標題: | 台灣指數期貨與現貨於台灣證券交易所升降單位縮小前後的非線性動態關係研究:門檻誤差修正模型 The Nonlinear Dynamic Relationship between the TAIEX Index Futures and Spot before and after the Reduction of Tick Size on the Taiwan Stock Exchange: Threshold VECM Approach |
作者: | 邱柏鈞 Chiu Po-Chun 陳達新 Chen Dar-Hsin 財務金融研究所 |
關鍵字: | 定價效率;套利;非線性動態關係;股價升降單位;門檻共整合;門檻誤差修正模型;股價升降單位;pricing efficiency;arbitrage;nonlinear dynamic relationship;tick size;threshold cointegration;TVECM;tick size |
公開日期: | 2005 |
摘要: | 本文採用門檻誤差修正模型(TVECM)來探討台灣證券交易所股市升降單位縮小前後,台指期貨與現貨間的非線性動態關係。資料從民國九十三年五月一日至民國九十四年十二月三十日,並將樣本期間以股市升降單位縮小前後區分成兩組樣本期間。實證結果顯示:台指期貨與現貨間存在著顯著的門檻共整合及非線性短期動態關係,意味著門檻誤差修正模型比線性誤差修正模型更能有效配適期貨對現貨的價格動態。而由於股市升降單位縮小可減少價差成本,因此套利門檻值降低,股市升降單位縮小確實有效提升期貨與現貨的長期連動關係。整體而言,台指期貨相對於台指現貨具有較強的領先性。最後,股市升降單位縮小可有效減少定價誤差,改善期貨與現貨的定價效率。 This study employs the threshold vector error correction model (TVECM) to model the price dynamics between futures and spot markets across the pre- and post- reduction of tick size periods. The sample period extends over two-year trading days from May 1, 2004 to December 31, 2005. The sample period is divided into two sub-periods before and after the reduction of tick size on March 1, 2005. First of all, the results confirm the presence of threshold cointegration, and nonlinear dynamic coefficients in both sub-sample periods, i.e., implying the threshold VECM model fits the price dynamics between futures and spot markets superior to the linear VECM model. Next, the threshold value decreases after the reduction of tick size, because the decrease of tick size reduces the spread cost which comprises the main transaction cost and lower the arbitrage threshold for arbitrageurs. Then, the long-run co-movement extent between these two financial markets turns stronger. This result is caused by the lower transaction costs after the reduction of tick size, which reduces the obstacles for the two prices to return to long-run equilibrium. Last, the dynamic coefficients show the futures clearly leads the spot in both sub-sample periods. Last but not least, the reduction of tick size can effectively lower the mispricing error and improve the pricing efficiency. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT009339519 http://hdl.handle.net/11536/79721 |
Appears in Collections: | Thesis |
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