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Title: Microstructure analysis of price discovery, information-based trading and intervention in foreign exchange markets
Author: Huang, Mingjian
ISNI:       0000 0004 5915 7993
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 2016
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Motivated by the disconnection between dealer-level (DL) and market-level (ML) models, and the inadequacy of theoretical macro models in explaining the behaviour of the exchange rate, this thesis first studies the microstructure of the foreign exchange market and links the behaviour of dealers to exchange rate determination. We develop a model incorporating both information effect and inventory effect in an environment that is closer to reality. Secondly, the order flow model explains around 13% of exchange rate movement per transaction for CNY/USD. In addition, we adopt the probability of information-based trading (PIN) model and the autoregressive conditional duration (ACD) model to directly study the information content of trading duration in the Chinese foreign exchange market. The estimated results provide some evidence that both the expected component and the unexpected component of trading duration are relevant to the arrival rate of informed traders. Signed expected and unexpected trading durations of order flow are proven to contain information in addition to order flow. The final effect of trading duration is a composite result between uninformed and informed traders. Therefore, the impact of trading duration differs according to different market situations. Thirdly, using the Logit model and Ordered Logit model in the estimation, we find that the Chinese government prefers a gradual achievement of its target exchange rate, and ‘leans-against-the-wind’ with a 150-day moving average and target appreciation rate. The driving force of intervention is asymmetric and dynamic. In addition, the Chinese government is found to show more tolerance toward USD appreciation (CNY depreciation) than to USD depreciation (CNY appreciation), except during periods when it actively accelerates the appreciation of CNY to counter a high rate of inflation. Our model is proved to perform better than OLS estimation and to improve prediction ability.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available