|其他題名: ||The investment tools vs. inflation for wealth management|
|作者: ||徐千惠;Hsu, Chien-hui|
|關鍵詞: ||通貨膨脹;財富管理;總體經濟因子;向量自我迴歸;非預期變動;Inflation;wealth management;factors of the overall economy;vector autoregression(VAR);innovation|
|上傳時間: ||2010-01-11 00:45:05 (UTC+8)|
近年來學者多以向量自我迴歸（vector autoregression, 以下簡稱VAR）的方法討論投資工具和總體經濟間的關聯性，如Hondroyiannis and Papapetrou（2001）探討1984-1999年原油價格(月資料)與總體指標的動態交互作用，發現原油價格會對希臘的工業生產帶來負面衝擊。Lee, Huh and Harris（2003）使用1959-1996年實質GDP和國際平均油價(季)資料，討論原油價格的衝擊和測量來自美國和日本對澳洲景氣循環的衝擊，發現原油價格對澳洲的影響長期較短期來的大，該研究認為油價對澳洲經濟不只有直接的衝擊，長期還會透過國外產出的衝擊影響。本文發現上述實證研究並未加入「預測誤差之變異分解」來分析變數之間的變動關係，也就是並未探討某一個變數有多少比例是由其他變數之預測誤差變異所貢獻。本文分析VAR體系中，探討何者總體經濟指標的非預期變動(innovation)最能影響投資工具之預測變異，進而找出總體經濟指標對投資工具解釋能力之差異。再者，本文不在於探討變數間長期的動態交互作用，轉而探討景氣擴張階段與景氣衰退階段不同期間，各投資工具與總體經濟因子之間，資訊傳遞情形是否也有所差異。
The core idea behind the wealth management market is to help retail investors create asset allocations that do not decline in value and that maintain wealth, thereby allowing the planning of asset allocations that are worry free during retirement. It is also an investment instrument to counter inflation of commodity prices. This paper studies several diversified investment instruments, including academic and actual financial tools that can be used to counter inflation. The more effective financial instruments to deal with inflation and deflation will be derived after collation and analysis. In other words, this analysis will include those instruments with a return on investment that keep pace with or exceed the expansion of a business cycle and rising commodity prices and that limit the impact from a recession and falling commodity prices. In this way, the investment market can prevent the negative effects from expansion or recession during a business cycle, realizing the investment goals andefficiency of “maintaining value and assets” as part of wealth management, and providing a guide to investors in making their decisions on asset allocation.
In recent years, most scholars have used vector autoregression (hereafter referred to as the VAR) method to discuss the relationship between investment instruments and the overalleconomy. For example, Hondroyiannis and Papapetrou (2001) investigated the dynamic interaction during 1984-1999 between crude oil prices (monthly data) and the entire index, discovering that crude oil prices had a negative impact on the industrial production of Greece. Lee, Huh and Harris (2003) used effective GDPs during 1959-1996 and (seasonal) data on average international oil prices to discuss the impact of crude oil price and survey the impact from the U.S. and Japan on business cycles in Australia, finding that the impact of crude oil prices on Australia was greater during the long term than during the short term. The study finds that oil prices did not only have a direct impact on the economy of Australia, but over the long term exerted an impact resulting from overseas output. This paper finds that the aforementioned empirical study wasn’t factored into “an analysis on variance of forecast error” regarding the changing relationships among the variables. That is, it did not examine to what extent a certain variable is affected by the variance of forecast error from other variables. This paper analyzes in the VAR system those unexpected variations (innovation) of the overall economic index that can best influence the forecast variation of investment instruments, and further seeks differences in interpretive ability of investment instruments regarding the overall economic index. Furthermore, this paper does not attempt to study the dynamic interaction of variables over the long term, but instead examines various periods of business expansion and recession, the factors between various investment instruments and the overall economy and whether discontinuities exist in the supply of data.