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Title: Comparing the hedge performance of financial assets for reducing the chinses stock market risk
Other Titles: การเปรียบเทียบผลการป้องกันความเสี่ยงของสินทรัพย์ทางการเงินเพื่อลดความเสี่ยงของในตลาดหุ้นจีน
Authors: Liu, Yanyao
Authors: Roengchai Tansuchat
Woraphon Yamaka
Nachatpong Kaewsompong
Liu, Yanyao
Issue Date: Nov-2022
Publisher: Chiang Mai : Graduate School, Chiang Mai University
Abstract: China's economy has grown quickly since the reform and opening up, and it is currently the second largest economy in the world. The financial markets in China are expanding and luring more and more investors. 1,800 companies were listed on the Shanghai Stock Exchange, according to the Shanghai Stock Exchange Statistics Annual (2021). With 275.5 million investors, the total trading value was 366.7 trillion yuan. However, China's financial market is not perfect at present, and the stock market crash in 2015 caused losses to many investors. How to allow investors to avoid risks and reduce losses when investing in the Chinese stock market is the purpose of our research. Previous studies have shown that assets, namely commodities and cryptocurrencies, can reduce the risk associated with the stock market. A approach for reducing the risks associated with financial assets is hedging. It uses market tactics or financial instruments to reduce the risk of any unfavourable price changes. To put it another way, investors use a trade in another investment to protect one investment. In our analysis, we selected seven assets, including three cryptocurrencies and four commodities (gold, silver, copper, and WTI oil). We compare assets using the hedge ratio, optimal weight, and hedging effective index and use the diagonal BEKK-Garch model to calculate the variance and covariance to determine which asset can significantly lower the Chinese stack market risk. The findings indicate that copper is the best asset among the four commodities and that Bitcoin is the best asset to lower the risk associated with the Chinese stock market.
Appears in Collections:ECON: Theses

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