| Summary: | 碩士 === 國立臺灣大學 === 國際企業學研究所 === 92 === Stock return is garbled and composed of its many origins. Factors of stock return have their own degrees of volatility and make different contributions to total return volatility. We first construct expected stock return models under different circumstances and then transfer them to volatility models by referring to VaRC method. This study focuses on the US S&P 500 stock total return index and sums risk origin factors up to estimate, forecast, and analyze real stock return volatility.
The result shows that the same risk factors will have the same weights of total volatility no matter in other different models. The estimate effectiveness is due to the errors caused by summing other risk factors up. We use ARIMA and GARCH (1,1) models to forecast volatility and find that it would be better using short-term high frequency time series data to forecast than using long-term low frequency data. The forecast result shows that return volatility dose not have long-term memory. On the other hand, each factor has different weights of total volatility during various periods. The weights of each factor to return differ from them to volatility. There is no absolute relationship between weights to return and weights to volatility.
Analyzing the componential contributions of each stock risk factor can let us know the risk structure and help us to make fine decisions when hedging. Also, this method can be utilized to analyze other financial instruments and offer some suggestions to practitioners and regulators.
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