Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.628693
Title: Essays in statistical arbitrage
Author: Alsayed, Hamad
ISNI:       0000 0004 5346 5987
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2014
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Abstract:
This three-paper thesis explores the important relationship between arbitrage and price efficiency. Chapter 3 investigates the risk-bearing capacity of arbitrageurs under varying degrees and types of risk. A novel stochastic process is introduced to the literature that is capable of jointly capturing fundamental risk factors which are absent from extant specifications. Using stochastic optimal control theory, the degree to which arbitrageurs' investment behaviour is affected by aversion to these risks is analytically characterized, as well as conditions under which arbitrageurs cut losses, effectively exacerbating pricing disequilibria. Chapter 4 explores the role of arbitrage in enforcing price parity between cross-listed securities. This work employs an overlooked mechanism by which arbitrage can maintain parity, namely pairs-trading, which is cheaper to implement than the mechanism most commonly employed in the literature on cross-listed securities. This work shows that arbitrage is successful at enforcing parity between cross-listed securities, and also documents the main limits to arbitrage in this market setting. Chapter 5 examines the extent to which arbitrage contributes to the flow of information across markets. It is shown that microscopic lead/lag relationships of the order of a few hundred milliseconds exist across three major international index futures. Importantly, these delays last long enough, and induce pricing anomalies large enough, to compensate arbitrageurs for appropriating pricing disequilibria. These results accord with the view that temporary disequilibria incentivise arbitrageurs to correct pricing anomalies.
Supervisor: Mcgroarty, Francis Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.628693  DOI: Not available
Keywords: HA Statistics
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