most influential and fastest-growing in recent years. According to the statistics of International Swap and Derivative Association and Bank for International Settlements statistics (SIC), the global derivatives transaction amount has been growing from 4.4 trillion U.S. dollars in 1991 to 592 trillion U.S. dollars in 2008. We can see derivative instrument growing fast. As challenge for each participant, more than expected risk would be involved in derivative instruments. Malay (2008) considered the first step of controlling risk is accurately assessing the risk. There are many scholars, like Engle, Bollerslev and Nelson, propose the risk evaluating model, called ARCH family models. We also use those models to assess TAIFEX Gold Futures, TAIFEX NT Dollar Gold Futures, Gold Options and Gold Passbook. Those models help us know about the relationship between present risk and their earlier risk, unexpected earlier residual, strength change on unexpected residual, large or small deviation on unexpected residual, and good or bad news on unexpected residual. In This investigation, we apply ARCH family models on the return of gold goods and research the time interval from 2006 to 2009. The Subprime Storm let us know about the risk management of asset securitization is not good enough, so we add VaR model to calculate the max loss on our investment. Then, we must confirm the suitable of these models, use technology of back test to test following 2 month data. Four major findings of this research of our 5 samples are following：(1)The influence of present risk includes earlier risk, unexpected earlier residual, strength change on unexpected residual, large deviation on unexpected residual, and bad news on unexpected residual. (2) EGARCH is more fitted than others. (3) The maximum loss interval of VaR is from 45 to 2844. (4) Many samples in our research pass back test. From above descriptions characteristics, “NT Dollar Gold Futures” is closely meet model identity and pass back test. So, in our empirical evaluation of VaR, “NT Dollar Gold Futures” could easy catch these characteristics.