日經股價指數期貨與現貨市場之評價、關聯及避險

Pricing, Interaction and Hedging Performance between Nikkei 225 Stock Index Futures and Stock Markets

10.6656/MR.1999.18.2.CHI.1

余尚武(Shang-Wu Yu)；王俞瓔(Yu-Ing Wang)

持有成本 ； 雙變數GARCH模型 ； 領先落後關係 ； 避險效率 ； 避險比率 ； cost of carry ； bivariate GARCH model ； lead-lag relationship ； hedging performance ； hedge ratio

管理評論

18卷2期（1999 / 05 / 01）

1 - 33

繁體中文

本研究以日本的日經(Nikkei)225股價指數期貨與現貨為研究標的，利用持有成本模型推導期貨的理論價格，並檢測評價的正確性。其次，利用雙變數AR(l)-GARCH(1,l)模型來探討期貨與現貨市場之報酬及報酬率波動的領先落後關係。最後，考慮兩市場價格間的長期共整合關係，以雙變數誤差修正GARCH模型及持有成本理論模型來計算期貨避險比率，並估計其避險效率。實證結果顯示，持有成本模型之期貨理論價格稍偏低於實際價格，但其評價誤差並不顯著。在兩市場領先落後關係的研究方面，現貨日報酬領先期貨日報酬。但是五分鐘資料則顯示不論是報酬率或報酬率波動，現貨與期貨兩市場均會互相影響，資訊在兩市場間其實是雙向流動的，也就是說不論期貨市場或是現貨市場均扮演重要的價格發現角色。OLS-CI法的避險效率最佳，而由於樣本期間內兩市場的波動不大，故強調波動群聚現象的雙變數誤差修正GARCH模型之避險效率最差。

Using both the data of Japanese Nikkei 225 index of the Tokyo Stock Exchange (TSE) and the prices of the Nikkei 225 index futures contracts traded on the Osaka Stock Exchange (OSE), this study examines the power of cost-of-carry futures pricing model to predict market prices. A bivariate AR (l)-GARCH (1,1) model is used to examine the lead-lag relationship between return and its volatility in the stock index futures and spot markets. Also, this study investigates the long-run cointegrating relationship between futures and spot markets. Using a bivariate error correction GARCH and cost-of-carry model, the hedge ratios are estimated and the hedging performance is further tested. We find that the theoretical futures prices are lower than actual prices, but the pricing errors are not significant. The futures daily returns lag cash. By contrast, the five-minutes testing results indicate a strong intermarket dependence in returns and the volatility of returns between spot and futures markets. The evidence is thus consistent with the hypothesis that new market information disseminates in both the futures and stock markets and that both markets serve important roles in price discovery. The OLS-CI hedging model provides the best hedging performance. Because the volatility between spot and futures markets during the sample periods are low, the bivariate error correction GARCH model emphasizing volatility clustering has the worst hedging performance.

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