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題名：台灣即期外匯市場價格發現與預測
作者：李英新
作者(外文)：LEE,YING-HSIN
校院名稱：國立臺北大學
系所名稱：企業管理學系
指導教授：陳達新
學位類別：博士
出版日期：2017
主題關鍵詞：共整合；外匯市場；價格發現；Cointegration；Foreign Exchange Market；Price Discovery
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具私有資訊交易員之交易策略所隱含訊息，對於市場均衡有一定程度之影響。本研究目的在於探討台灣外匯市場之日內價格發現過程，並提供交易員一個預測短期匯率走勢的方法。本研究由三篇財金實證研究所組成。首先，檢視台灣二家外匯經紀公司於日內匯率價格發現過程中所扮演之角色。第二篇實證研究則運用了相當獨特的台灣國營石油公司下單資料與台北外匯經紀公司每5分鐘美元兌台幣價格資料，驗證大型企業客戶與銀行間之交易行為對於外匯經紀市場所產生之漣漪效果。第三篇實證研究以即時即期美元/台幣交易資料檢驗交易流量與價格變動之相關性。
本文各篇實證研究之發現分述如下。首先，第一篇實證研究結果顯示，於成交價格部份，規模較大之台北外匯提供較多之價格發現資訊，特別是在開盤與收盤時段。然而，在買進報價方面，元太外匯則提供外匯市場較多價格發現訊息。二家外匯經紀公司之成交價、賣出報價及買賣價差分別具有相互因果，然而就買進報價而言，則僅具單向因果。對流動性交易之交易員而言，於規模較小的元太外匯進行交易之最適時機較少，但具有較高之節約報酬。第二篇實證研究發現來自於大型鉅額交易客戶之下單交易對於台北外匯經紀市場價格變動，在不同交易時段之影響皆達15分鐘以上。價差變動程度與客戶交易量是影響價格衝擊之主要因素，而且隨交易時段而有所差異。另外，研究結果亦顯示存貨與流動性效果亦會影響客戶鉅額交易之衝擊程度。第三篇實證研究則以均方根誤差(RMSE)為基準，對交易流量模型與隨機模型及其他之時間序列模型之預測精確性進行比較。本研究發現運用交易流量模型可增加預測短期匯率之精準度。另一方面，運用前期交易流量進行預測之結果亦優於利用前期報酬率進行預測之結果，且其達到效率市場之時間相對較長。尤其，對於低交易頻率之市場，運用交易流量百分比作為解釋變數有助於提高衡量交易壓力方向之準確度。
整體而言，了解交易流量與價格動態間之關連性，有助於外匯交易員決定其交易策略。
The effect of information on trading strategies for informed dealers may influence the market equilibrium during the day. This research aims to explore the intraday price discovery in Taiwan’s foreign exchange market and provide a method for forecasting the short-term exchange rate.
This research comprises three essays. Essay 1 examines the role of price discovery in Taiwan’s two foreign exchange markets during trading days. Essay 2 employs the unique transaction dataset of Taiwan’s state-owned petroleum company and the 5-minute frequency data of Reuter’s TAIFX1 to examine the ripple effect evoked by a large noise customer. Essay 3 examines the relation between the price movement and order flow in Taipei foreign exchange market.
First, results of this research show that the major market, Taipei Foreign Exchange Co., contributes more information for transaction price discovery, especially during the market opening and closing periods. However, the minor market dominants price discovery for the bid price. The causality is bidirectional between the two markets for transaction price, ask price, and spread, but unidirectional for the bid price. The minor market, Cosmos Foreign Exchange International Co., has small optimal trading timing, but has a greater mean saving for liquidity dealers.
Furthermore, the empirical results of Essay 2 demonstrate that the price impact from the noise and non-financial customer persists for at least 15 minutes and differs across trading hours. The change of inside spread and trade volume are important factors of price impact, depending on different trading timing. This paper confirms that inventory and liquidity effects are important for the price impact around a large customer trade.
Finally, in Essay 3, we compare the forecasting accuracy of the order flow model to those of the random walk and various time series models of exchange rate in terms of the root-mean-squared error (RMSE) criteria. We find that the order flow model tends to improve the forecasting accuracy. The lagged order flow has significant predictable ability over the lagged return, and has a longer speed of converging to the market efficient. In particular, order flow in percentage (OBIP) could accurately measure order pressure in the market with the limitation of price discovery and the low trading density.
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