| The portfolio insurance theory, rising in 1980s, is adopted by institutional investors prevalently. And its correlative researches are ongoing. Its basic idea is described as following: lock up the risk of price falling that the portfolio faced with by paying a specific premium and losing the upside capture, and control the risk of portfolio insurance into an acceptable range so as to limit downside loss. Basing on the summary of theoretical basics and various portfolio insurance strategies, the article designs empirical method and process according as a series of hypotheses. Combining ShangZheng Index, the article executes empirical study on contingent portfolio insurance strategies and compares it with non-contingent portfolio insurance strategies and buy-and-hold strategy. Black & Scholes option pricing model and synthetic theory of option are the theoretical basics of portfolio insurance, and deriving from it are there two types of portfolio insurance strategies: one type is derived from the Black-Scholes option pricing formula named option-based portfolio insurance (OBPI), the other type sets some simple parameters, according to investors' return-risk preference and tolerance, to obtain objective of portfolio insurance. In addition, the article develops a new portfolio insurance called VaR-based Portfolio Insurance strategy (VBPIS), and this insurance strategy would enrich portfolio insurance's research. At the aspect of empirical method, the article quits the implement of asset allocation adjustment although the investment period. And it uses Filter Rules as indexes of market trend, executing portfolio insurance strategy at long position and buy-and-hold strategy at short position. Thus, it improves the performance of portfolio insurance. Resulting from the empirical study, the performance of contingent portfolio insurance strategies is better than that of non-performance portfolio insurance strategies significantly. So it indicates strong applicability of contingent portfolio insurance strategies in China. |
